diff --git a/.codex b/.codex new file mode 100644 index 00000000000..e69de29bb2d diff --git a/.gitignore b/.gitignore index 7227515c0a3..258e3823f60 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,13 @@ # Unignore all dirs !*/ + +# 忽略所有 csv log 檔案 + +*.txt +# 忽略資料集目錄 (Euroc 相關) +data/ + CMakeLists.txt.user CMakeLists_modified.txt @@ -91,3 +98,741 @@ my_settings.txt borrar/* */ExecMean.txt +evaluation/fix_mono_tum_imu_corr_2.1txt +evaluation/fix_mono_tum_imu_corr_2.2txt +evaluation/fix_mono_tum_imu_corr_2.3txt +evaluation/fix_mono_tum_imu_corr_2.4txt +localmapping_log.csv +localmapping_log.csv +tracking_log.csv +evaluation/benchmark_results_20260407_025931.csv +evaluation/benchmark_results_20260407_122827.csv +evaluation/mimu_euroc_v101_localmapping_log.csv +evaluation/mimu_euroc_v101_tracking_log.csv +evaluation/mimu_euroc_v102_localmapping_log.csv +evaluation/mimu_euroc_v102_tracking_log.csv +evaluation/mimu_euroc_v103_localmapping_log.csv 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+evaluation/benchmark_results_20260519_152333.csv +evaluation/benchmark_results_20260519_152518.csv +evaluation/benchmark_results_20260519_153604.csv +evaluation/benchmark_results_20260519_153749.csv +evaluation/benchmark_results_20260519_155314.csv +evaluation/benchmark_results_20260519_155643.csv +evaluation/benchmark_results_20260519_161010.csv +evaluation/benchmark_results_20260519_161413.csv +evaluation/diagnosis_stereo_euroc_v102.pdf +evaluation/stereo_euroc_v102_localmapping_log.csv +evaluation/stereo_euroc_v102_loop_closing_log.csv +evaluation/stereo_euroc_v102_tracking_log.csv +evaluation/stereo/v102/diagnosis_stereo_euroc_v1020.059.pdf +evaluation/stereo/v102/diagnosis_stereo_euroc_v1022222.pdf +evaluation/stereo/v102/stereo_euroc_v102_localmapping_log0.059.csv +evaluation/stereo/v102/stereo_euroc_v102_localmapping_log2222.csv +evaluation/stereo/v102/stereo_euroc_v102_loop_closing_log0.059.csv +evaluation/stereo/v102/stereo_euroc_v102_loop_closing_log2222.csv +evaluation/stereo/v102/stereo_euroc_v102_tracking_log0.059.csv +evaluation/stereo/v102/stereo_euroc_v102_tracking_log2222.csv +evaluation/stereo/v102/trajectory_stereo_euroc_v1020.059.pdf +evaluation/stereo/v102/trajectory_stereo_euroc_v1022222.pdf diff --git a/.vscode/c_cpp_properties.json b/.vscode/c_cpp_properties.json new file mode 100644 index 00000000000..2227b5e3f41 --- /dev/null +++ b/.vscode/c_cpp_properties.json @@ -0,0 +1,20 @@ +{ + "configurations": [ + { + "name": "Linux", + "includePath": [ + "${workspaceFolder}/**", + "${workspaceFolder}/include", + "/usr/include/opencv4", + "/usr/local/include/opencv4", + "/usr/include/eigen3" + ], + "defines": [], + "compilerPath": "/usr/bin/gcc", + "cStandard": "c17", + "cppStandard": "gnu++17", + "intelliSenseMode": "linux-gcc-x64" + } + ], + "version": 4 +} \ No newline at end of file diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 00000000000..96ec3d97c88 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,113 @@ +# CLAUDE.md + +This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. + +## Build + +**First-time build** (builds Thirdparty libs + main library): +```bash +./build.sh +``` + +**Incremental rebuild** (after modifying `src/` or `include/`): +```bash +cd build && make -j4 +``` + +**ROS nodes** (optional): +```bash +./build_ros.sh +``` + +The build produces `lib/libORB_SLAM3.so` and executables under `Examples/`. + +## Running Examples + +All executables take `Vocabulary/ORBvoc.txt` as first argument and a YAML config as second. + +**Stereo-Inertial on EuRoC:** +```bash +./Examples/Stereo-Inertial/stereo_inertial_euroc \ + Vocabulary/ORBvoc.txt Examples/Stereo-Inertial/EuRoC.yaml \ + /path/to/EuRoC/MH_01_easy ./Examples/Stereo-Inertial/EuRoC_TimeStamps/MH01.txt dataset-MH01_stereo_inertial +``` + +**Stereo on EuRoC:** +```bash +./Examples/Stereo/stereo_euroc \ + Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml \ + /path/to/EuRoC/MH_01_easy ./Examples/Stereo/EuRoC_TimeStamps/MH01.txt dataset-MH01_stereo +``` + +Dataset paths on this machine: +- EuRoC: `/media/lab405/Windows1/data/Euroc` +- TUM-VI: `/media/lab405/Windows1/data/TUM` + +## Evaluation / Benchmarking + +The unified benchmark script runs all sensor configurations and computes APE RMSE via `evo_ape`: +```bash +cd evaluation + +# All algorithms and datasets +python3 run_stereo_inertial_benchmark.py + +# Filter by algorithm or dataset +python3 run_stereo_inertial_benchmark.py --algo stereo --dataset euroc +python3 run_stereo_inertial_benchmark.py --algo stereo_inertial +python3 run_stereo_inertial_benchmark.py --algo mono_inertial --dataset tum +``` + +Results are saved to `evaluation/benchmark_results_.csv`. The script: +1. Runs the binary +2. Converts timestamps ns→s via `fix_time.py` +3. Computes APE RMSE with `evo_ape` + +`evaluation/ate_3d.py` is a 3D ATE evaluation script. + +## Enabling Timing Profiling + +Uncomment `#define REGISTER_TIMES` in `include/Config.h` (note: currently the file is a stub — the actual flag was used in the old code path), then rebuild. Stats are printed to terminal and saved to `ExecTimeMean.txt`. + +## Architecture + +ORB-SLAM3 is a **multi-threaded SLAM system** built around three parallel threads: + +### Core Threads +- **Tracking** (`src/Tracking.cc`): Main thread. Receives each frame, extracts ORB features, estimates camera pose via motion model or relocalization, and decides when to insert KeyFrames. +- **LocalMapping** (`src/LocalMapping.cc`): Processes new KeyFrames — creates new MapPoints via triangulation, culls redundant KeyFrames, and runs Local BA (via `Optimizer`). +- **LoopClosing** (`src/LoopClosing.cc`): Detects loop closures using DBoW2 place recognition, computes Sim3 correction, runs Essential Graph optimization, and triggers full Bundle Adjustment. + +### Key Data Structures +- **Atlas** (`src/Atlas.cc`): Container for multiple Maps. Enables multi-map SLAM — when tracking is lost, a new Map is created; loop closure can merge maps. +- **Map** / **MapPoint** / **KeyFrame**: The 3D map representation. MapPoints store 3D position and observations from multiple KeyFrames. +- **Frame**: A single image capture with extracted ORB features. Short-lived; converted to KeyFrame when selected. + +### Optimization (`src/Optimizer.cc`) +All non-linear optimization uses **g2o** (included in `Thirdparty/g2o`). Key functions: +- `PoseOptimization` — single-frame pose-only BA +- `LocalBundleAdjustment` — local map BA triggered by LocalMapping; the `num_iters` parameter has been added to this fork for monitoring +- `OptimizeEssentialGraph` — loop closure correction +- `GlobalBundleAdjustemnt` — full map BA after loop closure + +### IMU Integration +`src/ImuTypes.cc` and `include/ImuTypes.h` define IMU preintegration. `src/G2oTypes.cc` defines the g2o edge/vertex types for visual-inertial optimization. IMU initialization happens in Tracking and is refined in LocalMapping. + +### Camera Models +`include/CameraModels/`: `Pinhole` (standard) and `KannalaBrandt8` (fisheye). Both inherit from `GeometricCamera`. The active model is selected based on the YAML config `Camera.type` field. + +### Settings / Config +`src/Settings.cc` parses YAML config files (OpenCV `FileStorage`). Each YAML in `Examples/*/` corresponds to a specific sensor+dataset combination. IMU noise parameters and camera intrinsics/extrinsics live there. + +### Vocabulary +`Vocabulary/ORBvoc.txt` is the prebuilt DBoW2 vocabulary (decompressed from `ORBvoc.txt.tar.gz` by `build.sh`). Required at runtime — do not delete. + +## Modified Files in This Fork + +This fork adds monitoring/logging capabilities: +- `src/Tracking.cc` — instrumentation additions +- `src/Optimizer.cc` — `num_iters` parameter for `LocalBundleAdjustment`, monitoring output +- `Examples/Stereo-Inertial/TUM-VI_far.yaml` — tuned config for far-range TUM-VI +- `Examples/Stereo/EuRoC.yaml` — tuned stereo config + +CSV logs (`localmapping_log.csv`, `tracking_log.csv`) are written during runs for analysis. `map_points_total` = current map point count; `new_map_points` = points attempted in a given frame. diff --git a/CMakeLists.txt b/CMakeLists.txt index 016e74354d6..ac5d7c423a8 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,4 +1,4 @@ -cmake_minimum_required(VERSION 2.8) +cmake_minimum_required(VERSION 3.10) project(ORB_SLAM3) IF(NOT CMAKE_BUILD_TYPE) @@ -14,18 +14,19 @@ set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -march=native") # Check C++11 or C++0x support include(CheckCXXCompilerFlag) +CHECK_CXX_COMPILER_FLAG("-std=c++14" COMPILER_SUPPORTS_CXX14) CHECK_CXX_COMPILER_FLAG("-std=c++11" COMPILER_SUPPORTS_CXX11) -CHECK_CXX_COMPILER_FLAG("-std=c++0x" COMPILER_SUPPORTS_CXX0X) -if(COMPILER_SUPPORTS_CXX11) - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11") + +if(COMPILER_SUPPORTS_CXX14) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -Wno-maybe-uninitialized -w") + add_definitions(-DCOMPILEDWITHC14) + message(STATUS "Using flag -std=c++14.") +elseif(COMPILER_SUPPORTS_CXX11) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wno-maybe-uninitialized -w") add_definitions(-DCOMPILEDWITHC11) message(STATUS "Using flag -std=c++11.") -elseif(COMPILER_SUPPORTS_CXX0X) - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++0x") - add_definitions(-DCOMPILEDWITHC0X) - message(STATUS "Using flag -std=c++0x.") else() - message(FATAL_ERROR "The compiler ${CMAKE_CXX_COMPILER} has no C++11 support. Please use a different C++ compiler.") + message(FATAL_ERROR "The compiler ${CMAKE_CXX_COMPILER} has no C++11/C++14 support. Please use a different C++ compiler.") endif() LIST(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake_modules) diff --git a/Examples/Monocular-Inertial/TUM-VI.yaml b/Examples/Monocular-Inertial/TUM-VI.yaml index b882f33f719..be1ecd1a3f1 100755 --- a/Examples/Monocular-Inertial/TUM-VI.yaml +++ b/Examples/Monocular-Inertial/TUM-VI.yaml @@ -1,5 +1,5 @@ %YAML:1.0 - +loopClosing: 0 #-------------------------------------------------------------------------------------------- # Camera Parameters. Adjust them! #-------------------------------------------------------------------------------------------- diff --git a/Examples/Monocular-Inertial/TUM-VI_far.yaml b/Examples/Monocular-Inertial/TUM-VI_far.yaml index 8ad355aaf03..bbd865d3f9c 100755 --- a/Examples/Monocular-Inertial/TUM-VI_far.yaml +++ b/Examples/Monocular-Inertial/TUM-VI_far.yaml @@ -46,7 +46,7 @@ IMU.NoiseGyro: 0.00016 # 0.004 (VINS) # 0.00016 (TUM) # 0.00016 # rad/s^0.5 IMU.NoiseAcc: 0.0028 # 0.04 (VINS) # 0.0028 (TUM) # 0.0028 # m/s^1.5 IMU.GyroWalk: 0.000022 # 0.000022 (VINS and TUM) rad/s^1.5 IMU.AccWalk: 0.00086 # 0.0004 (VINS) # 0.00086 # 0.00086 # m/s^2.5 -IMU.Frequency: 200 +IMU.Frequency: 200.0 System.thFarPoints: 20.0 #-------------------------------------------------------------------------------------------- diff --git a/Examples/Monocular-Inertial/mono_inertial_euroc.cc b/Examples/Monocular-Inertial/mono_inertial_euroc.cc index b9f320fd76d..a6902a1785c 100644 --- a/Examples/Monocular-Inertial/mono_inertial_euroc.cc +++ b/Examples/Monocular-Inertial/mono_inertial_euroc.cc @@ -117,7 +117,7 @@ int main(int argc, char *argv[]) cout.precision(17); // Create SLAM system. It initializes all system threads and gets ready to process frames. - ORB_SLAM3::System SLAM(argv[1],argv[2],ORB_SLAM3::System::IMU_MONOCULAR, true); + ORB_SLAM3::System SLAM(argv[1],argv[2],ORB_SLAM3::System::IMU_MONOCULAR, false); float imageScale = SLAM.GetImageScale(); double t_resize = 0.f; @@ -151,7 +151,7 @@ int main(int argc, char *argv[]) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -161,7 +161,7 @@ int main(int argc, char *argv[]) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -187,7 +187,7 @@ int main(int argc, char *argv[]) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -197,7 +197,7 @@ int main(int argc, char *argv[]) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES @@ -239,11 +239,13 @@ int main(int argc, char *argv[]) const string f_file = "f_" + string(argv[argc-1]) + ".txt"; SLAM.SaveTrajectoryEuRoC(f_file); SLAM.SaveKeyFrameTrajectoryEuRoC(kf_file); + SLAM.SaveMapPoints(string(argv[argc-1]) + "_mappoints.txt"); } else { SLAM.SaveTrajectoryEuRoC("CameraTrajectory.txt"); SLAM.SaveKeyFrameTrajectoryEuRoC("KeyFrameTrajectory.txt"); + SLAM.SaveMapPoints("mappoints.txt"); } return 0; diff --git a/Examples/Monocular-Inertial/mono_inertial_realsense_D435i.cc b/Examples/Monocular-Inertial/mono_inertial_realsense_D435i.cc index c8bf1ff589d..885bf405aa1 100644 --- a/Examples/Monocular-Inertial/mono_inertial_realsense_D435i.cc +++ b/Examples/Monocular-Inertial/mono_inertial_realsense_D435i.cc @@ -317,7 +317,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point time_Start_Process = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #endif if(count_im_buffer>1) @@ -368,7 +368,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -378,7 +378,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -389,7 +389,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif #endif // Pass the image to the SLAM system @@ -398,7 +398,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t_End_Track - t_Start_Track).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples/Monocular-Inertial/mono_inertial_realsense_t265.cc b/Examples/Monocular-Inertial/mono_inertial_realsense_t265.cc index b4575b6acba..77484253536 100644 --- a/Examples/Monocular-Inertial/mono_inertial_realsense_t265.cc +++ b/Examples/Monocular-Inertial/mono_inertial_realsense_t265.cc @@ -233,7 +233,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point time_Start_Process = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #endif if(count_im_buffer>1) @@ -260,7 +260,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imCV.cols * imageScale; @@ -270,7 +270,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -311,7 +311,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system SLAM.TrackMonocular(im, timestamp, vImuMeas); @@ -319,7 +319,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES diff --git a/Examples/Monocular-Inertial/mono_inertial_tum_vi.cc b/Examples/Monocular-Inertial/mono_inertial_tum_vi.cc index bd84ce00121..91941e26047 100644 --- a/Examples/Monocular-Inertial/mono_inertial_tum_vi.cc +++ b/Examples/Monocular-Inertial/mono_inertial_tum_vi.cc @@ -116,7 +116,7 @@ int main(int argc, char **argv) cout << "IMU data in the sequence: " << nImu << endl << endl;*/ // Create SLAM system. It initializes all system threads and gets ready to process frames. - ORB_SLAM3::System SLAM(argv[1],argv[2],ORB_SLAM3::System::IMU_MONOCULAR, true, 0, file_name); + ORB_SLAM3::System SLAM(argv[1],argv[2],ORB_SLAM3::System::IMU_MONOCULAR, false, 0, file_name); float imageScale = SLAM.GetImageScale(); double t_resize = 0.f; @@ -175,7 +175,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -185,7 +185,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -198,7 +198,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -208,7 +208,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES @@ -257,11 +257,13 @@ int main(int argc, char **argv) const string f_file = "f_" + string(argv[argc-1]) + ".txt"; SLAM.SaveTrajectoryEuRoC(f_file); SLAM.SaveKeyFrameTrajectoryEuRoC(kf_file); + SLAM.SaveMapPoints(string(argv[argc-1]) + "_mappoints.txt"); } else { SLAM.SaveTrajectoryEuRoC("CameraTrajectory.txt"); SLAM.SaveKeyFrameTrajectoryEuRoC("KeyFrameTrajectory.txt"); + SLAM.SaveMapPoints("mappoints.txt"); } sort(vTimesTrack.begin(),vTimesTrack.end()); diff --git a/Examples/Monocular/mono_euroc.cc b/Examples/Monocular/mono_euroc.cc index 3a233129bea..23de92109c1 100644 --- a/Examples/Monocular/mono_euroc.cc +++ b/Examples/Monocular/mono_euroc.cc @@ -80,7 +80,7 @@ int main(int argc, char **argv) int fps = 20; float dT = 1.f/fps; // Create SLAM system. It initializes all system threads and gets ready to process frames. - ORB_SLAM3::System SLAM(argv[1],argv[2],ORB_SLAM3::System::MONOCULAR, false); + ORB_SLAM3::System SLAM(argv[1],argv[2],ORB_SLAM3::System::MONOCULAR, false);//切換視覺化 float imageScale = SLAM.GetImageScale(); double t_resize = 0.f; @@ -112,7 +112,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -122,7 +122,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -132,7 +132,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -142,7 +142,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES @@ -193,11 +193,13 @@ int main(int argc, char **argv) const string f_file = "f_" + string(argv[argc-1]) + ".txt"; SLAM.SaveTrajectoryEuRoC(f_file); SLAM.SaveKeyFrameTrajectoryEuRoC(kf_file); + SLAM.SaveMapPoints(string(argv[argc-1]) + "_mappoints.txt"); } else { SLAM.SaveTrajectoryEuRoC("CameraTrajectory.txt"); SLAM.SaveKeyFrameTrajectoryEuRoC("KeyFrameTrajectory.txt"); + SLAM.SaveMapPoints("mappoints.txt"); } return 0; diff --git a/Examples/Monocular/mono_kitti.cc b/Examples/Monocular/mono_kitti.cc index 404c8242c8e..60b4d087424 100644 --- a/Examples/Monocular/mono_kitti.cc +++ b/Examples/Monocular/mono_kitti.cc @@ -81,7 +81,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -91,7 +91,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -101,7 +101,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -110,7 +110,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES @@ -149,6 +149,7 @@ int main(int argc, char **argv) // Save camera trajectory SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt"); + SLAM.SaveMapPoints("mappoints.txt"); return 0; } diff --git a/Examples/Monocular/mono_realsense_D435i.cc b/Examples/Monocular/mono_realsense_D435i.cc index 11cbc035d2e..12ea68e9bed 100644 --- a/Examples/Monocular/mono_realsense_D435i.cc +++ b/Examples/Monocular/mono_realsense_D435i.cc @@ -231,7 +231,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point time_Start_Process = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #endif if(count_im_buffer>1) @@ -250,7 +250,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -261,7 +261,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -272,7 +272,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif #endif // Stereo images are already rectified. @@ -281,7 +281,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t_End_Track - t_Start_Track).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples/Monocular/mono_realsense_t265.cc b/Examples/Monocular/mono_realsense_t265.cc index 9895eef8ca2..d8a67679801 100644 --- a/Examples/Monocular/mono_realsense_t265.cc +++ b/Examples/Monocular/mono_realsense_t265.cc @@ -119,7 +119,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imCV.cols * imageScale; @@ -129,7 +129,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -144,7 +144,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif #endif @@ -155,7 +155,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t2 - t1).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples/Monocular/mono_tum.cc b/Examples/Monocular/mono_tum.cc index 0e7d922c378..6db8941ae01 100644 --- a/Examples/Monocular/mono_tum.cc +++ b/Examples/Monocular/mono_tum.cc @@ -82,7 +82,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -92,7 +92,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -102,7 +102,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -111,7 +111,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES @@ -150,6 +150,7 @@ int main(int argc, char **argv) // Save camera trajectory SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt"); + SLAM.SaveMapPoints("mappoints.txt"); return 0; } diff --git a/Examples/Monocular/mono_tum_vi.cc b/Examples/Monocular/mono_tum_vi.cc index 2805c925220..b6b45e7a3ad 100644 --- a/Examples/Monocular/mono_tum_vi.cc +++ b/Examples/Monocular/mono_tum_vi.cc @@ -115,7 +115,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -125,7 +125,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -148,7 +148,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -157,7 +157,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES @@ -205,11 +205,13 @@ int main(int argc, char **argv) const string f_file = "f_" + string(argv[argc-1]) + ".txt"; SLAM.SaveTrajectoryEuRoC(f_file); SLAM.SaveKeyFrameTrajectoryEuRoC(kf_file); + SLAM.SaveMapPoints(string(argv[argc-1]) + "_mappoints.txt"); } else { SLAM.SaveTrajectoryEuRoC("CameraTrajectory.txt"); SLAM.SaveKeyFrameTrajectoryEuRoC("KeyFrameTrajectory.txt"); + SLAM.SaveMapPoints("mappoints.txt"); } sort(vTimesTrack.begin(),vTimesTrack.end()); diff --git a/Examples/RGB-D-Inertial/rgbd_inertial_realsense_D435i.cc b/Examples/RGB-D-Inertial/rgbd_inertial_realsense_D435i.cc index 03970c511ac..0117656c953 100644 --- a/Examples/RGB-D-Inertial/rgbd_inertial_realsense_D435i.cc +++ b/Examples/RGB-D-Inertial/rgbd_inertial_realsense_D435i.cc @@ -348,7 +348,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point time_Start_Process = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #endif fs = fsSLAM; @@ -415,7 +415,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -427,7 +427,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -438,7 +438,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif #endif // Pass the image to the SLAM system @@ -448,7 +448,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t_End_Track - t_Start_Track).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples/RGB-D/rgbd_realsense_D435i.cc b/Examples/RGB-D/rgbd_realsense_D435i.cc index 8ff6131804a..c992603ca1a 100644 --- a/Examples/RGB-D/rgbd_realsense_D435i.cc +++ b/Examples/RGB-D/rgbd_realsense_D435i.cc @@ -326,7 +326,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point time_Start_Process = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #endif fs = fsSLAM; @@ -362,7 +362,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -374,7 +374,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -385,7 +385,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif #endif // Pass the image to the SLAM system @@ -395,7 +395,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t_End_Track - t_Start_Track).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples/RGB-D/rgbd_tum.cc b/Examples/RGB-D/rgbd_tum.cc index d420da52c39..9540bb7cf04 100644 --- a/Examples/RGB-D/rgbd_tum.cc +++ b/Examples/RGB-D/rgbd_tum.cc @@ -97,7 +97,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -106,7 +106,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif double ttrack= std::chrono::duration_cast >(t2 - t1).count(); @@ -141,6 +141,7 @@ int main(int argc, char **argv) // Save camera trajectory SLAM.SaveTrajectoryTUM("CameraTrajectory.txt"); SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt"); + SLAM.SaveMapPoints("mappoints.txt"); return 0; } diff --git a/Examples/Stereo-Inertial/EuRoC.yaml b/Examples/Stereo-Inertial/EuRoC.yaml index 517da5100c1..d231cb3f450 100644 --- a/Examples/Stereo-Inertial/EuRoC.yaml +++ b/Examples/Stereo-Inertial/EuRoC.yaml @@ -1,5 +1,5 @@ %YAML:1.0 - +#loopClosing: 0 #-------------------------------------------------------------------------------------------- # System config #-------------------------------------------------------------------------------------------- @@ -50,7 +50,7 @@ Camera.fps: 20 Camera.RGB: 1 # Close/Far threshold. Baseline times. -Stereo.ThDepth: 60.0 +Stereo.ThDepth: 40.0 Stereo.T_c1_c2: !!opencv-matrix rows: 4 cols: 4 diff --git a/Examples/Stereo-Inertial/TUM-VI.yaml b/Examples/Stereo-Inertial/TUM-VI.yaml index a0d38141fbd..35e7471f609 100755 --- a/Examples/Stereo-Inertial/TUM-VI.yaml +++ b/Examples/Stereo-Inertial/TUM-VI.yaml @@ -1,5 +1,5 @@ %YAML:1.0 - +#loopClosing: 0 #-------------------------------------------------------------------------------------------- # Camera Parameters. Adjust them! #-------------------------------------------------------------------------------------------- diff --git a/Examples/Stereo-Inertial/TUM-VI_far.yaml b/Examples/Stereo-Inertial/TUM-VI_far.yaml index adc0521884d..a6e92a0f030 100755 --- a/Examples/Stereo-Inertial/TUM-VI_far.yaml +++ b/Examples/Stereo-Inertial/TUM-VI_far.yaml @@ -1,5 +1,5 @@ %YAML:1.0 - +#loopClosing: 0 #-------------------------------------------------------------------------------------------- # Camera Parameters. Adjust them! #-------------------------------------------------------------------------------------------- diff --git a/Examples/Stereo-Inertial/stereo_inertial_euroc.cc b/Examples/Stereo-Inertial/stereo_inertial_euroc.cc index bb522235426..9e31f30cbd3 100644 --- a/Examples/Stereo-Inertial/stereo_inertial_euroc.cc +++ b/Examples/Stereo-Inertial/stereo_inertial_euroc.cc @@ -178,7 +178,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the images to the SLAM system @@ -187,7 +187,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES @@ -230,11 +230,13 @@ int main(int argc, char **argv) const string f_file = "f_" + string(argv[argc-1]) + ".txt"; SLAM.SaveTrajectoryEuRoC(f_file); SLAM.SaveKeyFrameTrajectoryEuRoC(kf_file); + SLAM.SaveMapPoints(string(argv[argc-1]) + "_mappoints.txt"); } else { SLAM.SaveTrajectoryEuRoC("CameraTrajectory.txt"); SLAM.SaveKeyFrameTrajectoryEuRoC("KeyFrameTrajectory.txt"); + SLAM.SaveMapPoints("mappoints.txt"); } return 0; diff --git a/Examples/Stereo-Inertial/stereo_inertial_realsense_D435i.cc b/Examples/Stereo-Inertial/stereo_inertial_realsense_D435i.cc index d3a03bf0056..d7fd8e87f72 100644 --- a/Examples/Stereo-Inertial/stereo_inertial_realsense_D435i.cc +++ b/Examples/Stereo-Inertial/stereo_inertial_realsense_D435i.cc @@ -347,7 +347,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point time_Start_Process = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #endif if(count_im_buffer>1) @@ -399,7 +399,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -411,7 +411,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -422,7 +422,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif #endif // Stereo images are already rectified. @@ -431,7 +431,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t_End_Track - t_Start_Track).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples/Stereo-Inertial/stereo_inertial_realsense_t265.cc b/Examples/Stereo-Inertial/stereo_inertial_realsense_t265.cc index 9fcd77588c8..7a4d3ac153f 100644 --- a/Examples/Stereo-Inertial/stereo_inertial_realsense_t265.cc +++ b/Examples/Stereo-Inertial/stereo_inertial_realsense_t265.cc @@ -246,7 +246,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imCV.cols * imageScale; @@ -257,7 +257,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -297,7 +297,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif #endif // Pass the image to the SLAM system @@ -306,7 +306,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t_End_Track - t_Start_Track).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples/Stereo-Inertial/stereo_inertial_tum_vi.cc b/Examples/Stereo-Inertial/stereo_inertial_tum_vi.cc index 2abe61472b3..b7b3e4ca95f 100644 --- a/Examples/Stereo-Inertial/stereo_inertial_tum_vi.cc +++ b/Examples/Stereo-Inertial/stereo_inertial_tum_vi.cc @@ -119,7 +119,7 @@ int main(int argc, char **argv) cout << "IMU data in the sequence: " << nImu << endl << endl;*/ // Create SLAM system. It initializes all system threads and gets ready to process frames. - ORB_SLAM3::System SLAM(argv[1],argv[2],ORB_SLAM3::System::IMU_STEREO, true, 0, file_name); + ORB_SLAM3::System SLAM(argv[1],argv[2],ORB_SLAM3::System::IMU_STEREO, false, 0, file_name); float imageScale = SLAM.GetImageScale(); double t_resize = 0.f; @@ -147,7 +147,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imLeft.cols * imageScale; @@ -158,7 +158,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -204,7 +204,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -213,7 +213,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES @@ -263,11 +263,13 @@ int main(int argc, char **argv) const string f_file = "f_" + string(argv[argc-1]) + ".txt"; SLAM.SaveTrajectoryEuRoC(f_file); SLAM.SaveKeyFrameTrajectoryEuRoC(kf_file); + SLAM.SaveMapPoints(string(argv[argc-1]) + "_mappoints.txt"); } else { SLAM.SaveTrajectoryEuRoC("CameraTrajectory.txt"); SLAM.SaveKeyFrameTrajectoryEuRoC("KeyFrameTrajectory.txt"); + SLAM.SaveMapPoints("mappoints.txt"); } sort(vTimesTrack.begin(),vTimesTrack.end()); diff --git a/Examples/Stereo/EuRoC.yaml b/Examples/Stereo/EuRoC.yaml index 531833b5114..72edac2b81a 100644 --- a/Examples/Stereo/EuRoC.yaml +++ b/Examples/Stereo/EuRoC.yaml @@ -1,5 +1,5 @@ %YAML:1.0 - +#loopClosing: 0 #-------------------------------------------------------------------------------------------- # System config #-------------------------------------------------------------------------------------------- @@ -49,7 +49,7 @@ Camera.fps: 20 # Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale) Camera.RGB: 1 -Stereo.ThDepth: 60.0 +Stereo.ThDepth: 35.0 Stereo.T_c1_c2: !!opencv-matrix rows: 4 cols: 4 diff --git a/Examples/Stereo/TUM-VI.yaml b/Examples/Stereo/TUM-VI.yaml index ad09c8ff6e1..088af75a137 100644 --- a/Examples/Stereo/TUM-VI.yaml +++ b/Examples/Stereo/TUM-VI.yaml @@ -45,8 +45,8 @@ Camera.width: 512 Camera.height: 512 # Lapping area between images -Camera.overlappingBegin: 0 -Camera.overlappingEnd: 511 +Camera1.overlappingBegin: 0 +Camera1.overlappingEnd: 511 Camera2.overlappingBegin: 0 Camera2.overlappingEnd: 511 diff --git a/Examples/Stereo/stereo_euroc.cc b/Examples/Stereo/stereo_euroc.cc index 1250f2ee3c0..24767290e8d 100644 --- a/Examples/Stereo/stereo_euroc.cc +++ b/Examples/Stereo/stereo_euroc.cc @@ -88,7 +88,7 @@ int main(int argc, char **argv) cout.precision(17); // Create SLAM system. It initializes all system threads and gets ready to process frames. - ORB_SLAM3::System SLAM(argv[1],argv[2],ORB_SLAM3::System::STEREO, true); + ORB_SLAM3::System SLAM(argv[1],argv[2],ORB_SLAM3::System::STEREO, false); cv::Mat imLeft, imRight; for (seq = 0; seq >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -105,7 +105,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the images to the SLAM system @@ -114,7 +114,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES @@ -153,6 +153,7 @@ int main(int argc, char **argv) // Save camera trajectory SLAM.SaveTrajectoryKITTI("CameraTrajectory.txt"); + SLAM.SaveMapPoints("mappoints.txt"); return 0; } diff --git a/Examples/Stereo/stereo_realsense_D435i.cc b/Examples/Stereo/stereo_realsense_D435i.cc index f1a45710b66..f61e05772fd 100644 --- a/Examples/Stereo/stereo_realsense_D435i.cc +++ b/Examples/Stereo/stereo_realsense_D435i.cc @@ -262,7 +262,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point time_Start_Process = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #endif if(count_im_buffer>1) @@ -282,7 +282,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -294,7 +294,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -305,7 +305,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif #endif // Stereo images are already rectified. @@ -314,7 +314,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t_End_Track - t_Start_Track).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples/Stereo/stereo_realsense_t265.cc b/Examples/Stereo/stereo_realsense_t265.cc index f96d80af354..b2824bf8967 100644 --- a/Examples/Stereo/stereo_realsense_t265.cc +++ b/Examples/Stereo/stereo_realsense_t265.cc @@ -129,7 +129,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imLeft.cols * imageScale; @@ -140,7 +140,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -154,7 +154,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif #endif @@ -165,7 +165,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #endif diff --git a/Examples/Stereo/stereo_tum_vi.cc b/Examples/Stereo/stereo_tum_vi.cc index 20a3ecf72d4..e31f7884a27 100644 --- a/Examples/Stereo/stereo_tum_vi.cc +++ b/Examples/Stereo/stereo_tum_vi.cc @@ -90,7 +90,7 @@ int main(int argc, char **argv) cout.precision(17); // Create SLAM system. It initializes all system threads and gets ready to process frames. - ORB_SLAM3::System SLAM(argv[1],argv[2],ORB_SLAM3::System::STEREO,true); + ORB_SLAM3::System SLAM(argv[1],argv[2],ORB_SLAM3::System::STEREO,false); float imageScale = SLAM.GetImageScale(); cout << endl << "-------" << endl; @@ -120,7 +120,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imLeft.cols * imageScale; @@ -131,7 +131,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -154,7 +154,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -163,7 +163,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES @@ -213,11 +213,13 @@ int main(int argc, char **argv) const string f_file = "f_" + string(argv[argc-1]) + ".txt"; SLAM.SaveTrajectoryEuRoC(f_file); SLAM.SaveKeyFrameTrajectoryEuRoC(kf_file); + SLAM.SaveMapPoints(string(argv[argc-1]) + "_mappoints.txt"); } else { SLAM.SaveTrajectoryEuRoC("CameraTrajectory.txt"); SLAM.SaveKeyFrameTrajectoryEuRoC("KeyFrameTrajectory.txt"); + SLAM.SaveMapPoints("mappoints.txt"); } sort(vTimesTrack.begin(),vTimesTrack.end()); diff --git a/Examples_old/Monocular-Inertial/mono_inertial_euroc.cc b/Examples_old/Monocular-Inertial/mono_inertial_euroc.cc index ea1dceb8053..a8446a3f62f 100644 --- a/Examples_old/Monocular-Inertial/mono_inertial_euroc.cc +++ b/Examples_old/Monocular-Inertial/mono_inertial_euroc.cc @@ -150,7 +150,7 @@ int main(int argc, char *argv[]) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -160,7 +160,7 @@ int main(int argc, char *argv[]) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -186,7 +186,7 @@ int main(int argc, char *argv[]) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -196,7 +196,7 @@ int main(int argc, char *argv[]) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES diff --git a/Examples_old/Monocular-Inertial/mono_inertial_realsense_D435i.cc b/Examples_old/Monocular-Inertial/mono_inertial_realsense_D435i.cc index c8bf1ff589d..885bf405aa1 100644 --- a/Examples_old/Monocular-Inertial/mono_inertial_realsense_D435i.cc +++ b/Examples_old/Monocular-Inertial/mono_inertial_realsense_D435i.cc @@ -317,7 +317,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point time_Start_Process = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #endif if(count_im_buffer>1) @@ -368,7 +368,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -378,7 +378,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -389,7 +389,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif #endif // Pass the image to the SLAM system @@ -398,7 +398,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t_End_Track - t_Start_Track).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples_old/Monocular-Inertial/mono_inertial_realsense_t265.cc b/Examples_old/Monocular-Inertial/mono_inertial_realsense_t265.cc index b4575b6acba..77484253536 100644 --- a/Examples_old/Monocular-Inertial/mono_inertial_realsense_t265.cc +++ b/Examples_old/Monocular-Inertial/mono_inertial_realsense_t265.cc @@ -233,7 +233,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point time_Start_Process = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #endif if(count_im_buffer>1) @@ -260,7 +260,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imCV.cols * imageScale; @@ -270,7 +270,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -311,7 +311,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system SLAM.TrackMonocular(im, timestamp, vImuMeas); @@ -319,7 +319,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES diff --git a/Examples_old/Monocular-Inertial/mono_inertial_tum_vi.cc b/Examples_old/Monocular-Inertial/mono_inertial_tum_vi.cc index bd84ce00121..ca1874be94e 100644 --- a/Examples_old/Monocular-Inertial/mono_inertial_tum_vi.cc +++ b/Examples_old/Monocular-Inertial/mono_inertial_tum_vi.cc @@ -175,7 +175,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -185,7 +185,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -198,7 +198,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -208,7 +208,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES diff --git a/Examples_old/Monocular/mono_euroc.cc b/Examples_old/Monocular/mono_euroc.cc index 3a233129bea..363939bc65f 100644 --- a/Examples_old/Monocular/mono_euroc.cc +++ b/Examples_old/Monocular/mono_euroc.cc @@ -112,7 +112,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -122,7 +122,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -132,7 +132,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -142,7 +142,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES diff --git a/Examples_old/Monocular/mono_kitti.cc b/Examples_old/Monocular/mono_kitti.cc index 404c8242c8e..ab5cde98585 100644 --- a/Examples_old/Monocular/mono_kitti.cc +++ b/Examples_old/Monocular/mono_kitti.cc @@ -81,7 +81,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -91,7 +91,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -101,7 +101,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -110,7 +110,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES diff --git a/Examples_old/Monocular/mono_realsense_D435i.cc b/Examples_old/Monocular/mono_realsense_D435i.cc index 11cbc035d2e..12ea68e9bed 100644 --- a/Examples_old/Monocular/mono_realsense_D435i.cc +++ b/Examples_old/Monocular/mono_realsense_D435i.cc @@ -231,7 +231,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point time_Start_Process = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #endif if(count_im_buffer>1) @@ -250,7 +250,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -261,7 +261,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -272,7 +272,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif #endif // Stereo images are already rectified. @@ -281,7 +281,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t_End_Track - t_Start_Track).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples_old/Monocular/mono_realsense_t265.cc b/Examples_old/Monocular/mono_realsense_t265.cc index 9895eef8ca2..d8a67679801 100644 --- a/Examples_old/Monocular/mono_realsense_t265.cc +++ b/Examples_old/Monocular/mono_realsense_t265.cc @@ -119,7 +119,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imCV.cols * imageScale; @@ -129,7 +129,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -144,7 +144,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif #endif @@ -155,7 +155,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t2 - t1).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples_old/Monocular/mono_tum.cc b/Examples_old/Monocular/mono_tum.cc index 0e7d922c378..2d627c18b72 100644 --- a/Examples_old/Monocular/mono_tum.cc +++ b/Examples_old/Monocular/mono_tum.cc @@ -82,7 +82,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -92,7 +92,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -102,7 +102,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -111,7 +111,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES diff --git a/Examples_old/Monocular/mono_tum_vi.cc b/Examples_old/Monocular/mono_tum_vi.cc index 2805c925220..31df34cb37d 100644 --- a/Examples_old/Monocular/mono_tum_vi.cc +++ b/Examples_old/Monocular/mono_tum_vi.cc @@ -115,7 +115,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -125,7 +125,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -148,7 +148,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -157,7 +157,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES diff --git a/Examples_old/RGB-D-Inertial/rgbd_inertial_realsense_D435i.cc b/Examples_old/RGB-D-Inertial/rgbd_inertial_realsense_D435i.cc index 03970c511ac..0117656c953 100644 --- a/Examples_old/RGB-D-Inertial/rgbd_inertial_realsense_D435i.cc +++ b/Examples_old/RGB-D-Inertial/rgbd_inertial_realsense_D435i.cc @@ -348,7 +348,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point time_Start_Process = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #endif fs = fsSLAM; @@ -415,7 +415,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -427,7 +427,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -438,7 +438,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif #endif // Pass the image to the SLAM system @@ -448,7 +448,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t_End_Track - t_Start_Track).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples_old/RGB-D/rgbd_realsense_D435i.cc b/Examples_old/RGB-D/rgbd_realsense_D435i.cc index 8ff6131804a..c992603ca1a 100644 --- a/Examples_old/RGB-D/rgbd_realsense_D435i.cc +++ b/Examples_old/RGB-D/rgbd_realsense_D435i.cc @@ -326,7 +326,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point time_Start_Process = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #endif fs = fsSLAM; @@ -362,7 +362,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -374,7 +374,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -385,7 +385,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif #endif // Pass the image to the SLAM system @@ -395,7 +395,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t_End_Track - t_Start_Track).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples_old/RGB-D/rgbd_tum.cc b/Examples_old/RGB-D/rgbd_tum.cc index d420da52c39..159c045304c 100644 --- a/Examples_old/RGB-D/rgbd_tum.cc +++ b/Examples_old/RGB-D/rgbd_tum.cc @@ -97,7 +97,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -106,7 +106,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif double ttrack= std::chrono::duration_cast >(t2 - t1).count(); diff --git a/Examples_old/Stereo-Inertial/stereo_inertial_euroc.cc b/Examples_old/Stereo-Inertial/stereo_inertial_euroc.cc index 25dbf3cf2be..1ebf48c10fc 100644 --- a/Examples_old/Stereo-Inertial/stereo_inertial_euroc.cc +++ b/Examples_old/Stereo-Inertial/stereo_inertial_euroc.cc @@ -197,7 +197,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Rect = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Rect = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Rect = std::chrono::steady_clock::now(); #endif #endif cv::remap(imLeft,imLeftRect,M1l,M2l,cv::INTER_LINEAR); @@ -207,7 +207,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Rect = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Rect = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Rect = std::chrono::steady_clock::now(); #endif t_rect = std::chrono::duration_cast >(t_End_Rect - t_Start_Rect).count(); SLAM.InsertRectTime(t_rect); @@ -220,7 +220,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imLeftRect.cols * imageScale; @@ -231,7 +231,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -255,7 +255,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the images to the SLAM system @@ -264,7 +264,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES diff --git a/Examples_old/Stereo-Inertial/stereo_inertial_realsense_D435i.cc b/Examples_old/Stereo-Inertial/stereo_inertial_realsense_D435i.cc index d3a03bf0056..d7fd8e87f72 100644 --- a/Examples_old/Stereo-Inertial/stereo_inertial_realsense_D435i.cc +++ b/Examples_old/Stereo-Inertial/stereo_inertial_realsense_D435i.cc @@ -347,7 +347,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point time_Start_Process = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #endif if(count_im_buffer>1) @@ -399,7 +399,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -411,7 +411,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -422,7 +422,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif #endif // Stereo images are already rectified. @@ -431,7 +431,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t_End_Track - t_Start_Track).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples_old/Stereo-Inertial/stereo_inertial_realsense_t265.cc b/Examples_old/Stereo-Inertial/stereo_inertial_realsense_t265.cc index 9fcd77588c8..7a4d3ac153f 100644 --- a/Examples_old/Stereo-Inertial/stereo_inertial_realsense_t265.cc +++ b/Examples_old/Stereo-Inertial/stereo_inertial_realsense_t265.cc @@ -246,7 +246,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imCV.cols * imageScale; @@ -257,7 +257,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -297,7 +297,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif #endif // Pass the image to the SLAM system @@ -306,7 +306,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t_End_Track - t_Start_Track).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples_old/Stereo-Inertial/stereo_inertial_tum_vi.cc b/Examples_old/Stereo-Inertial/stereo_inertial_tum_vi.cc index 2abe61472b3..3fcb2f930dc 100644 --- a/Examples_old/Stereo-Inertial/stereo_inertial_tum_vi.cc +++ b/Examples_old/Stereo-Inertial/stereo_inertial_tum_vi.cc @@ -147,7 +147,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imLeft.cols * imageScale; @@ -158,7 +158,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -204,7 +204,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -213,7 +213,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES diff --git a/Examples_old/Stereo/stereo_euroc.cc b/Examples_old/Stereo/stereo_euroc.cc index 08a59e42721..562f447707e 100644 --- a/Examples_old/Stereo/stereo_euroc.cc +++ b/Examples_old/Stereo/stereo_euroc.cc @@ -163,7 +163,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Rect = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Rect = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Rect = std::chrono::steady_clock::now(); #endif #endif cv::remap(imLeft,imLeftRect,M1l,M2l,cv::INTER_LINEAR); @@ -173,7 +173,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Rect = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Rect = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Rect = std::chrono::steady_clock::now(); #endif t_rect = std::chrono::duration_cast >(t_End_Rect - t_Start_Rect).count(); SLAM.InsertRectTime(t_rect); @@ -186,7 +186,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imLeftRect.cols * imageScale; @@ -197,7 +197,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -207,7 +207,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the images to the SLAM system @@ -216,7 +216,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES diff --git a/Examples_old/Stereo/stereo_kitti.cc b/Examples_old/Stereo/stereo_kitti.cc index fdc8ef04151..2f82038d5b3 100644 --- a/Examples_old/Stereo/stereo_kitti.cc +++ b/Examples_old/Stereo/stereo_kitti.cc @@ -84,7 +84,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imLeft.cols * imageScale; @@ -95,7 +95,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -105,7 +105,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the images to the SLAM system @@ -114,7 +114,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES diff --git a/Examples_old/Stereo/stereo_realsense_D435i.cc b/Examples_old/Stereo/stereo_realsense_D435i.cc index f1a45710b66..f61e05772fd 100644 --- a/Examples_old/Stereo/stereo_realsense_D435i.cc +++ b/Examples_old/Stereo/stereo_realsense_D435i.cc @@ -262,7 +262,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point time_Start_Process = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point time_Start_Process = std::chrono::steady_clock::now(); #endif if(count_im_buffer>1) @@ -282,7 +282,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = im.cols * imageScale; @@ -294,7 +294,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -305,7 +305,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Track = std::chrono::steady_clock::now(); #endif #endif // Stereo images are already rectified. @@ -314,7 +314,7 @@ int main(int argc, char **argv) { #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Track = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Track = std::chrono::steady_clock::now(); #endif t_track = t_resize + std::chrono::duration_cast >(t_End_Track - t_Start_Track).count(); SLAM.InsertTrackTime(t_track); diff --git a/Examples_old/Stereo/stereo_realsense_t265.cc b/Examples_old/Stereo/stereo_realsense_t265.cc index f96d80af354..b2824bf8967 100644 --- a/Examples_old/Stereo/stereo_realsense_t265.cc +++ b/Examples_old/Stereo/stereo_realsense_t265.cc @@ -129,7 +129,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imLeft.cols * imageScale; @@ -140,7 +140,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -154,7 +154,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif #endif @@ -165,7 +165,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #endif diff --git a/Examples_old/Stereo/stereo_tum_vi.cc b/Examples_old/Stereo/stereo_tum_vi.cc index 3e296f6d21b..4718bcd95ca 100644 --- a/Examples_old/Stereo/stereo_tum_vi.cc +++ b/Examples_old/Stereo/stereo_tum_vi.cc @@ -121,7 +121,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_Start_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_Start_Resize = std::chrono::steady_clock::now(); #endif #endif int width = imLeft.cols * imageScale; @@ -132,7 +132,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t_End_Resize = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t_End_Resize = std::chrono::steady_clock::now(); #endif t_resize = std::chrono::duration_cast >(t_End_Resize - t_Start_Resize).count(); SLAM.InsertResizeTime(t_resize); @@ -155,7 +155,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); #endif // Pass the image to the SLAM system @@ -164,7 +164,7 @@ int main(int argc, char **argv) #ifdef COMPILEDWITHC11 std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #else - std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now(); + std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now(); #endif #ifdef REGISTER_TIMES diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 9cecc1d4669..00000000000 --- a/LICENSE +++ /dev/null @@ -1,674 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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But first, please read -. diff --git a/READ.md b/READ.md new file mode 100644 index 00000000000..f993af02774 --- /dev/null +++ b/READ.md @@ -0,0 +1,4 @@ +- map_points_total 是結果（現在地圖裡剩多少點）。 +- new_map_points 是過程（這一幀嘗試新增了多少點）。 +估測腳本為run_stereo_inertial_benchmark.py裡面有說明使用方法 +localmapping_log.csv的地圖點有時候還是無法對齊，試試看在調低更新章數 \ No newline at end of file diff --git a/Thirdparty/DBoW2/DUtils/Timestamp.cpp b/Thirdparty/DBoW2/DUtils/Timestamp.cpp deleted file mode 100644 index 4551839f177..00000000000 --- a/Thirdparty/DBoW2/DUtils/Timestamp.cpp +++ /dev/null @@ -1,246 +0,0 @@ -/* - * File: Timestamp.cpp - * Author: Dorian Galvez-Lopez - * Date: March 2009 - * Description: timestamping functions - * - * Note: in windows, this class has a 1ms resolution - * - * License: see the LICENSE.txt file - * - */ - -#include -#include -#include -#include -#include -#include - -#ifdef _WIN32 -#ifndef WIN32 -#define WIN32 -#endif -#endif - -#ifdef WIN32 -#include -#define sprintf sprintf_s -#else -#include -#endif - -#include "Timestamp.h" - -using namespace std; - -using namespace DUtils; - -Timestamp::Timestamp(Timestamp::tOptions option) -{ - if(option & CURRENT_TIME) - setToCurrentTime(); - else if(option & ZERO) - setTime(0.); -} - -Timestamp::~Timestamp(void) -{ -} - -bool Timestamp::empty() const -{ - return m_secs == 0 && m_usecs == 0; -} - -void Timestamp::setToCurrentTime(){ - -#ifdef WIN32 - struct __timeb32 timebuffer; - _ftime32_s( &timebuffer ); // C4996 - // Note: _ftime is deprecated; consider using _ftime_s instead - m_secs = timebuffer.time; - m_usecs = timebuffer.millitm * 1000; -#else - struct timeval now; - gettimeofday(&now, NULL); - m_secs = now.tv_sec; - m_usecs = now.tv_usec; -#endif - -} - -void Timestamp::setTime(const string &stime){ - string::size_type p = stime.find('.'); - if(p == string::npos){ - m_secs = atol(stime.c_str()); - m_usecs = 0; - }else{ - m_secs = atol(stime.substr(0, p).c_str()); - - string s_usecs = stime.substr(p+1, 6); - m_usecs = atol(stime.substr(p+1).c_str()); - m_usecs *= (unsigned long)pow(10.0, double(6 - s_usecs.length())); - } -} - -void Timestamp::setTime(double s) -{ - m_secs = (unsigned long)s; - m_usecs = (s - (double)m_secs) * 1e6; -} - -double Timestamp::getFloatTime() const { - return double(m_secs) + double(m_usecs)/1000000.0; -} - -string Timestamp::getStringTime() const { - char buf[32]; - sprintf(buf, "%.6lf", this->getFloatTime()); - return string(buf); -} - -double Timestamp::operator- (const Timestamp &t) const { - return this->getFloatTime() - t.getFloatTime(); -} - -Timestamp& Timestamp::operator+= (double s) -{ - *this = *this + s; - return *this; -} - -Timestamp& Timestamp::operator-= (double s) -{ - *this = *this - s; - return *this; -} - -Timestamp Timestamp::operator+ (double s) const -{ - unsigned long secs = (long)floor(s); - unsigned long usecs = (long)((s - (double)secs) * 1e6); - - return this->plus(secs, usecs); -} - -Timestamp Timestamp::plus(unsigned long secs, unsigned long usecs) const -{ - Timestamp t; - - const unsigned long max = 1000000ul; - - if(m_usecs + usecs >= max) - t.setTime(m_secs + secs + 1, m_usecs + usecs - max); - else - t.setTime(m_secs + secs, m_usecs + usecs); - - return t; -} - -Timestamp Timestamp::operator- (double s) const -{ - unsigned long secs = (long)floor(s); - unsigned long usecs = (long)((s - (double)secs) * 1e6); - - return this->minus(secs, usecs); -} - -Timestamp Timestamp::minus(unsigned long secs, unsigned long usecs) const -{ - Timestamp t; - - const unsigned long max = 1000000ul; - - if(m_usecs < usecs) - t.setTime(m_secs - secs - 1, max - (usecs - m_usecs)); - else - t.setTime(m_secs - secs, m_usecs - usecs); - - return t; -} - -bool Timestamp::operator> (const Timestamp &t) const -{ - if(m_secs > t.m_secs) return true; - else if(m_secs == t.m_secs) return m_usecs > t.m_usecs; - else return false; -} - -bool Timestamp::operator>= (const Timestamp &t) const -{ - if(m_secs > t.m_secs) return true; - else if(m_secs == t.m_secs) return m_usecs >= t.m_usecs; - else return false; -} - -bool Timestamp::operator< (const Timestamp &t) const -{ - if(m_secs < t.m_secs) return true; - else if(m_secs == t.m_secs) return m_usecs < t.m_usecs; - else return false; -} - -bool Timestamp::operator<= (const Timestamp &t) const -{ - if(m_secs < t.m_secs) return true; - else if(m_secs == t.m_secs) return m_usecs <= t.m_usecs; - else return false; -} - -bool Timestamp::operator== (const Timestamp &t) const -{ - return(m_secs == t.m_secs && m_usecs == t.m_usecs); -} - - -string Timestamp::Format(bool machine_friendly) const -{ - struct tm tm_time; - - time_t t = (time_t)getFloatTime(); - -#ifdef WIN32 - localtime_s(&tm_time, &t); -#else - localtime_r(&t, &tm_time); -#endif - - char buffer[128]; - - if(machine_friendly) - { - strftime(buffer, 128, "%Y%m%d_%H%M%S", &tm_time); - } - else - { - strftime(buffer, 128, "%c", &tm_time); // Thu Aug 23 14:55:02 2001 - } - - return string(buffer); -} - -string Timestamp::Format(double s) { - int days = int(s / (24. * 3600.0)); - s -= days * (24. * 3600.0); - int hours = int(s / 3600.0); - s -= hours * 3600; - int minutes = int(s / 60.0); - s -= minutes * 60; - int seconds = int(s); - int ms = int((s - seconds)*1e6); - - stringstream ss; - ss.fill('0'); - bool b; - if((b = (days > 0))) ss << days << "d "; - if((b = (b || hours > 0))) ss << setw(2) << hours << ":"; - if((b = (b || minutes > 0))) ss << setw(2) << minutes << ":"; - if(b) ss << setw(2); - ss << seconds; - if(!b) ss << "." << setw(6) << ms; - - return ss.str(); -} - - diff --git a/Thirdparty/DBoW2/DUtils/Timestamp.h b/Thirdparty/DBoW2/DUtils/Timestamp.h deleted file mode 100644 index b92f89f8729..00000000000 --- a/Thirdparty/DBoW2/DUtils/Timestamp.h +++ /dev/null @@ -1,204 +0,0 @@ -/* - * File: Timestamp.h - * Author: Dorian Galvez-Lopez - * Date: March 2009 - * Description: timestamping functions - * License: see the LICENSE.txt file - * - */ - -#ifndef __D_TIMESTAMP__ -#define __D_TIMESTAMP__ - -#include -using namespace std; - -namespace DUtils { - -/// Timestamp -class Timestamp -{ -public: - - /// Options to initiate a timestamp - enum tOptions - { - NONE = 0, - CURRENT_TIME = 0x1, - ZERO = 0x2 - }; - -public: - - /** - * Creates a timestamp - * @param option option to set the initial time stamp - */ - Timestamp(Timestamp::tOptions option = NONE); - - /** - * Destructor - */ - virtual ~Timestamp(void); - - /** - * Says if the timestamp is "empty": seconds and usecs are both 0, as - * when initiated with the ZERO flag - * @return true iif secs == usecs == 0 - */ - bool empty() const; - - /** - * Sets this instance to the current time - */ - void setToCurrentTime(); - - /** - * Sets the timestamp from seconds and microseconds - * @param secs: seconds - * @param usecs: microseconds - */ - inline void setTime(unsigned long secs, unsigned long usecs){ - m_secs = secs; - m_usecs = usecs; - } - - /** - * Returns the timestamp in seconds and microseconds - * @param secs seconds - * @param usecs microseconds - */ - inline void getTime(unsigned long &secs, unsigned long &usecs) const - { - secs = m_secs; - usecs = m_usecs; - } - - /** - * Sets the timestamp from a string with the time in seconds - * @param stime: string such as "1235603336.036609" - */ - void setTime(const string &stime); - - /** - * Sets the timestamp from a number of seconds from the epoch - * @param s seconds from the epoch - */ - void setTime(double s); - - /** - * Returns this timestamp as the number of seconds in (long) float format - */ - double getFloatTime() const; - - /** - * Returns this timestamp as the number of seconds in fixed length string format - */ - string getStringTime() const; - - /** - * Returns the difference in seconds between this timestamp (greater) and t (smaller) - * If the order is swapped, a negative number is returned - * @param t: timestamp to subtract from this timestamp - * @return difference in seconds - */ - double operator- (const Timestamp &t) const; - - /** - * Returns a copy of this timestamp + s seconds + us microseconds - * @param s seconds - * @param us microseconds - */ - Timestamp plus(unsigned long s, unsigned long us) const; - - /** - * Returns a copy of this timestamp - s seconds - us microseconds - * @param s seconds - * @param us microseconds - */ - Timestamp minus(unsigned long s, unsigned long us) const; - - /** - * Adds s seconds to this timestamp and returns a reference to itself - * @param s seconds - * @return reference to this timestamp - */ - Timestamp& operator+= (double s); - - /** - * Substracts s seconds to this timestamp and returns a reference to itself - * @param s seconds - * @return reference to this timestamp - */ - Timestamp& operator-= (double s); - - /** - * Returns a copy of this timestamp + s seconds - * @param s: seconds - */ - Timestamp operator+ (double s) const; - - /** - * Returns a copy of this timestamp - s seconds - * @param s: seconds - */ - Timestamp operator- (double s) const; - - /** - * Returns whether this timestamp is at the future of t - * @param t - */ - bool operator> (const Timestamp &t) const; - - /** - * Returns whether this timestamp is at the future of (or is the same as) t - * @param t - */ - bool operator>= (const Timestamp &t) const; - - /** - * Returns whether this timestamp and t represent the same instant - * @param t - */ - bool operator== (const Timestamp &t) const; - - /** - * Returns whether this timestamp is at the past of t - * @param t - */ - bool operator< (const Timestamp &t) const; - - /** - * Returns whether this timestamp is at the past of (or is the same as) t - * @param t - */ - bool operator<= (const Timestamp &t) const; - - /** - * Returns the timestamp in a human-readable string - * @param machine_friendly if true, the returned string is formatted - * to yyyymmdd_hhmmss, without weekday or spaces - * @note This has not been tested under Windows - * @note The timestamp is truncated to seconds - */ - string Format(bool machine_friendly = false) const; - - /** - * Returns a string version of the elapsed time in seconds, with the format - * xd hh:mm:ss, hh:mm:ss, mm:ss or s.us - * @param s: elapsed seconds (given by getFloatTime) to format - */ - static string Format(double s); - - -protected: - /// Seconds - unsigned long m_secs; // seconds - /// Microseconds - unsigned long m_usecs; // microseconds -}; - -} - -#endif - diff --git a/Thirdparty/g2o/CMakeLists.txt b/Thirdparty/g2o/CMakeLists.txt index 1a32ff9a2b2..83f3b3203d3 100644 --- a/Thirdparty/g2o/CMakeLists.txt +++ b/Thirdparty/g2o/CMakeLists.txt @@ -1,4 +1,4 @@ -CMAKE_MINIMUM_REQUIRED(VERSION 2.6) +CMAKE_MINIMUM_REQUIRED(VERSION 3.10) SET(CMAKE_LEGACY_CYGWIN_WIN32 0) PROJECT(g2o) diff --git a/Thirdparty/g2o/g2o/stuff/timeutil.cpp b/Thirdparty/g2o/g2o/stuff/timeutil.cpp deleted file mode 100644 index ec190519e6e..00000000000 --- a/Thirdparty/g2o/g2o/stuff/timeutil.cpp +++ /dev/null @@ -1,124 +0,0 @@ -// g2o - General Graph Optimization -// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, W. Burgard -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are -// met: -// -// * Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// * Redistributions in binary form must reproduce the above copyright -// notice, this list of conditions and the following disclaimer in the -// documentation and/or other materials provided with the distribution. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS -// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED -// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED -// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR -// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF -// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING -// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -#include "timeutil.h" -#include - -#ifdef _WINDOWS -#include -#include -#endif - -#ifdef UNIX -#include -#endif - -namespace g2o { - -#ifdef _WINDOWS -#if defined(_MSC_VER) || defined(_MSC_EXTENSIONS) - #define DELTA_EPOCH_IN_MICROSECS 11644473600000000Ui64 -#else - #define DELTA_EPOCH_IN_MICROSECS 11644473600000000ULL -#endif - -struct timezone -{ - int tz_minuteswest; /* minutes W of Greenwich */ - int tz_dsttime; /* type of dst correction */ -}; - -int gettimeofday(struct timeval *tv, struct timezone *tz) -{ -// Define a structure to receive the current Windows filetime - FILETIME ft; - -// Initialize the present time to 0 and the timezone to UTC - unsigned __int64 tmpres = 0; - static int tzflag = 0; - - if (NULL != tv) - { - GetSystemTimeAsFileTime(&ft); - -// The GetSystemTimeAsFileTime returns the number of 100 nanosecond -// intervals since Jan 1, 1601 in a structure. Copy the high bits to -// the 64 bit tmpres, shift it left by 32 then or in the low 32 bits. - tmpres |= ft.dwHighDateTime; - tmpres <<= 32; - tmpres |= ft.dwLowDateTime; - -// Convert to microseconds by dividing by 10 - tmpres /= 10; - -// The Unix epoch starts on Jan 1 1970. Need to subtract the difference -// in seconds from Jan 1 1601. - tmpres -= DELTA_EPOCH_IN_MICROSECS; - -// Finally change microseconds to seconds and place in the seconds value. -// The modulus picks up the microseconds. - tv->tv_sec = (long)(tmpres / 1000000UL); - tv->tv_usec = (long)(tmpres % 1000000UL); - } - - if (NULL != tz) { - if (!tzflag) { - _tzset(); - tzflag++; - } - - long sec; - int hours; - _get_timezone(&sec); - _get_daylight(&hours); - -// Adjust for the timezone west of Greenwich - tz->tz_minuteswest = sec / 60; - tz->tz_dsttime = hours; - } - - return 0; -} -#endif - -ScopeTime::ScopeTime(const char* title) : _title(title), _startTime(get_monotonic_time()) {} - -ScopeTime::~ScopeTime() { - std::cerr << _title<<" took "<<1000*(get_monotonic_time()-_startTime)<<"ms.\n"; -} - -double get_monotonic_time() -{ -#if (defined(_POSIX_TIMERS) && (_POSIX_TIMERS+0 >= 0) && defined(_POSIX_MONOTONIC_CLOCK)) - struct timespec ts; - clock_gettime(CLOCK_MONOTONIC, &ts); - return ts.tv_sec + ts.tv_nsec*1e-9; -#else - return get_time(); -#endif -} - -} // end namespace diff --git a/Thirdparty/g2o/g2o/stuff/timeutil.h b/Thirdparty/g2o/g2o/stuff/timeutil.h deleted file mode 100644 index bde8e318ae7..00000000000 --- a/Thirdparty/g2o/g2o/stuff/timeutil.h +++ /dev/null @@ -1,132 +0,0 @@ -// g2o - General Graph Optimization -// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, W. Burgard -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are -// met: -// -// * Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// * Redistributions in binary form must reproduce the above copyright -// notice, this list of conditions and the following disclaimer in the -// documentation and/or other materials provided with the distribution. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS -// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED -// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED -// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR -// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF -// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING -// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -#ifndef G2O_TIMEUTIL_H -#define G2O_TIMEUTIL_H - -#ifdef _WINDOWS -#include -#else -#include -#endif - -#include - - -/** @addtogroup utils **/ -// @{ - -/** \file timeutil.h - * \brief utility functions for handling time related stuff - */ - -/// Executes code, only if secs are gone since last exec. -/// extended version, in which the current time is given, e.g., timestamp of IPC message -#ifndef DO_EVERY_TS -#define DO_EVERY_TS(secs, currentTime, code) \ -if (1) {\ - static double s_lastDone_ = (currentTime); \ - double s_now_ = (currentTime); \ - if (s_lastDone_ > s_now_) \ - s_lastDone_ = s_now_; \ - if (s_now_ - s_lastDone_ > (secs)) { \ - code; \ - s_lastDone_ = s_now_; \ - }\ -} else \ - (void)0 -#endif - -/// Executes code, only if secs are gone since last exec. -#ifndef DO_EVERY -#define DO_EVERY(secs, code) DO_EVERY_TS(secs, g2o::get_time(), code) -#endif - -#ifndef MEASURE_TIME -#define MEASURE_TIME(text, code) \ - if(1) { \ - double _start_time_ = g2o::get_time(); \ - code; \ - fprintf(stderr, "%s took %f sec\n", text, g2o::get_time() - _start_time_); \ - } else \ - (void) 0 -#endif - -namespace g2o { - -#ifdef _WINDOWS -typedef struct timeval { - long tv_sec; - long tv_usec; -} timeval; - int gettimeofday(struct timeval *tv, struct timezone *tz); -#endif - -/** - * return the current time in seconds since 1. Jan 1970 - */ -inline double get_time() -{ - struct timeval ts; - gettimeofday(&ts,0); - return ts.tv_sec + ts.tv_usec*1e-6; -} - -/** - * return a monotonic increasing time which basically does not need to - * have a reference point. Consider this for measuring how long some - * code fragments required to execute. - * - * On Linux we call clock_gettime() on other systems we currently - * call get_time(). - */ - double get_monotonic_time(); - -/** - * \brief Class to measure the time spent in a scope - * - * To use this class, e.g. to measure the time spent in a function, - * just create and instance at the beginning of the function. - */ -class ScopeTime { - public: - ScopeTime(const char* title); - ~ScopeTime(); - private: - std::string _title; - double _startTime; -}; - -} // end namespace - -#ifndef MEASURE_FUNCTION_TIME -#define MEASURE_FUNCTION_TIME \ - g2o::ScopeTime scopeTime(__PRETTY_FUNCTION__) -#endif - - -// @} -#endif diff --git a/[1]J21-ORB_SLAM3.pdf b/[1]J21-ORB_SLAM3.pdf new file mode 100644 index 00000000000..b0abf2325bd Binary files /dev/null and b/[1]J21-ORB_SLAM3.pdf differ diff --git a/data.tum b/data.tum new file mode 100644 index 00000000000..3064076dc0a --- /dev/null +++ b/data.tum @@ -0,0 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5.546609999999999596e-01 1.549369999999999914e-01 diff --git a/evaluation/analyze_trajectory.py b/evaluation/analyze_trajectory.py new file mode 100644 index 00000000000..e0c269c9055 --- /dev/null +++ b/evaluation/analyze_trajectory.py @@ -0,0 +1,1162 @@ +#!/usr/bin/env python3 +""" +軌跡診斷腳本 +============= +將估計軌跡與 GT 對齊後計算每幀 ATE / RPE，並把 +tracking_log.csv、localmapping_log.csv、loop_closing_log.csv +對齊到同一條 dataset 時間軸，找出高誤差區段附近發生的 +Tracking / LocalMapping / LoopClosing 診斷事件。 + +使用方式： + # 最常用：自動尋找 fix_{name}.txt 與三份 log，輸出 PDF + intervals CSV + python3 evaluation/analyze_trajectory.py \ + --name stereo_imu_euroc_v102 \ + --gt /media/lab405/Windows1/data/Euroc/V1_02_medium/mav0/state_groundtruth_estimate0/data.csv \ + --gt-format euroc + + # 指定 log / output，適合比較不同實驗版本 + python3 evaluation/analyze_trajectory.py \ + --name stereo_imu_euroc_v102_close_lp \ + --gt evaluation/Ground_truth/EuRoC_left_cam/V102_GT.txt \ + --gt-format euroc \ + --traj evaluation/fix_stereo_imu_euroc_v102_close_lp.txt \ + --log evaluation/stereo_imu_euroc_v102_tracking_close_lp_log.csv \ + --local-log evaluation/stereo_imu_euroc_v102_localmapping_close_lp_log.csv \ + --loop-log evaluation/stereo_imu_euroc_v102_loop_closing_close_lp_log.csv \ + --output evaluation/diagnosis_stereo_imu_euroc_v102_close_lp.pdf \ + --interval-output evaluation/diagnosis_stereo_imu_euroc_v102_close_lp_intervals.csv + + # TUM-VI / mocap GT + python3 evaluation/analyze_trajectory.py \ + --name stereo_imu_tum_out6 \ + --gt /media/lab405/Windows1/data/TUM/outdoors/dataset-outdoors6_512_16/mav0/mocap0/data.csv \ + --gt-format tum + +引數： + --name output_name；預設對應 evaluation/fix_{name}.txt + 以及 evaluation/{name}_tracking_log.csv + --gt GT 檔案路徑 + --gt-format euroc 或 tum + --traj 直接指定估計軌跡路徑（可選） + --log 直接指定 Tracking log 路徑（可選，自動搜尋） + --local-log 直接指定 LocalMapping log 路徑（可選，自動搜尋） + --loop-log 直接指定 LoopClosing log 路徑（可選，自動搜尋） + --output 診斷 PDF（預設 evaluation/diagnosis_{name}.pdf） + --interval-output 高誤差區段 CSV + （預設和 PDF 同名，加上 _intervals.csv） + --error-percentile ATE 高誤差門檻 percentile（預設 90） + --max-intervals 最多輸出幾段高誤差區段（預設 8） + --before-window 回查每段高誤差區段前方幾秒（預設 1.0） + --after-window 回查每段高誤差區段後方幾秒（預設 0.5） + --no-align 關閉 SE3 對齊（debug 用） + +輸出： + diagnosis_{name}.pdf + 兩頁式診斷圖。每個子圖的 x 軸都是相同的相對 dataset time： + timestamp - 第一個成功對齊 GT 的估計軌跡 timestamp。 + + Page 1: ATE/RPE、tracking inliers、tracking state/method。 + Page 2: visual/IMU residual、bias、backend correction magnitude、 + LocalMapping BA rug marker、LoopClosing/GBA rug marker。Rug marker 是子圖底部短線， + 避免事件太多時把曲線蓋住。 + LocalMapping 的 bias/velocity/pose correction 欄位表示該次 + Local BA local KF window 內的最大 before/after 修正量。 + + diagnosis_{name}_intervals.csv + 每段高誤差區間的 start/end/peak time、peak ATE、max RPE、 + dATE/dt，以及 rule-based diagnosis。 +""" + +import argparse +import csv +import os +import sys +from pathlib import Path + +os.environ.setdefault("MPLCONFIGDIR", "/tmp/matplotlib") +os.environ.setdefault("XDG_CACHE_HOME", "/tmp") + +import matplotlib +matplotlib.use("Agg") +import matplotlib.patches as mpatches +import matplotlib.pyplot as plt +from matplotlib.backends.backend_pdf import PdfPages +from matplotlib.lines import Line2D +import numpy as np +import pandas as pd + +SCRIPT_DIR = Path(__file__).parent.resolve() +X_AXIS_LABEL = "Relative dataset time from first matched trajectory frame (s)" +RECENTLY_LOST_COLOR = "#f59e0b" +LOST_COLOR = "#dc2626" +HIGH_ERROR_COLOR = "#fbbf24" +DATE_COLOR = "#0f766e" +LOCAL_BA_COLOR = "#7c3aed" +LOOP_EVENT_COLOR = "#e11d48" +RELOC_COLOR = "#be123c" + +# ───────────────────────────────────────────── +# GT 載入 +# ───────────────────────────────────────────── + +def load_gt_euroc(gt_path: Path) -> pd.DataFrame: + """EuRoC GT：timestamp_ns, px, py, pz, qw, qx, qy, qz""" + rows = [] + with open(gt_path) as f: + for line in f: + line = line.strip() + if not line or line.startswith('#'): + continue + parts = line.split(',') + if len(parts) < 8: + continue + ts = float(parts[0]) / 1e9 + px, py, pz = float(parts[1]), float(parts[2]), float(parts[3]) + rows.append({"timestamp": ts, "px": px, "py": py, "pz": pz}) + return pd.DataFrame(rows) + + +def load_gt_tum(gt_path: Path) -> pd.DataFrame: + """TUM-VI GT：同 EuRoC 格式（mocap0/data.csv）""" + return load_gt_euroc(gt_path) + + +# ───────────────────────────────────────────── +# 估計軌跡載入（TUM 格式） +# ───────────────────────────────────────────── + +def load_traj(traj_path: Path) -> pd.DataFrame: + """TUM 格式：timestamp tx ty tz qx qy qz qw""" + rows = [] + with open(traj_path) as f: + for line in f: + line = line.strip() + if not line or line.startswith('#'): + continue + parts = line.split() + if len(parts) < 4: + continue + ts = float(parts[0]) + px, py, pz = float(parts[1]), float(parts[2]), float(parts[3]) + rows.append({"timestamp": ts, "px": px, "py": py, "pz": pz}) + return pd.DataFrame(rows) + + +# ───────────────────────────────────────────── +# 時間戳對齊（最近鄰） +# ───────────────────────────────────────────── + +def match_timestamps(est: pd.DataFrame, gt: pd.DataFrame, + max_diff: float = 0.05) -> tuple: + """對每個估計幀找最近的 GT 幀，回傳對齊後的兩個 numpy array (N,3)""" + est_ts = est["timestamp"].values + gt_ts = gt["timestamp"].values + gt_pos = gt[["px", "py", "pz"]].values + + est_matched, gt_matched, timestamps = [], [], [] + for i, ts in enumerate(est_ts): + idx = np.argmin(np.abs(gt_ts - ts)) + if np.abs(gt_ts[idx] - ts) < max_diff: + est_matched.append(est[["px", "py", "pz"]].values[i]) + gt_matched.append(gt_pos[idx]) + timestamps.append(ts) + + return (np.array(timestamps), + np.array(est_matched), + np.array(gt_matched)) + + +# ───────────────────────────────────────────── +# SE3 對齊（Umeyama，無尺度） +# ───────────────────────────────────────────── + +def align_se3(est_pos: np.ndarray, gt_pos: np.ndarray): + """回傳 R (3x3), t (3,)，使 R @ est + t ≈ gt""" + mu_e = est_pos.mean(axis=0) + mu_g = gt_pos.mean(axis=0) + ec = est_pos - mu_e + gc = gt_pos - mu_g + H = ec.T @ gc + U, _, Vt = np.linalg.svd(H) + d = np.linalg.det(Vt.T @ U.T) + D = np.diag([1.0, 1.0, d]) + R = Vt.T @ D @ U.T + t = mu_g - R @ mu_e + return R, t + + +# ───────────────────────────────────────────── +# Log 載入與欄位診斷 +# ───────────────────────────────────────────── + +def resolve_tracking_log(name: str, explicit_path: str | None) -> Path: + """自動尋找 Tracking log；支援一般檔名與 close_lp 歷史檔名。""" + if explicit_path: + return Path(explicit_path) + + candidates = [SCRIPT_DIR / f"{name}_tracking_log.csv"] + if name.endswith("_close_lp"): + base = name[: -len("_close_lp")] + candidates.append(SCRIPT_DIR / f"{base}_tracking_close_lp_log.csv") + candidates.append(SCRIPT_DIR / f"{name}_tracking_close_lp_log.csv") + + for p in candidates: + if p.exists() and p.stat().st_size > 0: + return p + return candidates[0] + + +def resolve_localmapping_log(name: str, explicit_path: str | None) -> Path | None: + """自動尋找 LocalMapping log。close_lp 的檔名歷史上放在 suffix 前面，這裡一起支援。""" + if explicit_path: + p = Path(explicit_path) + return p if p.exists() else None + + candidates = [SCRIPT_DIR / f"{name}_localmapping_log.csv"] + if name.endswith("_close_lp"): + base = name[: -len("_close_lp")] + candidates.append(SCRIPT_DIR / f"{base}_localmapping_close_lp_log.csv") + candidates.append(SCRIPT_DIR / f"{name}_localmapping_close_lp_log.csv") + + for p in candidates: + if p.exists() and p.stat().st_size > 0: + return p + return None + + +def resolve_loopclosing_log(name: str, explicit_path: str | None) -> Path | None: + if explicit_path: + p = Path(explicit_path) + return p if p.exists() and p.stat().st_size > 0 else None + + candidates = [SCRIPT_DIR / f"{name}_loop_closing_log.csv"] + if name.endswith("_close_lp"): + base = name[: -len("_close_lp")] + candidates.append(SCRIPT_DIR / f"{base}_loop_closing_close_lp_log.csv") + candidates.append(SCRIPT_DIR / f"{name}_loop_closing_close_lp_log.csv") + + for p in candidates: + if p.exists() and p.stat().st_size > 0: + return p + return None + + +def coerce_numeric_when_possible(df: pd.DataFrame) -> pd.DataFrame: + for col in df.columns: + converted = pd.to_numeric(df[col], errors="coerce") + if converted.notna().any(): + df[col] = converted + return df + + +def load_localmapping_log(path: Path) -> pd.DataFrame: + """ + LocalMapping log 結尾會附上 survive KF 清單，欄位數比 header 少。 + 另外舊版 IMU_INITIALIZATION row 曾經多寫一個 -1 placeholder； + 這裡會讀成可修復的表格，避免 pandas 因欄位數不一致直接中止。 + """ + rows = [] + repaired_extra_fields = 0 + with open(path, newline="") as f: + reader = csv.reader(f) + header = None + for line_no, row in enumerate(reader, start=1): + if not row or (row[0].strip().startswith("#")): + continue + if header is None: + header = row + continue + + while len(row) > len(header) and row[-1] == "": + row = row[:-1] + + if len(row) == len(header) + 1 and "init_scale" in header and "IMU_INITIALIZATION" in row: + drop_idx = header.index("init_scale") + row = row[:drop_idx] + row[drop_idx + 1:] + repaired_extra_fields += 1 + + if len(row) > len(header): + print(f"[WARN] LocalMapping log line {line_no} has {len(row)} fields, expected {len(header)}; truncating extras.") + row = row[:len(header)] + elif len(row) < len(header): + row = row + [""] * (len(header) - len(row)) + + rows.append(row) + + if header is None: + return pd.DataFrame() + if repaired_extra_fields: + print(f"[WARN] Repaired {repaired_extra_fields} LocalMapping IMU_INITIALIZATION row(s) with one extra placeholder field.") + + df = pd.DataFrame(rows, columns=header) + df = coerce_numeric_when_possible(df) + if "track_frame_id" in df.columns: + df["track_frame_id"] = pd.to_numeric(df["track_frame_id"], errors="coerce") + df = df[df["track_frame_id"].notna()].copy() + df["track_frame_id"] = df["track_frame_id"].astype(int) + return df + + +def load_loopclosing_log(path: Path) -> pd.DataFrame: + df = pd.read_csv(path, comment="#") + return coerce_numeric_when_possible(df) + + +def find_inertial_residual_columns(df: pd.DataFrame) -> list[str]: + """找真正像 IMU/inertial residual 或 chi2 的欄位，避免把 bias_norm 誤判成 residual。""" + cols = [] + for col in df.columns: + c = col.lower() + has_imu_name = any(k in c for k in ["imu", "inertial", "preint"]) + has_res_name = any(k in c for k in ["residual", "chi2", "error"]) + excluded = any(k in c for k in ["initialized", "predicted", "time", "ms", "bias"]) + if has_imu_name and has_res_name and not excluded: + cols.append(col) + return cols + + +def columns_containing(df: pd.DataFrame, keywords: list[str]) -> list[str]: + cols = [] + for col in df.columns: + c = col.lower() + if any(k in c for k in keywords): + cols.append(col) + return cols + + +def numeric_series(df: pd.DataFrame, col: str) -> pd.Series: + return pd.to_numeric(df[col], errors="coerce") + + +def diagnostic_series(df: pd.DataFrame, col: str) -> pd.Series: + """Diagnostic numeric columns use -1 as 'not collected'; hide it in plots.""" + vals = numeric_series(df, col) + return vals.mask(vals <= -0.999999) + + +def first_existing_column(df: pd.DataFrame, candidates: list[str]) -> str | None: + for col in candidates: + if col in df.columns: + return col + return None + + +def add_relative_time(df: pd.DataFrame, common_t0: float) -> pd.DataFrame: + if df is None or df.empty: + return pd.DataFrame() + out = df.copy() + if "timestamp" in out.columns: + out["_rel_time_s"] = pd.to_numeric(out["timestamp"], errors="coerce") - common_t0 + elif "rel_time_s" in out.columns: + out["_rel_time_s"] = pd.to_numeric(out["rel_time_s"], errors="coerce") + else: + out["_rel_time_s"] = np.nan + return out + + +def compute_rpe(est_pos_aligned: np.ndarray, gt_pos: np.ndarray) -> np.ndarray: + rpe = np.zeros(len(est_pos_aligned), dtype=float) + if len(est_pos_aligned) > 1: + est_delta = np.diff(est_pos_aligned, axis=0) + gt_delta = np.diff(gt_pos, axis=0) + rpe[1:] = np.linalg.norm(est_delta - gt_delta, axis=1) + return rpe + + +def compute_error_derivative(rel_ts: np.ndarray, ate: np.ndarray) -> np.ndarray: + deriv = np.zeros(len(ate), dtype=float) + if len(ate) > 1: + dt = np.diff(rel_ts) + de = np.diff(ate) + deriv[1:] = np.divide(de, dt, out=np.zeros_like(de), where=np.abs(dt) > 1e-9) + return deriv + + +def find_high_error_intervals(rel_ts: np.ndarray, ate: np.ndarray, rpe: np.ndarray, + percentile: float = 90.0, + max_intervals: int = 8) -> list[dict]: + if len(rel_ts) == 0: + return [] + threshold = float(np.percentile(ate, percentile)) + mask = ate >= threshold + intervals = [] + start = None + for i, active in enumerate(mask): + if active and start is None: + start = i + elif not active and start is not None: + intervals.append((start, i - 1)) + start = None + if start is not None: + intervals.append((start, len(mask) - 1)) + + rows = [] + for s, e in intervals: + peak_local = s + int(np.argmax(ate[s:e+1])) + rows.append({ + "start_time": float(rel_ts[s]), + "end_time": float(rel_ts[e]), + "peak_time": float(rel_ts[peak_local]), + "peak_ATE": float(ate[peak_local]), + "mean_ATE": float(np.mean(ate[s:e+1])), + "max_RPE": float(np.max(rpe[s:e+1])), + "_score": float(ate[peak_local]), + }) + rows.sort(key=lambda x: x["_score"], reverse=True) + return rows[:max_intervals] + + +def window_df(df: pd.DataFrame, start_t: float, end_t: float) -> pd.DataFrame: + if df is None or df.empty or "_rel_time_s" not in df.columns: + return pd.DataFrame() + t = pd.to_numeric(df["_rel_time_s"], errors="coerce") + return df[(t >= start_t) & (t <= end_t)] + + +def max_in_window(df: pd.DataFrame, col: str) -> float: + if df.empty or col not in df.columns: + return np.nan + vals = pd.to_numeric(df[col], errors="coerce") + return float(vals.max()) if vals.notna().any() else np.nan + + +def max_diag_in_window(df: pd.DataFrame, col: str) -> float: + if df.empty or col not in df.columns: + return np.nan + vals = diagnostic_series(df, col) + return float(vals.max()) if vals.notna().any() else np.nan + + +def median_diag(df: pd.DataFrame, col: str) -> float: + if df.empty or col not in df.columns: + return np.nan + vals = diagnostic_series(df, col) + return float(vals.median()) if vals.notna().any() else np.nan + + +def min_in_window(df: pd.DataFrame, col: str) -> float: + if df.empty or col not in df.columns: + return np.nan + vals = pd.to_numeric(df[col], errors="coerce") + return float(vals.min()) if vals.notna().any() else np.nan + + +def changed_in_window(df: pd.DataFrame, col: str) -> bool: + if df.empty or col not in df.columns: + return False + vals = df[col].dropna().astype(str).unique() + return len(vals) > 1 + + +def diagnose_interval(interval: dict, tracking_df: pd.DataFrame, + lm_df: pd.DataFrame, loop_df: pd.DataFrame, + before_window: float = 1.0, + after_window: float = 0.5) -> dict: + start = interval["start_time"] - before_window + end = interval["end_time"] + after_window + tw = window_df(tracking_df, start, end) + lw = window_df(lm_df, start, end) + cw = window_df(loop_df, start, end) + + reasons = [] + min_inliers = min_in_window(tw, "final_inliers") + global_median_inliers = pd.to_numeric(tracking_df.get("final_inliers", pd.Series(dtype=float)), errors="coerce").median() + state_col = "當前幀狀態" if "當前幀狀態" in tracking_df.columns else ("当前帧状态" if "当前帧状态" in tracking_df.columns else "tracking_state") + state_bad = False + if state_col in tw.columns: + state_bad = tw[state_col].astype(str).isin(["LOST", "RECENTLY_LOST"]).any() + + visual_max = max_in_window(tw, "visual_chi2_max") + visual_global = pd.to_numeric(tracking_df.get("visual_chi2_max", pd.Series(dtype=float)), errors="coerce").median() + imu_max = max_in_window(tw, "imu_chi2_max") + imu_global = pd.to_numeric(tracking_df.get("imu_chi2_max", pd.Series(dtype=float)), errors="coerce").median() + pose_update_max = max_diag_in_window(tw, "pose_update_norm") + velocity_update_max = max_diag_in_window(tw, "velocity_update_norm") + bias_update_max = max_diag_in_window(tw, "bias_update_norm") + + if state_bad or (not np.isnan(min_inliers) and not np.isnan(global_median_inliers) and min_inliers < 0.6 * global_median_inliers): + reasons.append("visual tracking quality dropped") + if not np.isnan(visual_max) and not np.isnan(visual_global) and visual_global > 0 and visual_max > 2.0 * visual_global: + reasons.append("visual residual increased") + if not np.isnan(imu_max) and not np.isnan(imu_global) and imu_global > 0 and imu_max > 2.0 * imu_global: + reasons.append("IMU residual increased") + if changed_in_window(tw, "tracking_method"): + reasons.append("tracking method switched") + + ba_events = [] + imu_init_nearby = False + if not lw.empty: + if "event_type" in lw.columns: + ba_events = lw["event_type"].dropna().astype(str).unique().tolist() + elif "ba_type" in lw.columns: + ba_events = lw["ba_type"].dropna().astype(str).unique().tolist() + if ba_events: + reasons.append("LocalMapping BA event nearby") + imu_init_nearby = any("IMU_INITIALIZATION" in ev or "InertialOptimizationInit" in ev for ev in ba_events) + if not imu_init_nearby and "ba_type" in lw.columns: + imu_init_nearby = lw["ba_type"].dropna().astype(str).str.contains("InertialOptimizationInit", regex=False).any() + if imu_init_nearby: + reasons.append("IMU initialization/refinement event nearby") + if max_in_window(lw, "imu_chi2_max") > 0: + reasons.append("LocalMapping inertial residual available") + + lm_visual_before = max_diag_in_window(lw, "visual_chi2_before") + lm_visual_after = max_diag_in_window(lw, "visual_chi2_after") + lm_imu_before = max_diag_in_window(lw, "imu_chi2_before") + lm_imu_after = max_diag_in_window(lw, "imu_chi2_after") + lm_bias_delta = max_diag_in_window(lw, "bias_delta_norm") + lm_velocity_delta = max_diag_in_window(lw, "velocity_delta_norm") + lm_pose_trans = max_diag_in_window(lw, "pose_correction_trans_norm") + lm_pose_rot = max_diag_in_window(lw, "pose_correction_rot_deg") + lm_init_scale = max_diag_in_window(lw, "init_scale") + lm_init_bg_norm = max_diag_in_window(lw, "init_bg_norm") + lm_init_ba_norm = max_diag_in_window(lw, "init_ba_norm") + lm_pose_trans_global = median_diag(lm_df, "pose_correction_trans_norm") + lm_bias_delta_global = median_diag(lm_df, "bias_delta_norm") + + if not np.isnan(lm_pose_trans) and lm_pose_trans > max(0.02, 3.0 * lm_pose_trans_global if not np.isnan(lm_pose_trans_global) else 0.0): + reasons.append("LocalMapping pose correction was unusually large") + if not np.isnan(lm_bias_delta) and lm_bias_delta > max(1e-4, 3.0 * lm_bias_delta_global if not np.isnan(lm_bias_delta_global) else 0.0): + reasons.append("LocalMapping bias correction was unusually large") + + loop_events = [] + if not cw.empty: + if "event_type" in cw.columns: + loop_events = cw["event_type"].dropna().astype(str).unique().tolist() + if loop_events: + reasons.append("LoopClosing / merge / GBA event nearby") + loop_sim3_trans = max_diag_in_window(cw, "sim3_correction_trans_norm") + loop_sim3_rot = max_diag_in_window(cw, "sim3_correction_rot_deg") + loop_time = max_diag_in_window(cw, "loop_time_ms") + merge_time = max_diag_in_window(cw, "merge_time_ms") + gba_time = max_diag_in_window(cw, "gba_time_ms") + loop_sim3_trans_global = median_diag(loop_df, "sim3_correction_trans_norm") + if not np.isnan(loop_sim3_trans) and loop_sim3_trans > max(0.02, 3.0 * loop_sim3_trans_global if not np.isnan(loop_sim3_trans_global) else 0.0): + reasons.append("LoopClosing/MapMerge Sim3 correction was unusually large") + + if not reasons: + reasons.append("internal logs normal or unavailable; check timestamp/export/evaluation") + + out = dict(interval) + out.pop("_score", None) + out.update({ + "window_start": float(start), + "window_end": float(end), + "min_final_inliers": min_inliers, + "max_visual_chi2": visual_max, + "max_imu_chi2": imu_max, + "max_pose_update_norm": pose_update_max, + "max_velocity_update_norm": velocity_update_max, + "max_bias_update_norm": bias_update_max, + "tracking_method_changed": changed_in_window(tw, "tracking_method"), + "bad_tracking_state": bool(state_bad), + "localmapping_events": ";".join(ba_events), + "imu_initialization_nearby": bool(imu_init_nearby), + "max_lm_visual_chi2_before": lm_visual_before, + "max_lm_visual_chi2_after": lm_visual_after, + "max_lm_imu_chi2_before": lm_imu_before, + "max_lm_imu_chi2_after": lm_imu_after, + "max_lm_bias_delta_norm": lm_bias_delta, + "max_lm_velocity_delta_norm": lm_velocity_delta, + "max_lm_pose_correction_trans_norm": lm_pose_trans, + "max_lm_pose_correction_rot_deg": lm_pose_rot, + "max_lm_init_scale": lm_init_scale, + "max_lm_init_bg_norm": lm_init_bg_norm, + "max_lm_init_ba_norm": lm_init_ba_norm, + "loopclosing_events": ";".join(loop_events), + "max_loop_sim3_correction_trans_norm": loop_sim3_trans, + "max_loop_sim3_correction_rot_deg": loop_sim3_rot, + "max_loop_time_ms": loop_time, + "max_merge_time_ms": merge_time, + "max_gba_time_ms": gba_time, + "diagnosis": "; ".join(reasons), + }) + return out + + +def event_values(df: pd.DataFrame) -> pd.Series: + if df is None or df.empty: + return pd.Series(dtype=str) + if "event_type" in df.columns: + return df["event_type"].fillna("").astype(str) + if "ba_type" in df.columns: + return df["ba_type"].fillna("").astype(str) + return pd.Series([""] * len(df), index=df.index, dtype=str) + + +def draw_event_markers(axes, df: pd.DataFrame, color_by_event: dict[str, str], + alpha: float = 0.75) -> None: + """Draw backend events as short bottom rug marks instead of full-height bars.""" + if df is None or df.empty or "_rel_time_s" not in df.columns: + return + events = event_values(df) + times = pd.to_numeric(df["_rel_time_s"], errors="coerce") + for t_i, ev in zip(times, events): + if not np.isfinite(t_i): + continue + color = color_by_event.get(ev) + if color is None: + continue + for ax in axes: + ax.vlines(float(t_i), 0.015, 0.09, transform=ax.get_xaxis_transform(), + color=color, linewidth=1.0, alpha=alpha) + + +def shade_intervals(axes, intervals: list[dict]) -> None: + for interval in intervals: + s = interval["start_time"] + e = interval["end_time"] + for ax in axes: + ax.axvspan(s, e, color=HIGH_ERROR_COLOR, alpha=0.13, label="_nolegend_") + + +def shade_tracking_states(axes, times: np.ndarray, states: np.ndarray) -> None: + if len(times) == 0 or len(states) == 0: + return + + active_state = None + active_start = None + last_t = None + style = { + "RECENTLY_LOST": (RECENTLY_LOST_COLOR, 0.14), + "LOST": (LOST_COLOR, 0.18), + } + + for t_i, state in zip(times, states): + if not np.isfinite(t_i): + continue + state = str(state) + state = state if state in style else None + if state != active_state: + if active_state is not None and active_start is not None: + color, alpha = style[active_state] + for ax in axes: + ax.axvspan(active_start, t_i, color=color, alpha=alpha, label="_nolegend_") + active_state = state + active_start = float(t_i) if state is not None else None + last_t = float(t_i) + + if active_state is not None and active_start is not None and last_t is not None: + color, alpha = style[active_state] + for ax in axes: + ax.axvspan(active_start, last_t, color=color, alpha=alpha, label="_nolegend_") + + +def align_localmapping_to_tracking(lm_df: pd.DataFrame, log_df: pd.DataFrame, + common_t0: float) -> pd.DataFrame: + """用 LocalMapping 的 track_frame_id 對到 Tracking frame_id，取得 dataset time 軸。""" + if lm_df.empty or "track_frame_id" not in lm_df.columns or "frame_id" not in log_df.columns: + return pd.DataFrame() + + frames = log_df[["frame_id", "timestamp"]].copy() + frames["frame_id"] = pd.to_numeric(frames["frame_id"], errors="coerce") + frames = frames.dropna(subset=["frame_id"]) + frames["frame_id"] = frames["frame_id"].astype(int) + + lm = lm_df.copy() + lm["track_frame_id"] = pd.to_numeric(lm["track_frame_id"], errors="coerce") + lm = lm.dropna(subset=["track_frame_id"]) + lm["track_frame_id"] = lm["track_frame_id"].astype(int) + + aligned = lm.merge(frames, left_on="track_frame_id", right_on="frame_id", + how="left", suffixes=("", "_tracking")) + aligned = aligned.dropna(subset=["timestamp"]) + aligned["rel_time_s"] = aligned["timestamp"] - common_t0 + return aligned + + +def print_residual_data_report(log_df: pd.DataFrame, lm_df: pd.DataFrame | None, + log_imu_res_cols: list[str], + lm_imu_res_cols: list[str]) -> None: + print("\n[Residual data check]") + if log_imu_res_cols: + print(f" Tracking log IMU residual columns: {', '.join(log_imu_res_cols)}") + else: + imu_related = columns_containing(log_df, ["imu", "inertial", "preint", "bias"]) + print(" Tracking log has no IMU residual / chi2 columns.") + print(f" Tracking log IMU-related columns: {', '.join(imu_related) if imu_related else 'none'}") + + if lm_df is None: + print(" LocalMapping log not found.") + elif lm_imu_res_cols: + print(f" LocalMapping log IMU residual columns: {', '.join(lm_imu_res_cols)}") + else: + reproj_cols = [c for c in ["mean_reprojection_error", "max_reprojection_error"] if c in lm_df.columns] + print(" LocalMapping log has no IMU residual / chi2 columns.") + print(f" LocalMapping available residual-like columns: {', '.join(reproj_cols) if reproj_cols else 'none'}") + + if not log_imu_res_cols and not lm_imu_res_cols: + print(" Most likely cause: current CSVs do not record EdgeInertial/EdgeInertialGS residuals.") + print(" tracking_log.csv can show IMU state, bias norm, timing, and optimizer branch,") + print(" but true inertial residual needs extra C++ logging inside Optimizer/IMU edges.") + + +# ───────────────────────────────────────────── +# 主程式 +# ───────────────────────────────────────────── + +def main(): + parser = argparse.ArgumentParser(description="軌跡診斷腳本") + parser.add_argument("--name", required=True, help="output_name") + parser.add_argument("--gt", required=True, help="GT 檔案路徑") + parser.add_argument("--gt-format", choices=["euroc", "tum"], default="euroc") + parser.add_argument("--traj", default=None, help="估計軌跡路徑（可選）") + parser.add_argument("--log", default=None, help="tracking log 路徑（可選）") + parser.add_argument("--local-log", default=None, help="LocalMapping log 路徑（可選，自動搜尋）") + parser.add_argument("--loop-log", default=None, help="LoopClosing log 路徑（可選，自動搜尋）") + parser.add_argument("--output", default=None, help="輸出圖片檔名") + parser.add_argument("--interval-output", default=None, help="高誤差區段 CSV 輸出路徑") + parser.add_argument("--error-percentile", type=float, default=90.0, + help="ATE 高誤差區段的 percentile 門檻") + parser.add_argument("--max-intervals", type=int, default=8, + help="最多輸出的高誤差區段數量") + parser.add_argument("--before-window", type=float, default=1.0, + help="回查每段誤差區間前方幾秒") + parser.add_argument("--after-window", type=float, default=0.5, + help="回查每段誤差區間後方幾秒") + parser.add_argument("--no-align", action="store_true", help="關閉 SE3 對齊") + args = parser.parse_args() + + name = args.name + traj_path = Path(args.traj) if args.traj else SCRIPT_DIR / f"fix_{name}.txt" + log_path = resolve_tracking_log(name, args.log) + lm_path = resolve_localmapping_log(name, args.local_log) + loop_path = resolve_loopclosing_log(name, args.loop_log) + out_path = Path(args.output) if args.output else SCRIPT_DIR / f"diagnosis_{name}.pdf" + interval_out_path = Path(args.interval_output) if args.interval_output else \ + out_path.with_name(f"{out_path.stem}_intervals.csv") + gt_path = Path(args.gt) + + # ── 載入資料 ── + for p, label in [(traj_path, "估計軌跡"), (gt_path, "GT"), (log_path, "Tracking log")]: + if not p.exists(): + print(f"[ERROR] 找不到 {label}: {p}", file=sys.stderr) + sys.exit(1) + + print(f"Loading trajectory: {traj_path}") + est = load_traj(traj_path) + + print(f"Loading GT: {gt_path}") + gt = load_gt_euroc(gt_path) if args.gt_format == "euroc" else load_gt_tum(gt_path) + + print(f"Loading tracking log: {log_path}") + log_df = coerce_numeric_when_possible(pd.read_csv(log_path)) + + lm_df = None + if lm_path is not None: + print(f"Loading LocalMapping log: {lm_path}") + lm_df = load_localmapping_log(lm_path) + else: + print("LocalMapping log: not found (optional)") + + loop_df = None + if loop_path is not None: + print(f"Loading LoopClosing log: {loop_path}") + loop_df = load_loopclosing_log(loop_path) + else: + print("LoopClosing log: not found (optional)") + + print(f"Est frames: {len(est)}, GT frames: {len(gt)}, Log frames: {len(log_df)}") + if lm_df is not None: + print(f"LocalMapping rows: {len(lm_df)}") + if loop_df is not None: + print(f"LoopClosing rows: {len(loop_df)}") + + # ── 時間戳對齊：估計軌跡 ↔ GT ── + ts_arr, est_pos, gt_pos = match_timestamps(est, gt) + if len(ts_arr) == 0: + print("[ERROR] 沒有找到任何對齊的幀，請檢查時間戳格式", file=sys.stderr) + sys.exit(1) + print(f"Matched frames: {len(ts_arr)}") + + # ── SE3 對齊 ── + if not args.no_align: + R, t = align_se3(est_pos, gt_pos) + est_pos_aligned = (R @ est_pos.T).T + t + else: + est_pos_aligned = est_pos + + # ── 每幀位置誤差 ── + errors = np.linalg.norm(est_pos_aligned - gt_pos, axis=1) + rel_ts = ts_arr - ts_arr[0] + rpe = compute_rpe(est_pos_aligned, gt_pos) + error_derivative = compute_error_derivative(rel_ts, errors) + + # ── 對齊 tracking log（用軌跡的 t0 當共同參考點）── + common_t0 = ts_arr[0] # 軌跡第一幀的絕對時間戳 + tracking_diag_df = add_relative_time(log_df, common_t0) + log_rel = pd.to_numeric(tracking_diag_df["_rel_time_s"], errors="coerce").values + + lm_aligned = add_relative_time(lm_df, common_t0) if lm_df is not None else pd.DataFrame() + if lm_df is not None and (lm_aligned.empty or lm_aligned["_rel_time_s"].isna().all()): + lm_aligned = align_localmapping_to_tracking(lm_df, log_df, common_t0) + if not lm_aligned.empty: + lm_aligned["_rel_time_s"] = pd.to_numeric(lm_aligned["rel_time_s"], errors="coerce") + loop_aligned = add_relative_time(loop_df, common_t0) if loop_df is not None else pd.DataFrame() + + log_imu_res_cols = find_inertial_residual_columns(log_df) + lm_imu_res_cols = find_inertial_residual_columns(lm_df) if lm_df is not None else [] + print_residual_data_report(log_df, lm_df, log_imu_res_cols, lm_imu_res_cols) + + high_error_intervals = find_high_error_intervals( + rel_ts, errors, rpe, + percentile=args.error_percentile, + max_intervals=args.max_intervals, + ) + for interval in high_error_intervals: + mask = (rel_ts >= interval["start_time"]) & (rel_ts <= interval["end_time"]) + interval["max_error_derivative"] = float(np.max(error_derivative[mask])) if mask.any() else np.nan + diagnosis_rows = [ + diagnose_interval(interval, tracking_diag_df, lm_aligned, loop_aligned, + before_window=args.before_window, + after_window=args.after_window) + for interval in high_error_intervals + ] + intervals_df = pd.DataFrame(diagnosis_rows) + if not intervals_df.empty: + intervals_df.to_csv(interval_out_path, index=False) + print(f"\nHigh-error intervals saved to: {interval_out_path}") + print("\n[High-error interval summary]") + for i, row in intervals_df.iterrows(): + print(f" #{i+1}: {row['start_time']:.2f}s-{row['end_time']:.2f}s, " + f"peak ATE={row['peak_ATE']:.4f}m at {row['peak_time']:.2f}s, " + f"max RPE={row['max_RPE']:.4f}m") + print(" backend: " + f"LM pose={row.get('max_lm_pose_correction_trans_norm', np.nan):.4g}m/" + f"{row.get('max_lm_pose_correction_rot_deg', np.nan):.4g}deg, " + f"LM bias={row.get('max_lm_bias_delta_norm', np.nan):.4g}, " + f"init scale={row.get('max_lm_init_scale', np.nan):.4g}, " + f"Loop Sim3={row.get('max_loop_sim3_correction_trans_norm', np.nan):.4g}m/" + f"{row.get('max_loop_sim3_correction_rot_deg', np.nan):.4g}deg") + print(f" diagnosis: {row['diagnosis']}") + else: + print("\nHigh-error intervals: none found") + + # ── 軌跡斷點：tracking log 中狀態標記 ── + state_col_name = first_existing_column(log_df, ["當前幀狀態", "当前帧状态", "tracking_state"]) + if state_col_name: + state_values = log_df[state_col_name].astype(str).values + lost_mask = log_df[state_col_name].isin(["LOST", "RECENTLY_LOST"]).values + else: + state_values = np.array([""] * len(log_df), dtype=str) + lost_mask = np.zeros(len(log_df), dtype=bool) + if "reloc_attempted" in log_df.columns: + reloc_mask = log_df["reloc_attempted"].astype(str).str.lower().isin(["yes", "true", "1"]).values + else: + reloc_mask = np.zeros(len(log_df), dtype=bool) + + # tracking_method 顏色對應 + method_map = {"none": 0, "RefKF": 1, "MM": 2, "IMU_MM": 3, "Reloc": 4} + if "tracking_method" in log_df.columns: + method_vals = log_df["tracking_method"].map(lambda x: method_map.get(str(x), -1)).values + else: + method_vals = np.full(len(log_df), -1) + + # ── 繪圖：拆成兩頁，避免每個子圖的 x 軸說明互相擠在一起 ── + fig1, axes_page1 = plt.subplots(3, 1, figsize=(16, 11.5), sharex=False) + fig2, axes_page2 = plt.subplots(3, 1, figsize=(16, 11.5), sharex=False) + fig1.suptitle(f"Trajectory Diagnosis: {name} | Page 1/2: Tracking and Error", + fontsize=13, fontweight="bold") + fig2.suptitle(f"Trajectory Diagnosis: {name} | Page 2/2: Residuals and Backend Events", + fontsize=13, fontweight="bold") + x_axis_note = ( + "X axis is relative dataset time: timestamp - first matched trajectory timestamp. " + "Using time lets trajectory error and Tracking/LocalMapping/LoopClosing logs be aligned." + ) + fig1.text(0.5, 0.935, x_axis_note, ha="center", va="center", fontsize=8) + fig2.text(0.5, 0.925, x_axis_note, ha="center", va="center", fontsize=8) + axes = list(axes_page1) + list(axes_page2) + + # 子圖 1：每幀位置誤差 + ax1 = axes[0] + ax1.plot(rel_ts, errors, color="royalblue", linewidth=0.8, label="ATE (m)") + ax1.plot(rel_ts, rpe, color="mediumpurple", linewidth=0.7, alpha=0.75, label="RPE-like drift (m)") + ax1.set_ylabel("ATE / RPE (m)") + ax1.set_title("Per-frame ATE / RPE (after alignment)") + ax1.grid(True, alpha=0.3) + # 標記 RMSE + rmse = float(np.sqrt(np.mean(errors**2))) + ax1.axhline(rmse, color="red", linestyle="--", linewidth=0.8, + label=f"RMSE={rmse:.4f}m") + ax1_deriv = ax1.twinx() + ax1_deriv.plot(rel_ts, error_derivative, color=DATE_COLOR, linewidth=0.7, + alpha=0.7, label="dATE/dt") + ax1_deriv.set_ylabel("dATE/dt") + ax1_deriv.tick_params(axis="y", labelsize=7) + lines1, labels1 = ax1.get_legend_handles_labels() + lines2, labels2 = ax1_deriv.get_legend_handles_labels() + ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper right", fontsize=8) + + # 子圖 2：final_inliers vs matches_before_tlm_opt + ax2 = axes[1] + if "matches_before_tlm_opt" in log_df.columns: + ax2.plot(log_rel, numeric_series(log_df, "matches_before_tlm_opt").values, + color="orange", linewidth=0.8, linestyle="--", label="before_opt") + if "final_inliers" in log_df.columns: + ax2.plot(log_rel, numeric_series(log_df, "final_inliers").values, + color="darkgreen", linewidth=0.8, label="final_inliers") + ax2.set_ylabel("Inliers") + ax2.set_title("Inliers: before opt (orange--) vs after opt (green) | large gap = unstable pose opt") + ax2.legend(loc="upper right", fontsize=8) + ax2.grid(True, alpha=0.3) + + # Tracking state background: pale orange=RECENTLY_LOST, pale red=LOST. + shade_tracking_states([ax1, ax2], log_rel, state_values) + + # Reloc 事件（紅色垂直線） + for t_i in log_rel[reloc_mask]: + ax2.axvline(t_i, color=RELOC_COLOR, linewidth=0.7, alpha=0.7) + + # 子圖 3：tracking_method — 用 step 折線顯示，非 MM 一目了然 + ax3 = axes[2] + ax3.step(log_rel, method_vals, where="mid", color="royalblue", linewidth=0.8) + ax3.set_yticks(list(method_map.values())) + ax3.set_yticklabels(list(method_map.keys()), fontsize=8) + ax3.set_ylim(-0.5, max(method_map.values()) + 0.5) + + # RECENTLY_LOST / LOST 狀態背景 + if state_col_name: + rl_mask = log_df[state_col_name].astype(str) == "RECENTLY_LOST" + lo_mask = log_df[state_col_name].astype(str) == "LOST" + in_rl = False; in_lo = False; rl_s = lo_s = None + for t_i, is_rl, is_lo in zip(log_rel, rl_mask.values, lo_mask.values): + if is_rl and not in_rl: + rl_s = t_i; in_rl = True + elif not is_rl and in_rl: + ax3.axvspan(rl_s, t_i, alpha=0.18, color=RECENTLY_LOST_COLOR); in_rl = False + if is_lo and not in_lo: + lo_s = t_i; in_lo = True + elif not is_lo and in_lo: + ax3.axvspan(lo_s, t_i, alpha=0.22, color=LOST_COLOR); in_lo = False + if in_rl and rl_s is not None: + ax3.axvspan(rl_s, np.nanmax(log_rel), alpha=0.18, color=RECENTLY_LOST_COLOR) + if in_lo and lo_s is not None: + ax3.axvspan(lo_s, np.nanmax(log_rel), alpha=0.22, color=LOST_COLOR) + + ax3.set_ylabel("Tracking Method") + ax3.set_title("Tracking Method (step) | orange=RECENTLY_LOST red=LOST") + ax3.legend(handles=[ + mpatches.Patch(color=RECENTLY_LOST_COLOR, alpha=0.25, label="RECENTLY_LOST"), + mpatches.Patch(color=LOST_COLOR, alpha=0.28, label="LOST"), + ], loc="upper right", fontsize=7) + ax3.grid(True, alpha=0.3) + + # 子圖 4：慣性 residual；若目前 CSV 沒記，明確畫出可用替代資訊 + ax4 = axes[3] + if log_imu_res_cols: + plotted_residual = False + for col in log_imu_res_cols: + vals = diagnostic_series(log_df, col) + if vals.notna().any(): + ax4.plot(log_rel, vals.values, linewidth=0.8, label=col) + plotted_residual = True + ax4.set_ylabel("IMU residual") + if plotted_residual: + ax4.set_title("Recorded IMU/Inertial Residuals from Tracking Log") + ax4.legend(loc="upper right", fontsize=8) + else: + ax4.set_title("IMU Residual: columns exist, but values are not collected") + ax4.text(0.5, 0.5, "IMU residual columns are present, but all values are -1/NaN.", + ha="center", va="center", transform=ax4.transAxes, fontsize=10) + elif lm_imu_res_cols and not lm_aligned.empty: + plotted_residual = False + for col in lm_imu_res_cols: + vals = diagnostic_series(lm_aligned, col) + if vals.notna().any(): + ax4.plot(lm_aligned["_rel_time_s"].values, vals.values, + linewidth=0.8, marker=".", markersize=2, label=col) + plotted_residual = True + ax4.set_ylabel("IMU residual") + if plotted_residual: + ax4.set_title("Recorded IMU/Inertial Residuals from LocalMapping Log") + ax4.legend(loc="upper right", fontsize=8) + else: + ax4.set_title("IMU Residual: LocalMapping values are not collected") + ax4.text(0.5, 0.5, "LocalMapping IMU residual columns are present, but all values are -1/NaN.", + ha="center", va="center", transform=ax4.transAxes, fontsize=10) + elif not lm_aligned.empty and {"mean_reprojection_error", "max_reprojection_error"}.issubset(lm_aligned.columns): + ax4.plot(lm_aligned["_rel_time_s"].values, numeric_series(lm_aligned, "mean_reprojection_error").values, + color="purple", linewidth=0.8, marker=".", markersize=2, label="mean_reprojection_error") + ax4.plot(lm_aligned["_rel_time_s"].values, numeric_series(lm_aligned, "max_reprojection_error").values, + color="brown", linewidth=0.6, alpha=0.55, label="max_reprojection_error") + ax4.set_ylabel("Reprojection error") + ax4.set_title("No true IMU residual in current CSVs; showing LocalMapping reprojection error proxy") + ax4.text(0.01, 0.92, + "True inertial residual requires logging EdgeInertial/EdgeInertialGS chi2/residual in C++.", + transform=ax4.transAxes, fontsize=8, va="top", + bbox=dict(facecolor="white", alpha=0.75, edgecolor="none")) + ax4.legend(loc="upper right", fontsize=8) + else: + ax4.text(0.5, 0.5, + "No IMU residual columns found in tracking/localmapping logs.\n" + "Current tracking_log.csv only records IMU state, bias norm, timing, and optimizer branch.\n" + "To plot true inertial residual, add C++ logging around EdgeInertial / EdgeInertialGS chi2.", + ha="center", va="center", transform=ax4.transAxes, fontsize=10) + ax4.set_ylabel("IMU residual") + ax4.set_title("IMU Residual: data not recorded") + ax4.grid(True, alpha=0.3) + + # 子圖 5：bias norm（VI 才有意義，純視覺全為 0） + ax5 = axes[4] + if "bias_acc_norm" in log_df.columns: + ax5.plot(log_rel, numeric_series(log_df, "bias_acc_norm").values, + color="tomato", linewidth=0.8, label="bias_acc_norm") + if "bias_gyro_norm" in log_df.columns: + ax5.plot(log_rel, numeric_series(log_df, "bias_gyro_norm").values, + color="steelblue", linewidth=0.8, label="bias_gyro_norm") + if "bias_update_norm" in log_df.columns: + vals = diagnostic_series(log_df, "bias_update_norm") + if vals.notna().any(): + ax5.plot(log_rel, vals.values, + color="#111827", linewidth=0.65, alpha=0.75, label="bias_update_norm") + if "bias_delta_norm" in lm_aligned.columns: + vals = diagnostic_series(lm_aligned, "bias_delta_norm") + if vals.notna().any(): + ax5.plot(lm_aligned["_rel_time_s"].values, vals.values, + color=LOCAL_BA_COLOR, linewidth=0.0, marker=".", markersize=3, + label="LM bias_delta_norm") + ax5.set_ylabel("Bias norm") + ax5.set_xlabel(X_AXIS_LABEL) + ax5.set_title("IMU Bias Norm (always 0 in pure visual mode)") + ax5.legend(loc="upper right", fontsize=8) + ax5.grid(True, alpha=0.3) + + # 子圖 6：後端修正量。這些值只描述 BA / loop correction 前後差異，不代表又跑了一次優化。 + ax6 = axes[5] + plotted_backend = False + if not lm_aligned.empty and "pose_correction_trans_norm" in lm_aligned.columns: + vals = diagnostic_series(lm_aligned, "pose_correction_trans_norm") + if vals.notna().any(): + ax6.plot(lm_aligned["_rel_time_s"].values, vals.values, + color=LOCAL_BA_COLOR, linewidth=0.0, marker=".", markersize=3, + label="LM pose correction trans (m)") + plotted_backend = True + if not lm_aligned.empty and "velocity_delta_norm" in lm_aligned.columns: + vals = diagnostic_series(lm_aligned, "velocity_delta_norm") + if vals.notna().any(): + ax6.plot(lm_aligned["_rel_time_s"].values, vals.values, + color="#0891b2", linewidth=0.0, marker=".", markersize=2.5, + label="LM velocity delta norm") + plotted_backend = True + if not loop_aligned.empty and "sim3_correction_trans_norm" in loop_aligned.columns: + vals = diagnostic_series(loop_aligned, "sim3_correction_trans_norm") + if vals.notna().any(): + ax6.plot(loop_aligned["_rel_time_s"].values, vals.values, + color=LOOP_EVENT_COLOR, linewidth=0.0, marker="x", markersize=4, + label="Loop Sim3 trans (m)") + plotted_backend = True + if not lm_aligned.empty and "init_scale" in lm_aligned.columns: + vals = diagnostic_series(lm_aligned, "init_scale") + if vals.notna().any(): + ax6.plot(lm_aligned["_rel_time_s"].values, vals.values, + color="#16a34a", linewidth=0.0, marker="D", markersize=3.5, + label="IMU init scale") + plotted_backend = True + + ax6_rot = ax6.twinx() + plotted_rot = False + if not lm_aligned.empty and "pose_correction_rot_deg" in lm_aligned.columns: + vals = diagnostic_series(lm_aligned, "pose_correction_rot_deg") + if vals.notna().any(): + ax6_rot.plot(lm_aligned["_rel_time_s"].values, vals.values, + color="#4338ca", linewidth=0.0, marker=".", markersize=2.5, + label="LM pose correction rot (deg)") + plotted_rot = True + if not loop_aligned.empty and "sim3_correction_rot_deg" in loop_aligned.columns: + vals = diagnostic_series(loop_aligned, "sim3_correction_rot_deg") + if vals.notna().any(): + ax6_rot.plot(loop_aligned["_rel_time_s"].values, vals.values, + color=DATE_COLOR, linewidth=0.0, marker="^", markersize=3.5, + label="Loop Sim3 rot (deg)") + plotted_rot = True + + ax6.set_ylabel("Translation / norm") + ax6_rot.set_ylabel("Rotation (deg)") + ax6.set_title("Backend Correction Magnitude | large spikes may explain trajectory jumps") + if plotted_backend or plotted_rot: + lines_l, labels_l = ax6.get_legend_handles_labels() + lines_r, labels_r = ax6_rot.get_legend_handles_labels() + ax6.legend(lines_l + lines_r, labels_l + labels_r, loc="upper right", fontsize=8) + else: + ax6.text(0.5, 0.5, + "Backend correction columns are missing or all -1/NaN in current logs.", + ha="center", va="center", transform=ax6.transAxes, fontsize=10) + ax6.grid(True, alpha=0.3) + + # ── 高誤差區段與後端事件標記 ── + shade_intervals(axes, high_error_intervals) + draw_event_markers(axes_page2, lm_aligned, { + "LOCAL_BA": LOCAL_BA_COLOR, + "LOCAL_INERTIAL_BA": LOCAL_BA_COLOR, + "IMU_INITIALIZATION": "#111827", + "InertialOptimizationInit": "#111827", + }) + draw_event_markers(axes_page2, loop_aligned, { + "LOOP_DETECTED": LOOP_EVENT_COLOR, + "LOOP_CLOSED": LOOP_EVENT_COLOR, + "LOOP_CLOSURE": LOOP_EVENT_COLOR, + "MAP_MERGE": "saddlebrown", + "GBA_STARTED": "teal", + "GBA_FINISHED": "darkcyan", + }) + + # ── 對齊 x 軸範圍 ── + t_max_log = float(np.nanmax(log_rel)) if len(log_rel) and np.isfinite(log_rel).any() else 0.0 + t_max_err = float(rel_ts[-1]) + t_max_lm = float(lm_aligned["_rel_time_s"].max()) if not lm_aligned.empty else 0.0 + t_max_loop = float(loop_aligned["_rel_time_s"].max()) if not loop_aligned.empty else 0.0 + for ax in axes: + ax.set_xlim(0, max(t_max_log, t_max_err, t_max_lm, t_max_loop)) + ax.set_xlabel(X_AXIS_LABEL) + + # ── 圖例補充 ── + recently_lost_patch = mpatches.Patch(color=RECENTLY_LOST_COLOR, alpha=0.25, + label="RECENTLY_LOST interval") + lost_patch = mpatches.Patch(color=LOST_COLOR, alpha=0.28, label="LOST interval") + reloc_line = Line2D([0], [0], color=RELOC_COLOR, lw=1.0, label="Reloc attempted") + high_err_patch = mpatches.Patch(color=HIGH_ERROR_COLOR, alpha=0.28, + label="High-error interval") + lm_marker = Line2D([0], [0], color=LOCAL_BA_COLOR, marker="|", markersize=12, + linestyle="None", label="LocalMapping BA rug") + lc_marker = Line2D([0], [0], color=LOOP_EVENT_COLOR, marker="|", markersize=12, + linestyle="None", label="LoopClosing / GBA rug") + legend_handles = [recently_lost_patch, lost_patch, reloc_line, + high_err_patch, lm_marker, lc_marker] + for fig_i in [fig1, fig2]: + fig_i.legend(handles=legend_handles, + loc="lower center", ncol=6, fontsize=8, framealpha=0.8) + fig_i.tight_layout(rect=[0, 0.06, 1, 0.91]) + + if out_path.suffix.lower() == ".pdf": + with PdfPages(out_path) as pdf: + pdf.savefig(fig1, dpi=150, bbox_inches="tight") + pdf.savefig(fig2, dpi=150, bbox_inches="tight") + print(f"\nDiagnosis PDF saved to: {out_path} (2 pages)") + else: + page2_path = out_path.with_name(f"{out_path.stem}_page2{out_path.suffix or '.png'}") + fig1.savefig(out_path, dpi=150, bbox_inches="tight") + fig2.savefig(page2_path, dpi=150, bbox_inches="tight") + print(f"\nDiagnosis plots saved to: {out_path} and {page2_path}") + plt.close(fig1) + plt.close(fig2) + if intervals_df.empty: + pd.DataFrame(columns=[ + "start_time", "end_time", "peak_time", "peak_ATE", "mean_ATE", + "max_RPE", "max_error_derivative", + "max_pose_update_norm", "max_velocity_update_norm", "max_bias_update_norm", + "max_lm_visual_chi2_before", "max_lm_visual_chi2_after", + "max_lm_imu_chi2_before", "max_lm_imu_chi2_after", + "max_lm_bias_delta_norm", "max_lm_velocity_delta_norm", + "max_lm_pose_correction_trans_norm", "max_lm_pose_correction_rot_deg", + "imu_initialization_nearby", "max_lm_init_scale", + "max_lm_init_bg_norm", "max_lm_init_ba_norm", + "max_loop_sim3_correction_trans_norm", "max_loop_sim3_correction_rot_deg", + "max_loop_time_ms", "max_merge_time_ms", "max_gba_time_ms", + "diagnosis" + ]).to_csv(interval_out_path, index=False) + print(f"High-error intervals saved to: {interval_out_path} (empty)") + print(f"RMSE = {rmse:.6f} m (matched frames: {len(ts_arr)})") + + +if __name__ == "__main__": + main() diff --git a/evaluation/associate.py b/evaluation/associate.py index 222b7c8c101..3c4dedb391b 100644 --- a/evaluation/associate.py +++ b/evaluation/associate.py @@ -85,8 +85,8 @@ def associate(first_list, second_list,offset,max_difference): matches -- list of matched tuples ((stamp1,data1),(stamp2,data2)) """ - first_keys = first_list.keys() - second_keys = second_list.keys() + first_keys = list(first_list.keys()) + second_keys = list(second_list.keys()) potential_matches = [(abs(a - (b + offset)), a, b) for a in first_keys for b in second_keys @@ -122,9 +122,9 @@ def associate(first_list, second_list,offset,max_difference): if args.first_only: for a,b in matches: - print("%f %s"%(a," ".join(first_list[a]))) + print(("%f %s"%(a," ".join(first_list[a])))) else: for a,b in matches: - print("%f %s %f %s"%(a," ".join(first_list[a]),b-float(args.offset)," ".join(second_list[b]))) + print(("%f %s %f %s"%(a," ".join(first_list[a]),b-float(args.offset)," ".join(second_list[b])))) diff --git a/evaluation/ate_3d.py b/evaluation/ate_3d.py new file mode 100644 index 00000000000..24d60186471 --- /dev/null +++ b/evaluation/ate_3d.py @@ -0,0 +1,203 @@ +import matplotlib.pyplot as plt +import os +import copy +from matplotlib.backends.backend_pdf import PdfPages +from evo.tools import file_interface +from evo.core import sync # 新增：用來對齊時間戳 + +# --- 設定：請確保這兩個檔案跟這個 .py 檔案在同一個資料夾 --- +# 注意：EuRoC 的 GT 通常是這個 data.csv 檔案 +gt_file = '/home/lab405/henry/for_git/ORB_SLAM3/data/Euroc/V1_02_medium/mav0/state_groundtruth_estimate0/data.csv' +# 注意：ORB-SLAM3 跑完 EuRoC 預設產生的檔名通常是 CameraTrajectory.txt (TUM 格式) +pred_file = 'fix_stereo_euroc_v102.txt' + +# --- 輸出設定 --- +save_pdf = True +pred_stem = os.path.splitext(os.path.basename(pred_file))[0] +pdf_output = f"{pred_stem}.pdf" + + +# --- 1. 讀取數據 (加入錯誤檢查，防止檔案找不到) --- +if not os.path.exists(gt_file) or not os.path.exists(pred_file): + print(f"錯誤：找不到數據檔。請確保 '{gt_file}' 和 '{pred_file}' 都在這個資料夾裡。") + exit() + +print("正在讀取數據...") + +# 修改：使用 evo 讀取 EuRoC CSV 格式與 TUM 格式 +traj_ref = file_interface.read_euroc_csv_trajectory(gt_file) +traj_est = file_interface.read_tum_trajectory_file(pred_file) + +print("正在同步時間戳並進行 SE(3) 對齊...") + +# 新增：同步軌跡的時間戳 (將估計軌跡與 GT 對齊，允許最大時間差 0.01 秒) +max_diff = 0.01 +traj_ref, traj_est = sync.associate_trajectories(traj_ref, traj_est, max_diff) + +# 複製一份估計軌跡來進行對齊 +traj_est_aligned = copy.deepcopy(traj_est) + +# 執行 SE(3) 對齊 (如果你跑的是單目 Monocular，缺乏真實尺度，請把 correct_scale 改為 True) +traj_est_aligned.align(traj_ref, correct_scale=False) + +# 修改：提取 X 軸與 Y 軸數據 (EuRoC 俯視圖通常看 X 與 Y) +# evo 將資料整理為 Nx3 的矩陣，索引 0 是 X，索引 1 是 Y，索引 2 是 Z +gt_x = traj_ref.positions_xyz[:, 0] +gt_y = traj_ref.positions_xyz[:, 1] + +pred_x = traj_est_aligned.positions_xyz[:, 0] +pred_y = traj_est_aligned.positions_xyz[:, 1] + +# --- 2. 開始畫圖 --- +plt.style.use('seaborn-v0_8-white') +fig = plt.figure(figsize=(10, 8), facecolor='white') +ax = fig.add_subplot(111) + +print("正在畫圖") + +# 畫出真實軌跡 +ax.plot(gt_x, gt_y, color='black', linestyle='-', linewidth=2.5, label='Ground Truth') + +# 估計軌跡：用時間漸層著色（藍→紅，對應開始→結束） +import numpy as np +n = len(pred_x) +colors = plt.cm.rainbow(np.linspace(0, 1, max(n - 1, 1))) +for i in range(n - 1): + ax.plot(pred_x[i:i+2], pred_y[i:i+2], color=colors[i], linewidth=2.0) + +# 標記起點與終點 +ax.plot(pred_x[0], pred_y[0], 'go', markersize=8, label=f'Start t=0s') +ax.plot(pred_x[-1], pred_y[-1], 'rs', markersize=8, label=f'End') + +# 每隔固定幀數標一個時間戳（估計軌跡的 timestamp） +ts = traj_est_aligned.timestamps +t0 = ts[0] +interval = max(1, n // 10) # 最多標 10 個點 +for i in range(0, n, interval): + ax.annotate(f'{ts[i]-t0:.1f}s', (pred_x[i], pred_y[i]), + fontsize=7, color='dimgray', + xytext=(4, 4), textcoords='offset points') + +# colorbar 顯示時間進度 +sm = plt.cm.ScalarMappable(cmap='rainbow', + norm=plt.Normalize(vmin=0, vmax=ts[-1]-t0)) +sm.set_array([]) +plt.colorbar(sm, ax=ax, label='Elapsed time (s)', shrink=0.7) + +# --- 3. 美化與強制等比例 --- +ax.axis('equal') +ax.set_xlabel('x [m]', fontsize=14, fontweight='bold') +ax.set_ylabel('y [m]', fontsize=14, fontweight='bold') +ax.set_title('Stereo SLAM Trajectory Evaluation (SE3) — color = time', fontsize=14, fontweight='bold', pad=15) +ax.tick_params(axis='both', which='major', labelsize=12) +ax.legend(loc='upper right', fontsize=11, frameon=True, edgecolor='black') +ax.grid(False) + +# 三維立體軌跡圖 +# 1. 提取 Z 軸數據 (索引 2 代表 Z 軸) +gt_z = traj_ref.positions_xyz[:, 2] +pred_z = traj_est_aligned.positions_xyz[:, 2] + +# 2. 建立第二個畫布與 3D 座標軸 +fig_3d = plt.figure(figsize=(10, 8), facecolor='white') +ax_3d = fig_3d.add_subplot(111, projection='3d') + +# 3. 畫出 3D 軌跡線條 +ax_3d.plot(gt_x, gt_y, gt_z, color='black', linestyle='-', linewidth=2, label='Ground Truth') + +# 估計軌跡：時間漸層著色 +n3 = len(pred_x) +colors3 = plt.cm.rainbow(np.linspace(0, 1, max(n3 - 1, 1))) +for i in range(n3 - 1): + ax_3d.plot(pred_x[i:i+2], pred_y[i:i+2], pred_z[i:i+2], + color=colors3[i], linewidth=2.0) + +# 起點與終點標記 +ax_3d.scatter(pred_x[0], pred_y[0], pred_z[0], c='green', s=60, zorder=5, label='Start') +ax_3d.scatter(pred_x[-1], pred_y[-1], pred_z[-1], c='red', s=60, zorder=5, marker='s', label='End') + +# 時間標記 +interval3 = max(1, n3 // 10) +for i in range(0, n3, interval3): + ax_3d.text(pred_x[i], pred_y[i], pred_z[i], + f'{ts[i]-t0:.1f}s', fontsize=7, color='dimgray') + +# 4. 3D 圖表美化與標籤 +ax_3d.set_xlabel('x [m]', fontsize=12, fontweight='bold') +ax_3d.set_ylabel('y [m]', fontsize=12, fontweight='bold') +ax_3d.set_zlabel('z [m]', fontsize=12, fontweight='bold') +ax_3d.set_title('3D Trajectory (SE3) — color = time', fontsize=14, fontweight='bold', pad=15) +ax_3d.legend(loc='upper right', fontsize=11, frameon=True, edgecolor='black') + +# 5. 強制 3D 空間等比例 (避免某個軸被過度拉長，適用於 Matplotlib 3.3.0 以上) +try: + ax_3d.set_box_aspect((1, 1, 1)) +except AttributeError: + pass # 若 matplotlib 版本較舊則略過等比例設定 + +# 6. 互動式點擊顯示時間：在軌跡上疊加可點擊的 scatter +_pick_sc = ax_3d.scatter(pred_x, pred_y, pred_z, + c=np.linspace(0, 1, n3), cmap='rainbow', + s=8, alpha=0.0, picker=5) # alpha=0 不顯示，picker=5 點擊容差 + +_annot_text = ax_3d.text2D(0.02, 0.95, '', transform=ax_3d.transAxes, + fontsize=10, color='black', + bbox=dict(boxstyle='round,pad=0.3', fc='yellow', alpha=0.8)) + +def _on_pick(event): + if event.artist is not _pick_sc: + return + idx = event.ind[0] + t_sec = ts[idx] - t0 + _annot_text.set_text(f't = {t_sec:.2f}s (idx={idx})') + fig_3d.canvas.draw_idle() + +fig_3d.canvas.mpl_connect('pick_event', _on_pick) + +# XZ 平面與 YZ 平面軌跡圖 +# 確保已經提取 Z 軸數據 (如果前面 3D 圖已經有這兩行也沒關係，重複賦值不影響) +gt_z = traj_ref.positions_xyz[:, 2] +pred_z = traj_est_aligned.positions_xyz[:, 2] + +# 畫出 XZ 平面 (正視圖) +fig_xz = plt.figure(figsize=(10, 8), facecolor='white') +ax_xz = fig_xz.add_subplot(111) + +ax_xz.plot(gt_x, gt_z, color='black', linestyle='-', linewidth=2.5, label='Ground Truth') +ax_xz.plot(pred_x, pred_z, color='red', linestyle='-', linewidth=2.5, label='ORB-SLAM3') + +ax_xz.axis('equal') +ax_xz.set_xlabel('x [m]', fontsize=14, fontweight='bold') +ax_xz.set_ylabel('z [m]', fontsize=14, fontweight='bold') +ax_xz.set_title('Trajectory Evaluation - XZ Plane (Front View)', fontsize=16, fontweight='bold', pad=15) +ax_xz.tick_params(axis='both', which='major', labelsize=12) +ax_xz.legend(loc='upper right', fontsize=12, frameon=True, edgecolor='black') +ax_xz.grid(False) + +# 畫出 YZ 平面 (側視圖) +fig_yz = plt.figure(figsize=(10, 8), facecolor='white') +ax_yz = fig_yz.add_subplot(111) + +ax_yz.plot(gt_y, gt_z, color='black', linestyle='-', linewidth=2.5, label='Ground Truth') +ax_yz.plot(pred_y, pred_z, color='red', linestyle='-', linewidth=2.5, label='ORB-SLAM3') + +ax_yz.axis('equal') +ax_yz.set_xlabel('y [m]', fontsize=14, fontweight='bold') +ax_yz.set_ylabel('z [m]', fontsize=14, fontweight='bold') +ax_yz.set_title('Trajectory Evaluation - YZ Plane (Side View)', fontsize=16, fontweight='bold', pad=15) +ax_yz.tick_params(axis='both', which='major', labelsize=12) +ax_yz.legend(loc='upper right', fontsize=12, frameon=True, edgecolor='black') +ax_yz.grid(False) + + +# --- 4. 輸出 PDF --- +if save_pdf: + with PdfPages(pdf_output) as pdf: + pdf.savefig(fig, bbox_inches='tight') + pdf.savefig(fig_3d, bbox_inches='tight') + pdf.savefig(fig_xz, bbox_inches='tight') + pdf.savefig(fig_yz, bbox_inches='tight') + print(f"已儲存 PDF：{pdf_output}") + +print("請查看彈出的圖片視窗。") +plt.show() \ No newline at end of file diff --git a/evaluation/command.md b/evaluation/command.md new file mode 100644 index 00000000000..1ac59e4f9f5 --- /dev/null +++ b/evaluation/command.md @@ -0,0 +1,28 @@ +*.1用data裡面的imu +*.2用Examples裡面的imu +*.3 open GUI mode+data + + +1. RANSAC 演算法中使用的亂數產生器。 +2. 追蹤（Tracking）、建圖（Mapping）和迴環檢測（Loop Closing）三個執行緒不可預測的交錯運行，這取決於你電腦作業系統的排程器。」 + + +room1.1 0.016347 +room1.2 0.017131 + +room2.2 0.009742 + +room32 0.029911 +room4.2 0.015034 +room5.2 0.014445 +room6.2 0.006671 + +slides1.1 0.604564 +slides2.1 0.755013 +3.1+ -s 0.691068 + +corridor1.1 0.027595 +corridor2.1 0.016889 +corridor3.1 0.019295 +corridor4.1 0.017819 +corridor5.1 0.017600 \ No newline at end of file diff --git a/evaluation/evaluate_ate_scale.py b/evaluation/evaluate_ate_scale.py index 3eb73ebcf79..83ef1c7ced5 100644 --- a/evaluation/evaluate_ate_scale.py +++ b/evaluation/evaluate_ate_scale.py @@ -1,46 +1,3 @@ -# Modified by Raul Mur-Artal -# Automatically compute the optimal scale factor for monocular VO/SLAM. - -# Software License Agreement (BSD License) -# -# Copyright (c) 2013, Juergen Sturm, TUM -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# * Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. -# * Neither the name of TUM nor the names of its -# contributors may be used to endorse or promote products derived -# from this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE -# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT -# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN -# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -# POSSIBILITY OF SUCH DAMAGE. -# -# Requirements: -# sudo apt-get install python-argparse - -""" -This script computes the absolute trajectory error from the ground truth -trajectory and the estimated trajectory. -""" - import sys import numpy import argparse @@ -164,34 +121,34 @@ def plot_traj(ax,stamps,traj,style,color,label): second_xyz_aligned = scale * rot * second_xyz + trans second_xyz_notscaled = rot * second_xyz + trans second_xyz_notscaled_full = rot * second_xyz_full + trans - first_stamps = first_list.keys() + + # [Python 3 Fix] Convert keys view to list before sorting + first_stamps = list(first_list.keys()) first_stamps.sort() first_xyz_full = numpy.matrix([[float(value) for value in first_list[b][0:3]] for b in first_stamps]).transpose() - second_stamps = second_list.keys() + # [Python 3 Fix] Convert keys view to list before sorting + second_stamps = list(second_list.keys()) second_stamps.sort() second_xyz_full = numpy.matrix([[float(value)*float(args.scale) for value in second_list[b][0:3]] for b in second_stamps]).transpose() second_xyz_full_aligned = scale * rot * second_xyz_full + trans if args.verbose: - print "compared_pose_pairs %d pairs"%(len(trans_error)) - - print "absolute_translational_error.rmse %f m"%numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error)) - print "absolute_translational_error.mean %f m"%numpy.mean(trans_error) - print "absolute_translational_error.median %f m"%numpy.median(trans_error) - print "absolute_translational_error.std %f m"%numpy.std(trans_error) - print "absolute_translational_error.min %f m"%numpy.min(trans_error) - print "absolute_translational_error.max %f m"%numpy.max(trans_error) - print "max idx: %i" %numpy.argmax(trans_error) + print("compared_pose_pairs %d pairs"%(len(trans_error))) + + print("absolute_translational_error.rmse %f m"%numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error))) + print("absolute_translational_error.mean %f m"%numpy.mean(trans_error)) + print("absolute_translational_error.median %f m"%numpy.median(trans_error)) + print("absolute_translational_error.std %f m"%numpy.std(trans_error)) + print("absolute_translational_error.min %f m"%numpy.min(trans_error)) + print("absolute_translational_error.max %f m"%numpy.max(trans_error)) + print("max idx: %i" %numpy.argmax(trans_error)) else: - # print "%f, %f " % (numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error)), scale) - # print "%f,%f" % (numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error)), scale) - print "%f,%f,%f" % (numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error)), scale, numpy.sqrt(numpy.dot(trans_errorGT,trans_errorGT) / len(trans_errorGT))) - # print "%f" % len(trans_error) + print("%f,%f,%f" % (numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error)), scale, numpy.sqrt(numpy.dot(trans_errorGT,trans_errorGT) / len(trans_errorGT)))) if args.verbose2: - print "compared_pose_pairs %d pairs"%(len(trans_error)) - print "absolute_translational_error.rmse %f m"%numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error)) - print "absolute_translational_errorGT.rmse %f m"%numpy.sqrt(numpy.dot(trans_errorGT,trans_errorGT) / len(trans_errorGT)) + print("compared_pose_pairs %d pairs"%(len(trans_error))) + print("absolute_translational_error.rmse %f m"%numpy.sqrt(numpy.dot(trans_error,trans_error) / len(trans_error))) + print("absolute_translational_errorGT.rmse %f m"%numpy.sqrt(numpy.dot(trans_errorGT,trans_errorGT) / len(trans_errorGT))) if args.save_associations: file = open(args.save_associations,"w") @@ -213,17 +170,14 @@ def plot_traj(ax,stamps,traj,style,color,label): ax = fig.add_subplot(111) plot_traj(ax,first_stamps,first_xyz_full.transpose().A,'-',"black","ground truth") plot_traj(ax,second_stamps,second_xyz_full_aligned.transpose().A,'-',"blue","estimated") - label="difference" - for (a,b),(x1,y1,z1),(x2,y2,z2) in zip(matches,first_xyz.transpose().A,second_xyz_aligned.transpose().A): - ax.plot([x1,x2],[y1,y2],'-',color="red",label=label) - label="" + # label="difference" + # for (a,b),(x1,y1,z1),(x2,y2,z2) in zip(matches,first_xyz.transpose().A,second_xyz_aligned.transpose().A): + # ax.plot([x1,x2],[y1,y2],'-',color="red",label=label) + # label="" - ax.legend() + # ax.legend() ax.set_xlabel('x [m]') ax.set_ylabel('y [m]') plt.axis('equal') - plt.savefig(args.plot,format="pdf") - - - + plt.savefig(args.plot,format="pdf") \ No newline at end of file diff --git a/evaluation/fix_time.py b/evaluation/fix_time.py new file mode 100644 index 00000000000..3eb7267686e --- /dev/null +++ b/evaluation/fix_time.py @@ -0,0 +1,48 @@ +import sys +import os + +def fix_tum_timestamps(input_file, output_file): + # 檢查輸入檔案是否存在 + if not os.path.exists(input_file): + print(f"錯誤：找不到檔案 '{input_file}'") + return + + try: + with open(input_file, 'r') as fin, open(output_file, 'w') as fout: + count = 0 + for line in fin: + # 忽略空白行或註解 + if not line.strip() or line.startswith('#'): + fout.write(line) + continue + + parts = line.strip().split() + # 確保是標準的 8 欄 TUM 格式 (時間 + 位置3 + 四元數4) + if len(parts) == 8: + # 將奈秒轉換為秒，保留 6 位小數 + timestamp_s = float(parts[0]) / 1e9 + # 重新組合字串並寫入新檔案 + new_line = f"{timestamp_s:.6f} {' '.join(parts[1:])}\n" + fout.write(new_line) + count += 1 + else: + # 如果不是 8 欄，原封不動照抄 + fout.write(line) + + print(f"轉換成功！共處理了 {count} 行資料。") + print(f"新軌跡檔已儲存為：{output_file}") + + except Exception as e: + print(f"轉換過程中發生錯誤：{e}") + +if __name__ == "__main__": + # 檢查使用者是否有輸入兩個檔案名稱 + if len(sys.argv) != 3: + print("用法提示：") + print("python fix_time.py <你的原始軌跡.txt> <你想命名的新軌跡.txt>") + sys.exit(1) + + old_file = sys.argv[1] + new_file = sys.argv[2] + + fix_tum_timestamps(old_file, new_file) \ No newline at end of file diff --git a/evaluation/run_stereo_inertial_benchmark.py b/evaluation/run_stereo_inertial_benchmark.py new file mode 100644 index 00000000000..879a418f2a5 --- /dev/null +++ b/evaluation/run_stereo_inertial_benchmark.py @@ -0,0 +1,950 @@ +#!/home/lab405/henry/ORB_SLAM3/slam_env/bin/python3 +""" +ORB-SLAM3 Unified Benchmark Script +==================================== +支援以下演算法： + mono – mono_euroc / mono_tum_vi + mono_inertial – mono_inertial_euroc / mono_inertial_tum_vi + stereo – stereo_euroc / stereo_tum_vi + stereo_inertial – stereo_inertial_euroc / stereo_inertial_tum_vi + +可執行的步驟（flags）： + --run 執行 SLAM binary，產生原始軌跡 f_.txt + --fix-time 將時間戳 ns → s，產生 fix_.txt + --eval 以 evo_ape 計算 APE RMSE，結果存入 benchmark_results_.csv + --plot-traj 繪製軌跡比對圖（evaluate_ate_scale.py） + --plot-3d 繪製 XY / 3D / XZ / YZ 四視圖 PDF + --plot-diag 繪製 per-frame ATE + tracking 指標診斷圖 + + 不加任何 step flag → 全部步驟依序執行（預設行為） + +使用方式： + cd /home/lab405/henry/for_git/ORB_SLAM3/evaluation + + # 全跑（原本行為） + python3 run_stereo_inertial_benchmark.py + + # 只跑 SLAM binary（不做後處理） + python3 run_stereo_inertial_benchmark.py --run + + # 只跑 binary + fix-time + python3 run_stereo_inertial_benchmark.py --run --fix-time + + # 已有 fix_*.txt，只重跑評估和所有圖 + python3 run_stereo_inertial_benchmark.py --eval --plot-traj --plot-3d --plot-diag + + # 只重繪診斷圖 + python3 run_stereo_inertial_benchmark.py --plot-diag + + # 篩選演算法 / 資料集（可與 step flags 組合） + python3 run_stereo_inertial_benchmark.py --algo stereo_inertial --dataset euroc + python3 run_stereo_inertial_benchmark.py --algo stereo --dataset euroc --plot-3d --plot-diag +""" + +import argparse +import copy +import csv +import re +import subprocess +import sys +from datetime import datetime +from pathlib import Path + +try: + import matplotlib + matplotlib.use("Agg") + import matplotlib.patches as mpatches + import matplotlib.pyplot as plt + from matplotlib.backends.backend_pdf import PdfPages + import numpy as np + import pandas as pd + from evo.tools import file_interface + from evo.core import sync as evo_sync + import copy + _PLOT_AVAILABLE = True +except ImportError as _e: + _PLOT_AVAILABLE = False + print(f"[WARN] 繪圖套件不可用，跳過軌跡圖與診斷圖：{_e}") + +# ───────────────────────────────────────────── +# 基礎路徑 +# ───────────────────────────────────────────── +SCRIPT_DIR = Path(__file__).parent.resolve() +ROOT_DIR = SCRIPT_DIR.parent + +VOCAB = ROOT_DIR / "Vocabulary/ORBvoc.txt" +EXAMPLES = ROOT_DIR / "Examples" +FIX_TIME_PY = SCRIPT_DIR / "fix_time.py" + +TUM_DATA = ROOT_DIR / "/media/lab405/Windows1/data/TUM" +EUROC_DATA = ROOT_DIR / "/media/lab405/Windows1/data/Euroc" +EVALUATE_ATE_PY = SCRIPT_DIR / "evaluate_ate_scale.py" + +GT_EUROC_LEFT_CAM = SCRIPT_DIR / "Ground_truth/EuRoC_left_cam" +GT_EUROC_IMU = SCRIPT_DIR / "Ground_truth/EuRoC_imu" + +# ───────────────────────────────────────────── +# 演算法設定 +# +# tum_extra(mav0_path) → 插入在 times_file 之前的額外引數列表 +# mono : [] +# mono_inertial : [imu_file] +# stereo : [cam1] +# stereo_inertial : [cam1, imu_file] +# ───────────────────────────────────────────── + +# 插在 times_file 之前的額外引數（僅 cam1） +def _tum_pre_mono(mav0: Path) -> list: + return [] + +def _tum_pre_mono_inertial(mav0: Path) -> list: + return [] + +def _tum_pre_stereo(mav0: Path) -> list: + return [str(mav0 / "cam1/data")] + +def _tum_pre_stereo_inertial(mav0: Path) -> list: + return [str(mav0 / "cam1/data")] + +# 插在 times_file 之後的額外引數（imu CSV） +def _tum_post_mono(mav0: Path) -> list: + return [] + +def _tum_post_mono_inertial(mav0: Path) -> list: + return [str(mav0 / "imu0/data.csv")] + +def _tum_post_stereo(mav0: Path) -> list: + return [] + +def _tum_post_stereo_inertial(mav0: Path) -> list: + return [str(mav0 / "imu0/data.csv")] + + +ALGO_CONFIGS: dict = { + "mono": { + "label": "Mono", + "bin_dir": EXAMPLES / "Monocular", + "bin_euroc": "mono_euroc", + "bin_tum": "mono_tum_vi", + "yaml_euroc": EXAMPLES / "Monocular/EuRoC.yaml", + "yaml_tum": EXAMPLES / "Monocular/TUM-VI.yaml", + "euroc_ts_dir": EXAMPLES / "Monocular/EuRoC_TimeStamps", + "tum_ts_dir": EXAMPLES / "Monocular/TUM_TimeStamps", + "tum_pre": _tum_pre_mono, # 插在 times 前 + "tum_post": _tum_post_mono, # 插在 times 後 + "out_prefix_e": "mono_euroc", + "out_prefix_t": "mono_tum", + "evo_align": "-as", # monocular: align + scale + "euroc_gt_type": "left_cam", # GT 來源：left camera frame + "out_subdir": "mono", + }, + "mono_inertial": { + "label": "Mono-Inertial", + "bin_dir": EXAMPLES / "Monocular-Inertial", + "bin_euroc": "mono_inertial_euroc", + "bin_tum": "mono_inertial_tum_vi", + "yaml_euroc": EXAMPLES / "Monocular-Inertial/EuRoC.yaml", + "yaml_tum": EXAMPLES / "Monocular-Inertial/TUM-VI.yaml", + "euroc_ts_dir": EXAMPLES / "Monocular-Inertial/EuRoC_TimeStamps", + "tum_ts_dir": EXAMPLES / "Monocular-Inertial/TUM_TimeStamps", + "tum_pre": _tum_pre_mono_inertial, # 插在 times 前（無） + "tum_post": _tum_post_mono_inertial, # 插在 times 後（imu） + "out_prefix_e": "mimu_euroc", + "out_prefix_t": "mimu_tum", + "evo_align": "-a", # inertial: scale known, align only + "euroc_gt_type": "imu", # GT 來源：IMU/body frame + "out_subdir": "mono_inertial", + }, + "stereo": { + "label": "Stereo", + "bin_dir": EXAMPLES / "Stereo", + "bin_euroc": "stereo_euroc", + "bin_tum": "stereo_tum_vi", + "yaml_euroc": EXAMPLES / "Stereo/EuRoC.yaml", + "yaml_tum": EXAMPLES / "Stereo/TUM-VI.yaml", + "euroc_ts_dir": EXAMPLES / "Stereo/EuRoC_TimeStamps", + "tum_ts_dir": EXAMPLES / "Stereo/TUM_TimeStamps", + "tum_pre": _tum_pre_stereo, # 插在 times 前（cam1） + "tum_post": _tum_post_stereo, # 插在 times 後（無） + "out_prefix_e": "stereo_euroc", + "out_prefix_t": "stereo_tum", + "evo_align": "-a", # stereo: align only + "euroc_gt_type": "left_cam", # GT 來源：left camera frame + "out_subdir": "stereo", + }, + "stereo_inertial": { + "label": "Stereo-Inertial", + "bin_dir": EXAMPLES / "Stereo-Inertial", + "bin_euroc": "stereo_inertial_euroc", + "bin_tum": "stereo_inertial_tum_vi", + "yaml_euroc": EXAMPLES / "Stereo-Inertial/EuRoC.yaml", + "yaml_tum": EXAMPLES / "Stereo-Inertial/TUM-VI.yaml", + "euroc_ts_dir": EXAMPLES / "Stereo-Inertial/EuRoC_TimeStamps", + "tum_ts_dir": EXAMPLES / "Stereo-Inertial/TUM_TimeStamps", + "tum_pre": _tum_pre_stereo_inertial, # 插在 times 前（cam1） + "tum_post": _tum_post_stereo_inertial, # 插在 times 後（imu） + "out_prefix_e": "stereo_imu_euroc", + "out_prefix_t": "stereo_imu_tum", + "evo_align": "-a", # stereo: align only + "euroc_gt_type": "imu", # GT 來源：IMU/body frame + "out_subdir": "stereo_imu", + }, +} + +# ───────────────────────────────────────────── +# 資料集清單 +# EuRoC : (seq_folder, ts_stem, output_suffix) +# TUM-VI: (category, seq_name, output_suffix) +# ───────────────────────────────────────────── +EUROC_DATASETS = [ + # ("V1_01_easy", "V101", "v101"), + ("V1_02_medium", "V102", "v102"), + # ("V1_03_difficult", "V103", "v103"), + # ("V2_01_easy", "V201", "v201"), + # ("V2_02_medium", "V202", "v202"), + # ("V2_03_difficult", "V203", "v203"), + # ("MH_01_easy", "MH01", "m01"), + # ("MH_02_easy", "MH02", "m02"), + # ("MH_03_medium", "MH03", "m03"), + # ("MH_04_difficult", "MH04", "m04"), + # ("MH_05_difficult", "MH05", "m05"), +] + +TUM_DATASETS = [ + # room + # ("room", "room1", "room1"), + # ("room", "room2", "room2"), + # ("room", "room3", "room3"), + # ("room", "room4", "room4"), + # ("room", "room5", "room5"), + # ("room", "room6", "room6"), + # # corridor + # ("corridor", "corridor1", "corr1"), + # ("corridor", "corridor2", "corr2"), + # ("corridor", "corridor3", "corr3"), + # ("corridor", "corridor4", "corr4"), + # ("corridor", "corridor5", "corr5"), + # # slides + # ("slides", "slides1", "slides1"), + # ("slides", "slides2", "slides2"), + # ("slides", "slides3", "slides3"), + # # magistrale + # ("magistrale", "magistrale1", "mag1"), + # ("magistrale", "magistrale2", "mag2"), + # ("magistrale", "magistrale3", "mag3"), + # ("magistrale", "magistrale4", "mag4"), + # ("magistrale", "magistrale5", "mag5"), + #outdoors + ("outdoors", "outdoors6", "out6"), +] + +# ───────────────────────────────────────────── +# 輔助函式 +# ───────────────────────────────────────────── + +def log(msg: str) -> None: + print(msg, flush=True) + + +def _seq_to_subdir(seq_suffix: str) -> str: + """Convert sequence suffix (e.g. 'm01') to output subdirectory name ('MH_01').""" + m = re.match(r'^m(\d+)$', seq_suffix) + if m: + return f"MH_{m.group(1).zfill(2)}" + return seq_suffix + + +def _get_out_dir(cfg: dict, seq_suffix: str) -> Path: + """Compute (and create) the per-run output subdirectory.""" + out_dir = SCRIPT_DIR / cfg["out_subdir"] / _seq_to_subdir(seq_suffix) + out_dir.mkdir(parents=True, exist_ok=True) + return out_dir + + +def run_cmd(cmd: list, cwd=None, timeout: int = 7200): + """執行外部指令，回傳 (stdout, stderr, returncode)""" + cmd_str = " ".join(str(c) for c in cmd) + log(f" >> {cmd_str}") + result = subprocess.run( + [str(c) for c in cmd], + cwd=str(cwd) if cwd else None, + capture_output=True, + text=True, + timeout=timeout, + ) + return result.stdout, result.stderr, result.returncode + + +def parse_evo_rmse(stdout: str, stderr: str) -> float | None: + """從 evo_ape 輸出解析 RMSE 數值""" + combined = stdout + stderr + print("=== evo_ape raw output ===") + print(combined[:1000]) # ← 加這行看原始輸出 + print("=========================") + match = re.search(r'rmse\s+([\d.eE+\-]+)', combined) + if match: + return float(match.group(1)) + return None + + +def resolve_euroc_gt(ts_stem: str, gt_type: str) -> str: + """ + 根據 ts_stem（如 'V102'、'V201'、'MH01'）和 gt_type 找到本地 GT 檔案路徑。 + + gt_type='left_cam'： + GT_EUROC_LEFT_CAM/{ts_stem}_GT.txt + 例：V102 → Ground_truth/EuRoC_left_cam/V102_GT.txt + + gt_type='imu'： + GT_EUROC_IMU/{group}_GT.txt，group 取 ts_stem 前 2 字元 + 例：V102 → V1_GT.txt；V201 → V2_GT.txt；MH01 → MH_GT.txt + """ + if gt_type == "left_cam": + return str(GT_EUROC_LEFT_CAM / f"{ts_stem}_GT.txt") + else: # imu + group = ts_stem[:2] # "V1", "V2", "MH" + return str(GT_EUROC_IMU / f"{group}_GT.txt") + + +def resolve_euroc_seq_path(seq_folder: str) -> Path | None: + """解析 EuRoC 序列實際路徑（處理巢狀資料夾情況）""" + base = EUROC_DATA / seq_folder + if (base / "mav0").exists(): + return base + nested = base / seq_folder + if (nested / "mav0").exists(): + return nested + return None + + +def fix_time(raw_traj: Path, fixed_traj: Path) -> bool: + """呼叫 fix_time.py 轉換時間戳 ns → s""" + stdout, stderr, rc = run_cmd( + [sys.executable, FIX_TIME_PY, raw_traj, fixed_traj] + ) + if rc != 0: + log(f" [ERROR] fix_time.py 失敗:\n{stderr[:500]}") + return False + return True + + +def eval_with_evo(gt_csv: str, fixed_traj: Path, align_flag: str = "-a") -> float | None: + """以 evo_ape euroc 計算 RMSE（align_flag: '-a' 或 '-as'）""" + stdout, stderr, rc = run_cmd( + ["evo_ape", "euroc", gt_csv, str(fixed_traj), align_flag], + cwd=SCRIPT_DIR, + ) + if rc != 0: + log(f" [ERROR] evo_ape 失敗:\n{stderr[:500]}") + return None + rmse = parse_evo_rmse(stdout, stderr) + if rmse is None: + log(f" [WARN] 無法從 evo_ape 輸出解析 RMSE\n{stdout[:300]}") + return rmse + + +def convert_gt_to_tum(gt_csv: str, out_path: Path) -> bool: + """將 EuRoC/TUM-VI GT CSV (timestamp_ns, px, py, pz, qw, qx, qy, qz, ...) + 轉換為 evaluate_ate_scale.py 所需的 TUM 格式 (timestamp_s tx ty tz qx qy qz qw)。""" + try: + with open(gt_csv, "r") as fin, open(out_path, "w") as fout: + for line in fin: + line = line.strip() + if not line or line.startswith('#'): + continue + parts = line.split(',') + if len(parts) < 8: + continue + ts_s = float(parts[0]) / 1e9 + px, py, pz = parts[1].strip(), parts[2].strip(), parts[3].strip() + # EuRoC/TUM-VI GT 四元數順序為 qw, qx, qy, qz；TUM 格式需要 qx, qy, qz, qw + qw, qx, qy, qz = parts[4].strip(), parts[5].strip(), parts[6].strip(), parts[7].strip() + fout.write(f"{ts_s:.9f} {px} {py} {pz} {qx} {qy} {qz} {qw}\n") + return True + except Exception as e: + log(f" [WARN] GT 格式轉換失敗: {e}") + return False + + +def plot_trajectory_3d_views(gt_csv: str, fixed_traj: Path, output_name: str, out_dir: Path) -> None: + """用 evo 產生 XY / 3D / XZ / YZ 四視圖 PDF（來自 ate_3d.py）。""" + if not _PLOT_AVAILABLE: + return + out_pdf = out_dir / f"traj3d_{output_name}.pdf" + try: + traj_ref = file_interface.read_euroc_csv_trajectory(gt_csv) + traj_est = file_interface.read_tum_trajectory_file(str(fixed_traj)) + traj_ref, traj_est = evo_sync.associate_trajectories(traj_ref, traj_est, 0.01) + traj_est_aligned = copy.deepcopy(traj_est) + traj_est_aligned.align(traj_ref, correct_scale=False) + + gt_x = traj_ref.positions_xyz[:, 0] + gt_y = traj_ref.positions_xyz[:, 1] + gt_z = traj_ref.positions_xyz[:, 2] + px = traj_est_aligned.positions_xyz[:, 0] + py = traj_est_aligned.positions_xyz[:, 1] + pz = traj_est_aligned.positions_xyz[:, 2] + + ts = traj_est_aligned.timestamps + t0 = ts[0] + n = len(px) + colors = plt.cm.rainbow(np.linspace(0, 1, max(n - 1, 1))) + interval = max(1, n // 10) + + def _add_time_labels(ax_2d, xs, ys): + for i in range(0, n, interval): + ax_2d.annotate(f"{ts[i]-t0:.1f}s", (xs[i], ys[i]), + fontsize=7, color="dimgray", + xytext=(4, 4), textcoords="offset points") + + with PdfPages(str(out_pdf)) as pdf: + # XY（時間漸層） + fig, ax = plt.subplots(figsize=(10, 8)) + ax.plot(gt_x, gt_y, "k-", linewidth=2, label="Ground Truth") + for i in range(n - 1): + ax.plot(px[i:i+2], py[i:i+2], color=colors[i], linewidth=2.0) + ax.plot(px[0], py[0], "go", markersize=8, label="Start") + ax.plot(px[-1], py[-1], "rs", markersize=8, label="End") + _add_time_labels(ax, px, py) + sm = plt.cm.ScalarMappable(cmap="rainbow", + norm=plt.Normalize(vmin=0, vmax=ts[-1]-t0)) + sm.set_array([]); plt.colorbar(sm, ax=ax, label="Elapsed time (s)", shrink=0.7) + ax.axis("equal"); ax.set_xlabel("x [m]"); ax.set_ylabel("y [m]") + ax.set_title(f"Trajectory XY — {output_name} (color=time)") + ax.legend(); ax.grid(False) + pdf.savefig(fig, bbox_inches="tight"); plt.close(fig) + + # 3D（時間漸層） + fig3 = plt.figure(figsize=(10, 8)) + ax3 = fig3.add_subplot(111, projection="3d") + ax3.plot(gt_x, gt_y, gt_z, "k-", linewidth=2, label="Ground Truth") + for i in range(n - 1): + ax3.plot(px[i:i+2], py[i:i+2], pz[i:i+2], color=colors[i], linewidth=2.0) + ax3.scatter(px[0], py[0], pz[0], c="green", s=60, zorder=5, label="Start") + ax3.scatter(px[-1], py[-1], pz[-1], c="red", s=60, marker="s", zorder=5, label="End") + for i in range(0, n, interval): + ax3.text(px[i], py[i], pz[i], f"{ts[i]-t0:.1f}s", fontsize=7, color="dimgray") + ax3.set_xlabel("x [m]"); ax3.set_ylabel("y [m]"); ax3.set_zlabel("z [m]") + ax3.set_title(f"Trajectory 3D — {output_name} (color=time)"); ax3.legend() + try: ax3.set_box_aspect((1, 1, 1)) + except AttributeError: pass + pdf.savefig(fig3, bbox_inches="tight"); plt.close(fig3) + + # XZ + fig_xz, ax_xz = plt.subplots(figsize=(10, 8)) + ax_xz.plot(gt_x, gt_z, "k-", linewidth=2, label="Ground Truth") + for i in range(n - 1): + ax_xz.plot(px[i:i+2], pz[i:i+2], color=colors[i], linewidth=2.0) + _add_time_labels(ax_xz, px, pz) + ax_xz.axis("equal"); ax_xz.set_xlabel("x [m]"); ax_xz.set_ylabel("z [m]") + ax_xz.set_title(f"Trajectory XZ — {output_name} (color=time)") + ax_xz.legend(); ax_xz.grid(False) + pdf.savefig(fig_xz, bbox_inches="tight"); plt.close(fig_xz) + + # YZ + fig_yz, ax_yz = plt.subplots(figsize=(10, 8)) + ax_yz.plot(gt_y, gt_z, "k-", linewidth=2, label="Ground Truth") + for i in range(n - 1): + ax_yz.plot(py[i:i+2], pz[i:i+2], color=colors[i], linewidth=2.0) + _add_time_labels(ax_yz, py, pz) + ax_yz.axis("equal"); ax_yz.set_xlabel("y [m]"); ax_yz.set_ylabel("z [m]") + ax_yz.set_title(f"Trajectory YZ — {output_name} (color=time)") + ax_yz.legend(); ax_yz.grid(False) + pdf.savefig(fig_yz, bbox_inches="tight"); plt.close(fig_yz) + + log(f" [PLOT] 3D 軌跡圖已儲存至: {out_pdf.name}") + except Exception as e: + log(f" [WARN] 3D 軌跡圖失敗: {e}") + + +def _load_gt_positions(gt_csv: str) -> pd.DataFrame: + rows = [] + with open(gt_csv) as f: + for line in f: + line = line.strip() + if not line or line.startswith("#"): + continue + parts = line.split(",") + if len(parts) < 4: + continue + try: + ts = float(parts[0]) / 1e9 + rows.append({"timestamp": ts, + "px": float(parts[1]), + "py": float(parts[2]), + "pz": float(parts[3])}) + except ValueError: + continue + return pd.DataFrame(rows) + + +def _load_traj_positions(traj_path: Path) -> pd.DataFrame: + rows = [] + with open(traj_path) as f: + for line in f: + line = line.strip() + if not line or line.startswith("#"): + continue + parts = line.split() + if len(parts) < 4: + continue + rows.append({"timestamp": float(parts[0]), + "px": float(parts[1]), + "py": float(parts[2]), + "pz": float(parts[3])}) + return pd.DataFrame(rows) + + +def _align_se3(est_pos: np.ndarray, gt_pos: np.ndarray): + mu_e = est_pos.mean(0); mu_g = gt_pos.mean(0) + H = (est_pos - mu_e).T @ (gt_pos - mu_g) + U, _, Vt = np.linalg.svd(H) + d = np.linalg.det(Vt.T @ U.T) + R = Vt.T @ np.diag([1, 1, d]) @ U.T + return R, mu_g - R @ mu_e + + +def plot_diagnosis(gt_csv: str, fixed_traj: Path, output_name: str, out_dir: Path) -> None: + """產生 per-frame ATE + tracking 指標診斷圖（來自 analyze_trajectory.py）。""" + if not _PLOT_AVAILABLE: + return + log_path = out_dir / f"{output_name}_tracking_log.csv" + out_pdf = out_dir / f"diagnosis_{output_name}.pdf" + if not log_path.exists(): + log(f" [WARN] 找不到 tracking log: {log_path.name}，跳過診斷圖") + return + try: + est = _load_traj_positions(fixed_traj) + gt = _load_gt_positions(gt_csv) + log_df = pd.read_csv(log_path) + + # 對齊時間戳 + est_ts = est["timestamp"].values + gt_ts = gt["timestamp"].values + gt_pos = gt[["px", "py", "pz"]].values + est_pos_list, gt_pos_list, ts_list = [], [], [] + for i, ts in enumerate(est_ts): + idx = int(np.argmin(np.abs(gt_ts - ts))) + if np.abs(gt_ts[idx] - ts) < 0.05: + est_pos_list.append(est[["px", "py", "pz"]].values[i]) + gt_pos_list.append(gt_pos[idx]) + ts_list.append(ts) + if not ts_list: + log(" [WARN] 診斷圖：無對齊幀，跳過") + return + + ts_arr = np.array(ts_list) + est_pos = np.array(est_pos_list) + gt_pos_a = np.array(gt_pos_list) + R, t = _align_se3(est_pos, gt_pos_a) + errors = np.linalg.norm((R @ est_pos.T).T + t - gt_pos_a, axis=1) + common_t0 = ts_arr[0] # 軌跡第一幀的絕對時間戳為共同參考點 + rel_ts = ts_arr - common_t0 + rmse = float(np.sqrt(np.mean(errors ** 2))) + + log_rel = log_df["timestamp"].values - common_t0 + state_col_name = "當前幀狀態" if "當前幀狀態" in log_df.columns else \ + ("当前帧状态" if "当前帧状态" in log_df.columns else None) + if state_col_name: + lost_mask = log_df[state_col_name].isin(["LOST", "RECENTLY_LOST"]).values + else: + lost_mask = np.zeros(len(log_df), dtype=bool) + reloc_mask = log_df["reloc_attempted"].astype(str).str.lower().isin(["yes", "true", "1"]).values + method_map = {"none": 0, "RefKF": 1, "MM": 2, "IMU_MM": 3, "Reloc": 4} + method_vals = log_df["tracking_method"].map(lambda x: method_map.get(str(x), -1)).values + + fig, axes = plt.subplots(4, 1, figsize=(16, 14), sharex=False) + fig.suptitle(f"Trajectory Diagnosis: {output_name}", fontsize=13, fontweight="bold") + + # 子圖 1：ATE + ax1 = axes[0] + ax1.plot(rel_ts, errors, color="royalblue", linewidth=0.8, label="Position Error (m)") + ax1.axhline(rmse, color="red", linestyle="--", linewidth=0.8, label=f"RMSE={rmse:.4f}m") + ax1.set_ylabel("ATE (m)"); ax1.set_title("Per-frame Position Error (after SE3 alignment)") + ax1.legend(loc="upper right", fontsize=8); ax1.grid(True, alpha=0.3) + + # 子圖 2：final_inliers vs matches_before_tlm_opt + ax2 = axes[1] + ax2.plot(log_rel, log_df["matches_before_tlm_opt"].values, + color="orange", linewidth=0.8, linestyle="--", label="before_opt") + ax2.plot(log_rel, log_df["final_inliers"].values, + color="darkgreen", linewidth=0.8, label="final_inliers") + ax2.set_ylabel("Inliers") + ax2.set_title("Inliers: before opt (orange--) vs after opt (green) | large gap = unstable pose opt") + ax2.legend(loc="upper right", fontsize=8); ax2.grid(True, alpha=0.3) + + # LOST 區塊 + in_lost = False; lost_start = None + for t_i, is_lost in zip(log_rel, lost_mask): + if is_lost and not in_lost: + lost_start = t_i; in_lost = True + elif not is_lost and in_lost: + for ax in [ax1, ax2]: + ax.axvspan(lost_start, t_i, alpha=0.15, color="gray") + in_lost = False + if in_lost: + for ax in [ax1, ax2]: + ax.axvspan(lost_start, log_rel[-1], alpha=0.15, color="gray") + + for t_i in log_rel[reloc_mask]: + ax2.axvline(t_i, color="red", linewidth=0.5, alpha=0.6) + + # 子圖 3：tracking_method step 折線 + 狀態背景 + ax3 = axes[2] + ax3.step(log_rel, method_vals, where="mid", color="royalblue", linewidth=0.8) + ax3.set_yticks(list(method_map.values())) + ax3.set_yticklabels(list(method_map.keys()), fontsize=8) + ax3.set_ylim(-0.5, max(method_map.values()) + 0.5) + if state_col_name: + rl_mask = log_df[state_col_name].astype(str) == "RECENTLY_LOST" + lo_mask = log_df[state_col_name].astype(str) == "LOST" + in_rl = False; in_lo = False; rl_s = lo_s = None + for t_i, is_rl, is_lo in zip(log_rel, rl_mask.values, lo_mask.values): + if is_rl and not in_rl: + rl_s = t_i; in_rl = True + elif not is_rl and in_rl: + ax3.axvspan(rl_s, t_i, alpha=0.35, color="orange"); in_rl = False + if is_lo and not in_lo: + lo_s = t_i; in_lo = True + elif not is_lo and in_lo: + ax3.axvspan(lo_s, t_i, alpha=0.5, color="red"); in_lo = False + if in_rl and rl_s is not None: + ax3.axvspan(rl_s, log_rel[-1], alpha=0.35, color="orange") + if in_lo and lo_s is not None: + ax3.axvspan(lo_s, log_rel[-1], alpha=0.5, color="red") + ax3.set_ylabel("Tracking Method") + ax3.set_title("Tracking Method (step) | orange=RECENTLY_LOST red=LOST") + ax3.legend(handles=[ + mpatches.Patch(color="orange", alpha=0.5, label="RECENTLY_LOST"), + mpatches.Patch(color="red", alpha=0.5, label="LOST"), + ], loc="upper right", fontsize=7) + ax3.grid(True, alpha=0.3) + + # 子圖 4：bias + ax4 = axes[3] + ax4.plot(log_rel, log_df["bias_acc_norm"].values, color="tomato", linewidth=0.8, label="bias_acc_norm") + ax4.plot(log_rel, log_df["bias_gyro_norm"].values, color="steelblue", linewidth=0.8, label="bias_gyro_norm") + ax4.set_ylabel("Bias norm"); ax4.set_xlabel("Relative time (s)") + ax4.set_title("IMU Bias Norm (always 0 in pure visual mode)") + ax4.legend(loc="upper right", fontsize=8); ax4.grid(True, alpha=0.3) + + t_max = max(float(log_rel[-1]), float(rel_ts[-1])) + for ax in axes: + ax.set_xlim(0, t_max) + + lost_patch = mpatches.Patch(color="gray", alpha=0.3, label="LOST / RECENTLY_LOST") + reloc_patch = mpatches.Patch(color="red", alpha=0.6, label="Reloc attempted") + fig.legend(handles=[lost_patch, reloc_patch], loc="lower center", ncol=2, fontsize=8) + + plt.tight_layout(rect=[0, 0.03, 1, 0.97]) + fig.savefig(str(out_pdf), dpi=150, bbox_inches="tight") + plt.close(fig) + log(f" [PLOT] 診斷圖已儲存至: {out_pdf.name} (RMSE={rmse:.6f}m, frames={len(ts_arr)})") + except Exception as e: + log(f" [WARN] 診斷圖失敗: {e}") + + +def plot_trajectory(gt_csv: str, fixed_traj: Path, output_name: str, out_dir: Path) -> None: + """呼叫 evaluate_ate_scale.py --plot 產生軌跡比對圖（PDF）。""" + if not EVALUATE_ATE_PY.exists(): + log(f" [WARN] 找不到 {EVALUATE_ATE_PY}，跳過繪圖") + return + + gt_tum = out_dir / f"gt_tum_{output_name}.txt" + plot_out = out_dir / f"trajectory_{output_name}.pdf" + + if not convert_gt_to_tum(gt_csv, gt_tum): + return + + stdout, stderr, rc = run_cmd( + [sys.executable, str(EVALUATE_ATE_PY), + str(gt_tum), str(fixed_traj), + "--max_difference", "0.02", + "--plot", str(plot_out)], + cwd=SCRIPT_DIR, + ) + gt_tum.unlink(missing_ok=True) # 刪除臨時 GT 轉換檔 + if rc != 0: + log(f" [WARN] 軌跡繪圖失敗:\n{stderr[:300]}") + else: + log(f" [PLOT] 軌跡圖已儲存至: {plot_out.name}") + + +def _postprocess(raw_traj: Path, output_name: str, gt_csv: str, + align_flag: str = "-a", steps: set = None, + out_dir: Path = None) -> tuple: + """fix_time + evo_ape + 軌跡圖，回傳 (rmse_or_'FAILED', note)""" + if steps is None: + steps = {"fix-time", "eval", "plot-traj", "plot-3d", "plot-diag"} + if out_dir is None: + out_dir = SCRIPT_DIR + + fixed_traj = out_dir / f"fix_{output_name}.txt" + + if "fix-time" in steps: + if not raw_traj.exists(): + log(f" [ERROR] {raw_traj} 不存在") + return "FAILED", f"找不到輸出軌跡: {raw_traj}" + if not fix_time(raw_traj, fixed_traj): + return "FAILED", "fix_time.py 失敗" + else: + if not fixed_traj.exists(): + log(f" [SKIP] fix-time 未執行且 {fixed_traj.name} 不存在，跳過後續步驟") + return "SKIP", "需先執行 --fix-time" + + if "plot-traj" in steps: + plot_trajectory(gt_csv, fixed_traj, output_name, out_dir) + + if "plot-3d" in steps: + plot_trajectory_3d_views(gt_csv, fixed_traj, output_name, out_dir) + + if "plot-diag" in steps: + plot_diagnosis(gt_csv, fixed_traj, output_name, out_dir) + + if "eval" in steps: + rmse = eval_with_evo(gt_csv, fixed_traj, align_flag) + if rmse is not None: + return rmse, "OK" + return "FAILED", "evo_ape 無法解析 RMSE" + + return "SKIP", "未執行 eval" + + +# ───────────────────────────────────────────── +# EuRoC 執行函式 +# 所有 EuRoC 二進位呼叫格式相同： +# bin vocab yaml seq_path times_file output_name +# （IMU 由二進位內部從 mav0/imu0 載入） +# ───────────────────────────────────────────── + +def run_euroc(cfg: dict, seq_folder: str, ts_stem: str, seq_suffix: str, + steps: set = None) -> dict: + if steps is None: + steps = {"run", "fix-time", "eval", "plot-traj", "plot-3d", "plot-diag"} + + output_name = f"{cfg['out_prefix_e']}_{seq_suffix}" + out_dir = _get_out_dir(cfg, seq_suffix) + entry = { + "algo": cfg["label"], + "dataset": "EuRoC", + "sequence": seq_folder, + "output_name": output_name, + "rmse": "FAILED", + "note": "", + } + + seq_path = resolve_euroc_seq_path(seq_folder) + if seq_path is None: + entry["note"] = "序列資料夾不存在" + log(f" [SKIP] {EUROC_DATA / seq_folder}/mav0 不存在") + return entry + + times_file = cfg["euroc_ts_dir"] / f"{ts_stem}.txt" + gt_csv = resolve_euroc_gt(ts_stem, cfg.get("euroc_gt_type", "left_cam")) + binary = cfg["bin_dir"] / cfg["bin_euroc"] + + if "run" in steps: + for p, label in [(binary, "執行檔"), (times_file, "時間戳檔案"), + (Path(gt_csv), "Ground truth")]: + if not Path(p).exists(): + entry["note"] = f"{label} 不存在: {p}" + log(f" [SKIP] {p}") + return entry + + cmd = [binary, VOCAB, cfg["yaml_euroc"], seq_path, times_file, output_name] + _, stderr, rc = run_cmd(cmd, cwd=ROOT_DIR) + + for log_type in ["tracking_log.csv", "localmapping_log.csv", "loop_closing_log.csv"]: + old_log = ROOT_DIR / log_type + if old_log.exists(): + new_log = out_dir / f"{output_name}_{log_type}" + old_log.rename(new_log) + log(f" [LOG] Moved {log_type} to {new_log.relative_to(SCRIPT_DIR)}") + + if rc != 0: + entry["note"] = f"{cfg['bin_euroc']} 回傳 {rc}" + log(f" [ERROR] 執行失敗:\n{stderr[-800:]}") + return entry + else: + if not Path(gt_csv).exists(): + entry["note"] = f"Ground truth 不存在: {gt_csv}" + return entry + + rmse, note = _postprocess(ROOT_DIR / f"f_{output_name}.txt", output_name, gt_csv, + cfg["evo_align"], steps, out_dir) + entry["rmse"] = rmse + entry["note"] = note + return entry + + +# ───────────────────────────────────────────── +# TUM-VI 執行函式 +# 呼叫格式：bin vocab yaml cam0 [extra...] times_file output_name +# extra 依演算法而異（無 / imu / cam1 / cam1+imu） +# ───────────────────────────────────────────── + +def run_tum(cfg: dict, category: str, seq_name: str, seq_suffix: str, + steps: set = None) -> dict: + if steps is None: + steps = {"run", "fix-time", "eval", "plot-traj", "plot-3d", "plot-diag"} + + output_name = f"{cfg['out_prefix_t']}_{seq_suffix}" + out_dir = _get_out_dir(cfg, seq_suffix) + entry = { + "algo": cfg["label"], + "dataset": "TUM-VI", + "sequence": seq_name, + "output_name": output_name, + "rmse": "FAILED", + "note": "", + } + + dataset_path = TUM_DATA / category / f"dataset-{seq_name}_512_16" + mav0 = dataset_path / "mav0" + gt_csv = str(mav0 / "mocap0/data.csv") + + if "run" in steps: + if not mav0.exists(): + entry["note"] = f"資料集目錄不存在: {mav0}" + log(f" [SKIP] {mav0}") + return entry + + cam0 = mav0 / "cam0/data" + times_file = cfg["tum_ts_dir"] / f"dataset-{seq_name}_512.txt" + binary = cfg["bin_dir"] / cfg["bin_tum"] + pre_args = cfg["tum_pre"](mav0) + post_args = cfg["tum_post"](mav0) + + for p, label in [(binary, "執行檔"), (cam0, "cam0"), + (times_file, "timestamps"), (Path(gt_csv), "Ground truth")]: + if not Path(p).exists(): + entry["note"] = f"{label} 不存在: {p}" + log(f" [SKIP] {p}") + return entry + + for extra_p in pre_args + post_args: + if not Path(extra_p).exists(): + entry["note"] = f"額外引數路徑不存在: {extra_p}" + log(f" [SKIP] {extra_p}") + return entry + + cmd = [binary, VOCAB, cfg["yaml_tum"], cam0] + pre_args + [times_file] + post_args + [output_name] + _, stderr, rc = run_cmd(cmd, cwd=ROOT_DIR) + + for log_type in ["tracking_log.csv", "localmapping_log.csv", "loop_closing_log.csv"]: + old_log = ROOT_DIR / log_type + if old_log.exists(): + new_log = out_dir / f"{output_name}_{log_type}" + old_log.rename(new_log) + log(f" [LOG] Moved {log_type} to {new_log.relative_to(SCRIPT_DIR)}") + + if rc != 0: + entry["note"] = f"{cfg['bin_tum']} 回傳 {rc}" + log(f" [ERROR] 執行失敗:\n{stderr[-800:]}") + return entry + else: + if not Path(gt_csv).exists(): + entry["note"] = f"Ground truth 不存在: {gt_csv}" + return entry + + rmse, note = _postprocess(ROOT_DIR / f"f_{output_name}.txt", output_name, gt_csv, + cfg["evo_align"], steps, out_dir) + entry["rmse"] = rmse + entry["note"] = note + return entry + + +# ───────────────────────────────────────────── +# 主程式 +# ───────────────────────────────────────────── + +def main() -> None: + parser = argparse.ArgumentParser(description="ORB-SLAM3 Unified Benchmark") + parser.add_argument( + "--algo", + choices=["mono", "mono_inertial", "stereo", "stereo_inertial", "all"], + default="all", + help="要測試的演算法 (預設: all)", + ) + parser.add_argument( + "--dataset", + choices=["euroc", "tum", "all"], + default="all", + help="要測試的資料集 (預設: all)", + ) + parser.add_argument("--run", action="store_true", help="執行 SLAM binary") + parser.add_argument("--fix-time", action="store_true", help="轉換時間戳 ns→s") + parser.add_argument("--eval", action="store_true", help="計算 evo_ape RMSE") + parser.add_argument("--plot-traj", action="store_true", help="繪製軌跡比對圖") + parser.add_argument("--plot-3d", action="store_true", help="繪製 3D 四視圖") + parser.add_argument("--plot-diag", action="store_true", help="繪製診斷圖") + args = parser.parse_args() + + # 若沒有指定任何 step → 全跑 + _all_steps = {"run", "fix-time", "eval", "plot-traj", "plot-3d", "plot-diag"} + active_steps = {s for s in _all_steps if getattr(args, s.replace("-", "_"))} + if not active_steps: + active_steps = _all_steps + + log(f"執行步驟: {', '.join(sorted(active_steps))}") + + algo_keys = list(ALGO_CONFIGS.keys()) if args.algo == "all" else [args.algo] + + # 預先警告缺少執行檔 + for key in algo_keys: + cfg = ALGO_CONFIGS[key] + for bin_name in (cfg["bin_euroc"], cfg["bin_tum"]): + bin_path = cfg["bin_dir"] / bin_name + if not bin_path.exists(): + log(f"[WARN] 找不到執行檔: {bin_path}，請先編譯專案") + + results = [] + + for algo_key in algo_keys: + cfg = ALGO_CONFIGS[algo_key] + + # ── EuRoC ── + if args.dataset in ("euroc", "all"): + log("\n" + "=" * 65) + log(f"{cfg['label']} × EuRoC 測試") + log("=" * 65) + for seq_folder, ts_stem, seq_suffix in EUROC_DATASETS: + log(f"\n[{cfg['label']}][EuRoC] {seq_folder}") + entry = run_euroc(cfg, seq_folder, ts_stem, seq_suffix, active_steps) + results.append(entry) + log(f" → RMSE = {entry['rmse']} ({entry['note']})") + + # ── TUM-VI ── + if args.dataset in ("tum", "all"): + log("\n" + "=" * 65) + log(f"{cfg['label']} × TUM-VI 測試") + log("=" * 65) + for category, seq_name, seq_suffix in TUM_DATASETS: + log(f"\n[{cfg['label']}][TUM-VI] {seq_name}") + entry = run_tum(cfg, category, seq_name, seq_suffix, active_steps) + results.append(entry) + log(f" → RMSE = {entry['rmse']} ({entry['note']})") + + # ── 儲存 CSV ── + ts_str = datetime.now().strftime("%Y%m%d_%H%M%S") + csv_path = SCRIPT_DIR / f"benchmark_results_{ts_str}.csv" + + fieldnames = ["algo", "dataset", "sequence", "output_name", "rmse", "note"] + with open(csv_path, "w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames) + writer.writeheader() + writer.writerows(results) + + # ── 摘要輸出 ── + log("\n" + "=" * 75) + log(f"結果已儲存至: {csv_path}") + log("=" * 75) + log(f"{'Algo':<18} {'Dataset':<8} {'Sequence':<22} {'RMSE':<14} {'Note'}") + log("-" * 75) + for r in results: + rmse_str = f"{r['rmse']:.6f}" if isinstance(r['rmse'], float) else str(r['rmse']) + log(f"{r['algo']:<18} {r['dataset']:<8} {r['sequence']:<22} {rmse_str:<14} {r['note']}") + + +if __name__ == "__main__": + main() diff --git a/for_orb_slam2/include/Converter.h b/for_orb_slam2/include/Converter.h new file mode 100644 index 00000000000..0002ad62c2e --- /dev/null +++ b/for_orb_slam2/include/Converter.h @@ -0,0 +1,57 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#ifndef CONVERTER_H +#define CONVERTER_H + +#include + +#include +#include"Thirdparty/g2o/g2o/types/types_six_dof_expmap.h" +#include"Thirdparty/g2o/g2o/types/types_seven_dof_expmap.h" + +namespace ORB_SLAM2 +{ + +class Converter +{ +public: + static std::vector toDescriptorVector(const cv::Mat &Descriptors); + + static g2o::SE3Quat toSE3Quat(const cv::Mat &cvT); + static g2o::SE3Quat toSE3Quat(const g2o::Sim3 &gSim3); + + static cv::Mat toCvMat(const g2o::SE3Quat &SE3); + static cv::Mat toCvMat(const g2o::Sim3 &Sim3); + static cv::Mat toCvMat(const Eigen::Matrix &m); + static cv::Mat toCvMat(const Eigen::Matrix3d &m); + static cv::Mat toCvMat(const Eigen::Matrix &m); + static cv::Mat toCvSE3(const Eigen::Matrix &R, const Eigen::Matrix &t); + + static Eigen::Matrix toVector3d(const cv::Mat &cvVector); + static Eigen::Matrix toVector3d(const cv::Point3f &cvPoint); + static Eigen::Matrix toMatrix3d(const cv::Mat &cvMat3); + + static std::vector toQuaternion(const cv::Mat &M); +}; + +}// namespace ORB_SLAM + +#endif // CONVERTER_H diff --git a/for_orb_slam2/include/Frame.h b/for_orb_slam2/include/Frame.h new file mode 100644 index 00000000000..a6a8032f570 --- /dev/null +++ b/for_orb_slam2/include/Frame.h @@ -0,0 +1,213 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#ifndef FRAME_H +#define FRAME_H + +#include + +#include "MapPoint.h" +#include "Thirdparty/DBoW2/DBoW2/BowVector.h" +#include "Thirdparty/DBoW2/DBoW2/FeatureVector.h" +#include "ORBVocabulary.h" +#include "KeyFrame.h" +#include "ORBextractor.h" + +#include + +namespace ORB_SLAM2 +{ +#define FRAME_GRID_ROWS 48 +#define FRAME_GRID_COLS 64 + +class MapPoint; +class KeyFrame; + +class Frame +{ +public: + Frame(); + + // Copy constructor. + Frame(const Frame &frame); + + // Constructor for stereo cameras. + Frame(const cv::Mat &imLeft, const cv::Mat &imRight, const double &timeStamp, ORBextractor* extractorLeft, ORBextractor* extractorRight, ORBVocabulary* voc, cv::Mat &K, cv::Mat &distCoef, const float &bf, const float &thDepth); + + // Constructor for RGB-D cameras. + Frame(const cv::Mat &imGray, const cv::Mat &imDepth, const double &timeStamp, ORBextractor* extractor,ORBVocabulary* voc, cv::Mat &K, cv::Mat &distCoef, const float &bf, const float &thDepth); + + // Constructor for Monocular cameras. + Frame(const cv::Mat &imGray, const double &timeStamp, ORBextractor* extractor,ORBVocabulary* voc, cv::Mat &K, cv::Mat &distCoef, const float &bf, const float &thDepth); + + // Extract ORB on the image. 0 for left image and 1 for right image. + void ExtractORB(int flag, const cv::Mat &im); + + // Compute Bag of Words representation. + void ComputeBoW(); + + // Set the camera pose. + void SetPose(cv::Mat Tcw); + + // Computes rotation, translation and camera center matrices from the camera pose. + void UpdatePoseMatrices(); + + // Returns the camera center. + inline cv::Mat GetCameraCenter(){ + return mOw.clone(); + } + + // Returns inverse of rotation + inline cv::Mat GetRotationInverse(){ + return mRwc.clone(); + } + + // Check if a MapPoint is in the frustum of the camera + // and fill variables of the MapPoint to be used by the tracking + bool isInFrustum(MapPoint* pMP, float viewingCosLimit); + + // Compute the cell of a keypoint (return false if outside the grid) + bool PosInGrid(const cv::KeyPoint &kp, int &posX, int &posY); + + vector GetFeaturesInArea(const float &x, const float &y, const float &r, const int minLevel=-1, const int maxLevel=-1) const; + + // Search a match for each keypoint in the left image to a keypoint in the right image. + // If there is a match, depth is computed and the right coordinate associated to the left keypoint is stored. + void ComputeStereoMatches(); + + // Associate a "right" coordinate to a keypoint if there is valid depth in the depthmap. + void ComputeStereoFromRGBD(const cv::Mat &imDepth); + + // Backprojects a keypoint (if stereo/depth info available) into 3D world coordinates. + cv::Mat UnprojectStereo(const int &i); + +public: + // Vocabulary used for relocalization. + ORBVocabulary* mpORBvocabulary; + + // Feature extractor. The right is used only in the stereo case. + ORBextractor* mpORBextractorLeft, *mpORBextractorRight; + + // Frame timestamp. + double mTimeStamp; + + // Calibration matrix and OpenCV distortion parameters. + cv::Mat mK; + static float fx; + static float fy; + static float cx; + static float cy; + static float invfx; + static float invfy; + cv::Mat mDistCoef; + + // Stereo baseline multiplied by fx. + float mbf; + + // Stereo baseline in meters. + float mb; + + // Threshold close/far points. Close points are inserted from 1 view. + // Far points are inserted as in the monocular case from 2 views. + float mThDepth; + + // Number of KeyPoints. + int N; + + // Vector of keypoints (original for visualization) and undistorted (actually used by the system). + // In the stereo case, mvKeysUn is redundant as images must be rectified. + // In the RGB-D case, RGB images can be distorted. + std::vector mvKeys, mvKeysRight; + std::vector mvKeysUn; + + // Corresponding stereo coordinate and depth for each keypoint. + // "Monocular" keypoints have a negative value. + std::vector mvuRight; + std::vector mvDepth; + + // Bag of Words Vector structures. + DBoW2::BowVector mBowVec; + DBoW2::FeatureVector mFeatVec; + + // ORB descriptor, each row associated to a keypoint. + cv::Mat mDescriptors, mDescriptorsRight; + + // MapPoints associated to keypoints, NULL pointer if no association. + std::vector mvpMapPoints; + + // Flag to identify outlier associations. + std::vector mvbOutlier; + + // Keypoints are assigned to cells in a grid to reduce matching complexity when projecting MapPoints. + static float mfGridElementWidthInv; + static float mfGridElementHeightInv; + std::vector mGrid[FRAME_GRID_COLS][FRAME_GRID_ROWS]; + + // Camera pose. + cv::Mat mTcw; + + // Current and Next Frame id. + static long unsigned int nNextId; + long unsigned int mnId; + + // Reference Keyframe. + KeyFrame* mpReferenceKF; + + // Scale pyramid info. + int mnScaleLevels; + float mfScaleFactor; + float mfLogScaleFactor; + vector mvScaleFactors; + vector mvInvScaleFactors; + vector mvLevelSigma2; + vector mvInvLevelSigma2; + + // Undistorted Image Bounds (computed once). + static float mnMinX; + static float mnMaxX; + static float mnMinY; + static float mnMaxY; + + static bool mbInitialComputations; + + +private: + + // Undistort keypoints given OpenCV distortion parameters. + // Only for the RGB-D case. Stereo must be already rectified! + // (called in the constructor). + void UndistortKeyPoints(); + + // Computes image bounds for the undistorted image (called in the constructor). + void ComputeImageBounds(const cv::Mat &imLeft); + + // Assign keypoints to the grid for speed up feature matching (called in the constructor). + void AssignFeaturesToGrid(); + + // Rotation, translation and camera center + cv::Mat mRcw; + cv::Mat mtcw; + cv::Mat mRwc; + cv::Mat mOw; //==mtwc +}; + +}// namespace ORB_SLAM + +#endif // FRAME_H diff --git a/for_orb_slam2/include/FrameDrawer.h b/for_orb_slam2/include/FrameDrawer.h new file mode 100644 index 00000000000..95c1df9d9ef --- /dev/null +++ b/for_orb_slam2/include/FrameDrawer.h @@ -0,0 +1,73 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#ifndef FRAMEDRAWER_H +#define FRAMEDRAWER_H + +#include "Tracking.h" +#include "MapPoint.h" +#include "Map.h" + +#include +#include + +#include + + +namespace ORB_SLAM2 +{ + +class Tracking; +class Viewer; + +class FrameDrawer +{ +public: + FrameDrawer(Map* pMap); + + // Update info from the last processed frame. + void Update(Tracking *pTracker); + + // Draw last processed frame. + cv::Mat DrawFrame(); + +protected: + + void DrawTextInfo(cv::Mat &im, int nState, cv::Mat &imText); + + // Info of the frame to be drawn + cv::Mat mIm; + int N; + vector mvCurrentKeys; + vector mvbMap, mvbVO; + bool mbOnlyTracking; + int mnTracked, mnTrackedVO; + vector mvIniKeys; + vector mvIniMatches; + int mState; + + Map* mpMap; + + std::mutex mMutex; +}; + +} //namespace ORB_SLAM + +#endif // FRAMEDRAWER_H diff --git a/for_orb_slam2/include/Initializer.h b/for_orb_slam2/include/Initializer.h new file mode 100644 index 00000000000..09b27a65bb0 --- /dev/null +++ b/for_orb_slam2/include/Initializer.h @@ -0,0 +1,101 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ +#ifndef INITIALIZER_H +#define INITIALIZER_H + +#include +#include "Frame.h" + + +namespace ORB_SLAM2 +{ + +// THIS IS THE INITIALIZER FOR MONOCULAR SLAM. NOT USED IN THE STEREO OR RGBD CASE. +class Initializer +{ + typedef pair Match; + +public: + + // Fix the reference frame + Initializer(const Frame &ReferenceFrame, float sigma = 1.0, int iterations = 200); + + // Computes in parallel a fundamental matrix and a homography + // Selects a model and tries to recover the motion and the structure from motion + bool Initialize(const Frame &CurrentFrame, const vector &vMatches12, + cv::Mat &R21, cv::Mat &t21, vector &vP3D, vector &vbTriangulated); + + +private: + + void FindHomography(vector &vbMatchesInliers, float &score, cv::Mat &H21); + void FindFundamental(vector &vbInliers, float &score, cv::Mat &F21); + + cv::Mat ComputeH21(const vector &vP1, const vector &vP2); + cv::Mat ComputeF21(const vector &vP1, const vector &vP2); + + float CheckHomography(const cv::Mat &H21, const cv::Mat &H12, vector &vbMatchesInliers, float sigma); + + float CheckFundamental(const cv::Mat &F21, vector &vbMatchesInliers, float sigma); + + bool ReconstructF(vector &vbMatchesInliers, cv::Mat &F21, cv::Mat &K, + cv::Mat &R21, cv::Mat &t21, vector &vP3D, vector &vbTriangulated, float minParallax, int minTriangulated); + + bool ReconstructH(vector &vbMatchesInliers, cv::Mat &H21, cv::Mat &K, + cv::Mat &R21, cv::Mat &t21, vector &vP3D, vector &vbTriangulated, float minParallax, int minTriangulated); + + void Triangulate(const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Mat &P1, const cv::Mat &P2, cv::Mat &x3D); + + void Normalize(const vector &vKeys, vector &vNormalizedPoints, cv::Mat &T); + + int CheckRT(const cv::Mat &R, const cv::Mat &t, const vector &vKeys1, const vector &vKeys2, + const vector &vMatches12, vector &vbInliers, + const cv::Mat &K, vector &vP3D, float th2, vector &vbGood, float ¶llax); + + void DecomposeE(const cv::Mat &E, cv::Mat &R1, cv::Mat &R2, cv::Mat &t); + + + // Keypoints from Reference Frame (Frame 1) + vector mvKeys1; + + // Keypoints from Current Frame (Frame 2) + vector mvKeys2; + + // Current Matches from Reference to Current + vector mvMatches12; + vector mvbMatched1; + + // Calibration + cv::Mat mK; + + // Standard Deviation and Variance + float mSigma, mSigma2; + + // Ransac max iterations + int mMaxIterations; + + // Ransac sets + vector > mvSets; + +}; + +} //namespace ORB_SLAM + +#endif // INITIALIZER_H diff --git a/for_orb_slam2/include/KeyFrame.h b/for_orb_slam2/include/KeyFrame.h new file mode 100644 index 00000000000..67f4348273b --- /dev/null +++ b/for_orb_slam2/include/KeyFrame.h @@ -0,0 +1,238 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#ifndef KEYFRAME_H +#define KEYFRAME_H + +#include "MapPoint.h" +#include "Thirdparty/DBoW2/DBoW2/BowVector.h" +#include "Thirdparty/DBoW2/DBoW2/FeatureVector.h" +#include "ORBVocabulary.h" +#include "ORBextractor.h" +#include "Frame.h" +#include "KeyFrameDatabase.h" + +#include + + +namespace ORB_SLAM2 +{ + +class Map; +class MapPoint; +class Frame; +class KeyFrameDatabase; + +class KeyFrame +{ +public: + KeyFrame(Frame &F, Map* pMap, KeyFrameDatabase* pKFDB); + + // Pose functions + void SetPose(const cv::Mat &Tcw); + cv::Mat GetPose(); + cv::Mat GetPoseInverse(); + cv::Mat GetCameraCenter(); + cv::Mat GetStereoCenter(); + cv::Mat GetRotation(); + cv::Mat GetTranslation(); + + // Bag of Words Representation + void ComputeBoW(); + + // Covisibility graph functions + void AddConnection(KeyFrame* pKF, const int &weight); + void EraseConnection(KeyFrame* pKF); + void UpdateConnections(); + void UpdateBestCovisibles(); + std::set GetConnectedKeyFrames(); + std::vector GetVectorCovisibleKeyFrames(); + std::vector GetBestCovisibilityKeyFrames(const int &N); + std::vector GetCovisiblesByWeight(const int &w); + int GetWeight(KeyFrame* pKF); + + // Spanning tree functions + void AddChild(KeyFrame* pKF); + void EraseChild(KeyFrame* pKF); + void ChangeParent(KeyFrame* pKF); + std::set GetChilds(); + KeyFrame* GetParent(); + bool hasChild(KeyFrame* pKF); + + // Loop Edges + void AddLoopEdge(KeyFrame* pKF); + std::set GetLoopEdges(); + + // MapPoint observation functions + void AddMapPoint(MapPoint* pMP, const size_t &idx); + void EraseMapPointMatch(const size_t &idx); + void EraseMapPointMatch(MapPoint* pMP); + void ReplaceMapPointMatch(const size_t &idx, MapPoint* pMP); + std::set GetMapPoints(); + std::vector GetMapPointMatches(); + int TrackedMapPoints(const int &minObs); + MapPoint* GetMapPoint(const size_t &idx); + + // KeyPoint functions + std::vector GetFeaturesInArea(const float &x, const float &y, const float &r) const; + cv::Mat UnprojectStereo(int i); + + // Image + bool IsInImage(const float &x, const float &y) const; + + // Enable/Disable bad flag changes + void SetNotErase(); + void SetErase(); + + // Set/check bad flag + void SetBadFlag(); + bool isBad(); + + // Compute Scene Depth (q=2 median). Used in monocular. + float ComputeSceneMedianDepth(const int q); + + static bool weightComp( int a, int b){ + return a>b; + } + + static bool lId(KeyFrame* pKF1, KeyFrame* pKF2){ + return pKF1->mnIdmnId; + } + + + // The following variables are accesed from only 1 thread or never change (no mutex needed). +public: + + static long unsigned int nNextId; + long unsigned int mnId; + const long unsigned int mnFrameId; + + const double mTimeStamp; + + // Grid (to speed up feature matching) + const int mnGridCols; + const int mnGridRows; + const float mfGridElementWidthInv; + const float mfGridElementHeightInv; + + // Variables used by the tracking + long unsigned int mnTrackReferenceForFrame; + long unsigned int mnFuseTargetForKF; + + // Variables used by the local mapping + long unsigned int mnBALocalForKF; + long unsigned int mnBAFixedForKF; + + // Variables used by the keyframe database + long unsigned int mnLoopQuery; + int mnLoopWords; + float mLoopScore; + long unsigned int mnRelocQuery; + int mnRelocWords; + float mRelocScore; + + // Variables used by loop closing + cv::Mat mTcwGBA; + cv::Mat mTcwBefGBA; + long unsigned int mnBAGlobalForKF; + + // Calibration parameters + const float fx, fy, cx, cy, invfx, invfy, mbf, mb, mThDepth; + + // Number of KeyPoints + const int N; + + // KeyPoints, stereo coordinate and descriptors (all associated by an index) + const std::vector mvKeys; + const std::vector mvKeysUn; + const std::vector mvuRight; // negative value for monocular points + const std::vector mvDepth; // negative value for monocular points + const cv::Mat mDescriptors; + + //BoW + DBoW2::BowVector mBowVec; + DBoW2::FeatureVector mFeatVec; + + // Pose relative to parent (this is computed when bad flag is activated) + cv::Mat mTcp; + + // Scale + const int mnScaleLevels; + const float mfScaleFactor; + const float mfLogScaleFactor; + const std::vector mvScaleFactors; + const std::vector mvLevelSigma2; + const std::vector mvInvLevelSigma2; + + // Image bounds and calibration + const int mnMinX; + const int mnMinY; + const int mnMaxX; + const int mnMaxY; + const cv::Mat mK; + + + // The following variables need to be accessed trough a mutex to be thread safe. +protected: + + // SE3 Pose and camera center + cv::Mat Tcw; + cv::Mat Twc; + cv::Mat Ow; + + cv::Mat Cw; // Stereo middel point. Only for visualization + + // MapPoints associated to keypoints + std::vector mvpMapPoints; + + // BoW + KeyFrameDatabase* mpKeyFrameDB; + ORBVocabulary* mpORBvocabulary; + + // Grid over the image to speed up feature matching + std::vector< std::vector > > mGrid; + + std::map mConnectedKeyFrameWeights; + std::vector mvpOrderedConnectedKeyFrames; + std::vector mvOrderedWeights; + + // Spanning Tree and Loop Edges + bool mbFirstConnection; + KeyFrame* mpParent; + std::set mspChildrens; + std::set mspLoopEdges; + + // Bad flags + bool mbNotErase; + bool mbToBeErased; + bool mbBad; + + float mHalfBaseline; // Only for visualization + + Map* mpMap; + + std::mutex mMutexPose; + std::mutex mMutexConnections; + std::mutex mMutexFeatures; +}; + +} //namespace ORB_SLAM + +#endif // KEYFRAME_H diff --git a/for_orb_slam2/include/KeyFrameDatabase.h b/for_orb_slam2/include/KeyFrameDatabase.h new file mode 100644 index 00000000000..fa3735762d4 --- /dev/null +++ b/for_orb_slam2/include/KeyFrameDatabase.h @@ -0,0 +1,74 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#ifndef KEYFRAMEDATABASE_H +#define KEYFRAMEDATABASE_H + +#include +#include +#include + +#include "KeyFrame.h" +#include "Frame.h" +#include "ORBVocabulary.h" + +#include + + +namespace ORB_SLAM2 +{ + +class KeyFrame; +class Frame; + + +class KeyFrameDatabase +{ +public: + + KeyFrameDatabase(const ORBVocabulary &voc); + + void add(KeyFrame* pKF); + + void erase(KeyFrame* pKF); + + void clear(); + + // Loop Detection + std::vector DetectLoopCandidates(KeyFrame* pKF, float minScore); + + // Relocalization + std::vector DetectRelocalizationCandidates(Frame* F); + +protected: + + // Associated vocabulary + const ORBVocabulary* mpVoc; + + // Inverted file + std::vector > mvInvertedFile; + + // Mutex + std::mutex mMutex; +}; + +} //namespace ORB_SLAM + +#endif diff --git a/for_orb_slam2/include/LocalMapping.h b/for_orb_slam2/include/LocalMapping.h new file mode 100644 index 00000000000..1361a5757ab --- /dev/null +++ b/for_orb_slam2/include/LocalMapping.h @@ -0,0 +1,128 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#ifndef LOCALMAPPING_H +#define LOCALMAPPING_H + +#include "KeyFrame.h" +#include "Map.h" +#include "LoopClosing.h" +#include "Tracking.h" +#include "KeyFrameDatabase.h" + +#include + + +namespace ORB_SLAM2 +{ + +class Tracking; +class LoopClosing; +class Map; + +class LocalMapping +{ +public: + LocalMapping(Map* pMap, const float bMonocular); + + void SetLoopCloser(LoopClosing* pLoopCloser); + + void SetTracker(Tracking* pTracker); + + // Main function + void Run(); + + void InsertKeyFrame(KeyFrame* pKF); + + // Thread Synch + void RequestStop(); + void RequestReset(); + bool Stop(); + void Release(); + bool isStopped(); + bool stopRequested(); + bool AcceptKeyFrames(); + void SetAcceptKeyFrames(bool flag); + bool SetNotStop(bool flag); + + void InterruptBA(); + + void RequestFinish(); + bool isFinished(); + + int KeyframesInQueue(){ + unique_lock lock(mMutexNewKFs); + return mlNewKeyFrames.size(); + } + +protected: + + bool CheckNewKeyFrames(); + void ProcessNewKeyFrame(); + void CreateNewMapPoints(); + + void MapPointCulling(); + void SearchInNeighbors(); + + void KeyFrameCulling(); + + cv::Mat ComputeF12(KeyFrame* &pKF1, KeyFrame* &pKF2); + + cv::Mat SkewSymmetricMatrix(const cv::Mat &v); + + bool mbMonocular; + + void ResetIfRequested(); + bool mbResetRequested; + std::mutex mMutexReset; + + bool CheckFinish(); + void SetFinish(); + bool mbFinishRequested; + bool mbFinished; + std::mutex mMutexFinish; + + Map* mpMap; + + LoopClosing* mpLoopCloser; + Tracking* mpTracker; + + std::list mlNewKeyFrames; + + KeyFrame* mpCurrentKeyFrame; + + std::list mlpRecentAddedMapPoints; + + std::mutex mMutexNewKFs; + + bool mbAbortBA; + + bool mbStopped; + bool mbStopRequested; + bool mbNotStop; + std::mutex mMutexStop; + + bool mbAcceptKeyFrames; + std::mutex mMutexAccept; +}; + +} //namespace ORB_SLAM + +#endif // LOCALMAPPING_H diff --git a/for_orb_slam2/include/LoopClosing.h b/for_orb_slam2/include/LoopClosing.h new file mode 100644 index 00000000000..7eb0416b15f --- /dev/null +++ b/for_orb_slam2/include/LoopClosing.h @@ -0,0 +1,151 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#ifndef LOOPCLOSING_H +#define LOOPCLOSING_H + +#include "KeyFrame.h" +#include "LocalMapping.h" +#include "Map.h" +#include "ORBVocabulary.h" +#include "Tracking.h" + +#include "KeyFrameDatabase.h" + +#include +#include +#include "Thirdparty/g2o/g2o/types/types_seven_dof_expmap.h" + +namespace ORB_SLAM2 +{ + +class Tracking; +class LocalMapping; +class KeyFrameDatabase; + + +class LoopClosing +{ +public: + + typedef pair,int> ConsistentGroup; + typedef map, + Eigen::aligned_allocator > > KeyFrameAndPose; + +public: + + LoopClosing(Map* pMap, KeyFrameDatabase* pDB, ORBVocabulary* pVoc,const bool bFixScale); + + void SetTracker(Tracking* pTracker); + + void SetLocalMapper(LocalMapping* pLocalMapper); + + // Main function + void Run(); + + void InsertKeyFrame(KeyFrame *pKF); + + void RequestReset(); + + // This function will run in a separate thread + void RunGlobalBundleAdjustment(unsigned long nLoopKF); + + bool isRunningGBA(){ + unique_lock lock(mMutexGBA); + return mbRunningGBA; + } + bool isFinishedGBA(){ + unique_lock lock(mMutexGBA); + return mbFinishedGBA; + } + + void RequestFinish(); + + bool isFinished(); + + EIGEN_MAKE_ALIGNED_OPERATOR_NEW + +protected: + + bool CheckNewKeyFrames(); + + bool DetectLoop(); + + bool ComputeSim3(); + + void SearchAndFuse(const KeyFrameAndPose &CorrectedPosesMap); + + void CorrectLoop(); + + void ResetIfRequested(); + bool mbResetRequested; + std::mutex mMutexReset; + + bool CheckFinish(); + void SetFinish(); + bool mbFinishRequested; + bool mbFinished; + std::mutex mMutexFinish; + + Map* mpMap; + Tracking* mpTracker; + + KeyFrameDatabase* mpKeyFrameDB; + ORBVocabulary* mpORBVocabulary; + + LocalMapping *mpLocalMapper; + + std::list mlpLoopKeyFrameQueue; + + std::mutex mMutexLoopQueue; + + // Loop detector parameters + float mnCovisibilityConsistencyTh; + + // Loop detector variables + KeyFrame* mpCurrentKF; + KeyFrame* mpMatchedKF; + std::vector mvConsistentGroups; + std::vector mvpEnoughConsistentCandidates; + std::vector mvpCurrentConnectedKFs; + std::vector mvpCurrentMatchedPoints; + std::vector mvpLoopMapPoints; + cv::Mat mScw; + g2o::Sim3 mg2oScw; + + long unsigned int mLastLoopKFid; + + // Variables related to Global Bundle Adjustment + bool mbRunningGBA; + bool mbFinishedGBA; + bool mbStopGBA; + std::mutex mMutexGBA; + std::thread* mpThreadGBA; + + // Fix scale in the stereo/RGB-D case + bool mbFixScale; + + + bool mnFullBAIdx; +}; + +} //namespace ORB_SLAM + +#endif // LOOPCLOSING_H diff --git a/for_orb_slam2/include/Map.h b/for_orb_slam2/include/Map.h new file mode 100644 index 00000000000..a75339feeab --- /dev/null +++ b/for_orb_slam2/include/Map.h @@ -0,0 +1,85 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#ifndef MAP_H +#define MAP_H + +#include "MapPoint.h" +#include "KeyFrame.h" +#include + +#include + + + +namespace ORB_SLAM2 +{ + +class MapPoint; +class KeyFrame; + +class Map +{ +public: + Map(); + + void AddKeyFrame(KeyFrame* pKF); + void AddMapPoint(MapPoint* pMP); + void EraseMapPoint(MapPoint* pMP); + void EraseKeyFrame(KeyFrame* pKF); + void SetReferenceMapPoints(const std::vector &vpMPs); + void InformNewBigChange(); + int GetLastBigChangeIdx(); + + std::vector GetAllKeyFrames(); + std::vector GetAllMapPoints(); + std::vector GetReferenceMapPoints(); + + long unsigned int MapPointsInMap(); + long unsigned KeyFramesInMap(); + + long unsigned int GetMaxKFid(); + + void clear(); + + vector mvpKeyFrameOrigins; + + std::mutex mMutexMapUpdate; + + // This avoid that two points are created simultaneously in separate threads (id conflict) + std::mutex mMutexPointCreation; + +protected: + std::set mspMapPoints; + std::set mspKeyFrames; + + std::vector mvpReferenceMapPoints; + + long unsigned int mnMaxKFid; + + // Index related to a big change in the map (loop closure, global BA) + int mnBigChangeIdx; + + std::mutex mMutexMap; +}; + +} //namespace ORB_SLAM + +#endif // MAP_H diff --git a/for_orb_slam2/include/MapDrawer.h b/for_orb_slam2/include/MapDrawer.h new file mode 100644 index 00000000000..83b282676d3 --- /dev/null +++ b/for_orb_slam2/include/MapDrawer.h @@ -0,0 +1,64 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#ifndef MAPDRAWER_H +#define MAPDRAWER_H + +#include"Map.h" +#include"MapPoint.h" +#include"KeyFrame.h" +#include + +#include + +namespace ORB_SLAM2 +{ + +class MapDrawer +{ +public: + MapDrawer(Map* pMap, const string &strSettingPath); + + Map* mpMap; + + void DrawMapPoints(); + void DrawKeyFrames(const bool bDrawKF, const bool bDrawGraph); + void DrawCurrentCamera(pangolin::OpenGlMatrix &Twc); + void SetCurrentCameraPose(const cv::Mat &Tcw); + void SetReferenceKeyFrame(KeyFrame *pKF); + void GetCurrentOpenGLCameraMatrix(pangolin::OpenGlMatrix &M); + +private: + + float mKeyFrameSize; + float mKeyFrameLineWidth; + float mGraphLineWidth; + float mPointSize; + float mCameraSize; + float mCameraLineWidth; + + cv::Mat mCameraPose; + + std::mutex mMutexCamera; +}; + +} //namespace ORB_SLAM + +#endif // MAPDRAWER_H diff --git a/for_orb_slam2/include/MapPoint.h b/for_orb_slam2/include/MapPoint.h new file mode 100644 index 00000000000..f26893d89d6 --- /dev/null +++ b/for_orb_slam2/include/MapPoint.h @@ -0,0 +1,152 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#ifndef MAPPOINT_H +#define MAPPOINT_H + +#include"KeyFrame.h" +#include"Frame.h" +#include"Map.h" + +#include +#include + +namespace ORB_SLAM2 +{ + +class KeyFrame; +class Map; +class Frame; + + +class MapPoint +{ +public: + MapPoint(const cv::Mat &Pos, KeyFrame* pRefKF, Map* pMap); + MapPoint(const cv::Mat &Pos, Map* pMap, Frame* pFrame, const int &idxF); + + void SetWorldPos(const cv::Mat &Pos); + cv::Mat GetWorldPos(); + + cv::Mat GetNormal(); + KeyFrame* GetReferenceKeyFrame(); + + std::map GetObservations(); + int Observations(); + + void AddObservation(KeyFrame* pKF,size_t idx); + void EraseObservation(KeyFrame* pKF); + + int GetIndexInKeyFrame(KeyFrame* pKF); + bool IsInKeyFrame(KeyFrame* pKF); + + void SetBadFlag(); + bool isBad(); + + void Replace(MapPoint* pMP); + MapPoint* GetReplaced(); + + void IncreaseVisible(int n=1); + void IncreaseFound(int n=1); + float GetFoundRatio(); + inline int GetFound(){ + return mnFound; + } + + void ComputeDistinctiveDescriptors(); + + cv::Mat GetDescriptor(); + + void UpdateNormalAndDepth(); + + float GetMinDistanceInvariance(); + float GetMaxDistanceInvariance(); + int PredictScale(const float ¤tDist, KeyFrame*pKF); + int PredictScale(const float ¤tDist, Frame* pF); + +public: + long unsigned int mnId; + static long unsigned int nNextId; + long int mnFirstKFid; + long int mnFirstFrame; + int nObs; + + // Variables used by the tracking + float mTrackProjX; + float mTrackProjY; + float mTrackProjXR; + bool mbTrackInView; + int mnTrackScaleLevel; + float mTrackViewCos; + long unsigned int mnTrackReferenceForFrame; + long unsigned int mnLastFrameSeen; + + // Variables used by local mapping + long unsigned int mnBALocalForKF; + long unsigned int mnFuseCandidateForKF; + + // Variables used by loop closing + long unsigned int mnLoopPointForKF; + long unsigned int mnCorrectedByKF; + long unsigned int mnCorrectedReference; + cv::Mat mPosGBA; + long unsigned int mnBAGlobalForKF; + + + static std::mutex mGlobalMutex; + +protected: + + // Position in absolute coordinates + cv::Mat mWorldPos; + + // Keyframes observing the point and associated index in keyframe + std::map mObservations; + + // Mean viewing direction + cv::Mat mNormalVector; + + // Best descriptor to fast matching + cv::Mat mDescriptor; + + // Reference KeyFrame + KeyFrame* mpRefKF; + + // Tracking counters + int mnVisible; + int mnFound; + + // Bad flag (we do not currently erase MapPoint from memory) + bool mbBad; + MapPoint* mpReplaced; + + // Scale invariance distances + float mfMinDistance; + float mfMaxDistance; + + Map* mpMap; + + std::mutex mMutexPos; + std::mutex mMutexFeatures; +}; + +} //namespace ORB_SLAM + +#endif // MAPPOINT_H diff --git a/for_orb_slam2/include/ORBVocabulary.h b/for_orb_slam2/include/ORBVocabulary.h new file mode 100644 index 00000000000..ca5fa8d7778 --- /dev/null +++ b/for_orb_slam2/include/ORBVocabulary.h @@ -0,0 +1,36 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + + +#ifndef ORBVOCABULARY_H +#define ORBVOCABULARY_H + +#include"Thirdparty/DBoW2/DBoW2/FORB.h" +#include"Thirdparty/DBoW2/DBoW2/TemplatedVocabulary.h" + +namespace ORB_SLAM2 +{ + +typedef DBoW2::TemplatedVocabulary + ORBVocabulary; + +} //namespace ORB_SLAM + +#endif // ORBVOCABULARY_H diff --git a/for_orb_slam2/include/ORBextractor.h b/for_orb_slam2/include/ORBextractor.h new file mode 100644 index 00000000000..66e8e7a547f --- /dev/null +++ b/for_orb_slam2/include/ORBextractor.h @@ -0,0 +1,116 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#ifndef ORBEXTRACTOR_H +#define ORBEXTRACTOR_H + +#include +#include +#include + + +namespace ORB_SLAM2 +{ + +class ExtractorNode +{ +public: + ExtractorNode():bNoMore(false){} + + void DivideNode(ExtractorNode &n1, ExtractorNode &n2, ExtractorNode &n3, ExtractorNode &n4); + + std::vector vKeys; + cv::Point2i UL, UR, BL, BR; + std::list::iterator lit; + bool bNoMore; +}; + +class ORBextractor +{ +public: + + enum {HARRIS_SCORE=0, FAST_SCORE=1 }; + + ORBextractor(int nfeatures, float scaleFactor, int nlevels, + int iniThFAST, int minThFAST); + + ~ORBextractor(){} + + // Compute the ORB features and descriptors on an image. + // ORB are dispersed on the image using an octree. + // Mask is ignored in the current implementation. + void operator()( cv::InputArray image, cv::InputArray mask, + std::vector& keypoints, + cv::OutputArray descriptors); + + int inline GetLevels(){ + return nlevels;} + + float inline GetScaleFactor(){ + return scaleFactor;} + + std::vector inline GetScaleFactors(){ + return mvScaleFactor; + } + + std::vector inline GetInverseScaleFactors(){ + return mvInvScaleFactor; + } + + std::vector inline GetScaleSigmaSquares(){ + return mvLevelSigma2; + } + + std::vector inline GetInverseScaleSigmaSquares(){ + return mvInvLevelSigma2; + } + + std::vector mvImagePyramid; + +protected: + + void ComputePyramid(cv::Mat image); + void ComputeKeyPointsOctTree(std::vector >& allKeypoints); + std::vector DistributeOctTree(const std::vector& vToDistributeKeys, const int &minX, + const int &maxX, const int &minY, const int &maxY, const int &nFeatures, const int &level); + + void ComputeKeyPointsOld(std::vector >& allKeypoints); + std::vector pattern; + + int nfeatures; + double scaleFactor; + int nlevels; + int iniThFAST; + int minThFAST; + + std::vector mnFeaturesPerLevel; + + std::vector umax; + + std::vector mvScaleFactor; + std::vector mvInvScaleFactor; + std::vector mvLevelSigma2; + std::vector mvInvLevelSigma2; +}; + +} //namespace ORB_SLAM + +#endif + diff --git a/for_orb_slam2/include/ORBmatcher.h b/for_orb_slam2/include/ORBmatcher.h new file mode 100644 index 00000000000..ad881ee04ae --- /dev/null +++ b/for_orb_slam2/include/ORBmatcher.h @@ -0,0 +1,106 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + + +#ifndef ORBMATCHER_H +#define ORBMATCHER_H + +#include +#include +#include + +#include"MapPoint.h" +#include"KeyFrame.h" +#include"Frame.h" + + +namespace ORB_SLAM2 +{ + +class ORBmatcher +{ +public: + + ORBmatcher(float nnratio=0.6, bool checkOri=true); + + // Computes the Hamming distance between two ORB descriptors + static int DescriptorDistance(const cv::Mat &a, const cv::Mat &b); + + // Search matches between Frame keypoints and projected MapPoints. Returns number of matches + // Used to track the local map (Tracking) + int SearchByProjection(Frame &F, const std::vector &vpMapPoints, const float th=3); + + // Project MapPoints tracked in last frame into the current frame and search matches. + // Used to track from previous frame (Tracking) + int SearchByProjection(Frame &CurrentFrame, const Frame &LastFrame, const float th, const bool bMono); + + // Project MapPoints seen in KeyFrame into the Frame and search matches. + // Used in relocalisation (Tracking) + int SearchByProjection(Frame &CurrentFrame, KeyFrame* pKF, const std::set &sAlreadyFound, const float th, const int ORBdist); + + // Project MapPoints using a Similarity Transformation and search matches. + // Used in loop detection (Loop Closing) + int SearchByProjection(KeyFrame* pKF, cv::Mat Scw, const std::vector &vpPoints, std::vector &vpMatched, int th); + + // Search matches between MapPoints in a KeyFrame and ORB in a Frame. + // Brute force constrained to ORB that belong to the same vocabulary node (at a certain level) + // Used in Relocalisation and Loop Detection + int SearchByBoW(KeyFrame *pKF, Frame &F, std::vector &vpMapPointMatches); + int SearchByBoW(KeyFrame *pKF1, KeyFrame* pKF2, std::vector &vpMatches12); + + // Matching for the Map Initialization (only used in the monocular case) + int SearchForInitialization(Frame &F1, Frame &F2, std::vector &vbPrevMatched, std::vector &vnMatches12, int windowSize=10); + + // Matching to triangulate new MapPoints. Check Epipolar Constraint. + int SearchForTriangulation(KeyFrame *pKF1, KeyFrame* pKF2, cv::Mat F12, + std::vector > &vMatchedPairs, const bool bOnlyStereo); + + // Search matches between MapPoints seen in KF1 and KF2 transforming by a Sim3 [s12*R12|t12] + // In the stereo and RGB-D case, s12=1 + int SearchBySim3(KeyFrame* pKF1, KeyFrame* pKF2, std::vector &vpMatches12, const float &s12, const cv::Mat &R12, const cv::Mat &t12, const float th); + + // Project MapPoints into KeyFrame and search for duplicated MapPoints. + int Fuse(KeyFrame* pKF, const vector &vpMapPoints, const float th=3.0); + + // Project MapPoints into KeyFrame using a given Sim3 and search for duplicated MapPoints. + int Fuse(KeyFrame* pKF, cv::Mat Scw, const std::vector &vpPoints, float th, vector &vpReplacePoint); + +public: + + static const int TH_LOW; + static const int TH_HIGH; + static const int HISTO_LENGTH; + + +protected: + + bool CheckDistEpipolarLine(const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Mat &F12, const KeyFrame *pKF); + + float RadiusByViewingCos(const float &viewCos); + + void ComputeThreeMaxima(std::vector* histo, const int L, int &ind1, int &ind2, int &ind3); + + float mfNNratio; + bool mbCheckOrientation; +}; + +}// namespace ORB_SLAM + +#endif // ORBMATCHER_H diff --git a/for_orb_slam2/include/Optimizer.h b/for_orb_slam2/include/Optimizer.h new file mode 100644 index 00000000000..2c4378ed3b5 --- /dev/null +++ b/for_orb_slam2/include/Optimizer.h @@ -0,0 +1,62 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#ifndef OPTIMIZER_H +#define OPTIMIZER_H + +#include "Map.h" +#include "MapPoint.h" +#include "KeyFrame.h" +#include "LoopClosing.h" +#include "Frame.h" + +#include "Thirdparty/g2o/g2o/types/types_seven_dof_expmap.h" + +namespace ORB_SLAM2 +{ + +class LoopClosing; + +class Optimizer +{ +public: + void static BundleAdjustment(const std::vector &vpKF, const std::vector &vpMP, + int nIterations = 5, bool *pbStopFlag=NULL, const unsigned long nLoopKF=0, + const bool bRobust = true); + void static GlobalBundleAdjustemnt(Map* pMap, int nIterations=5, bool *pbStopFlag=NULL, + const unsigned long nLoopKF=0, const bool bRobust = true); + void static LocalBundleAdjustment(KeyFrame* pKF, bool *pbStopFlag, Map *pMap); + int static PoseOptimization(Frame* pFrame); + + // if bFixScale is true, 6DoF optimization (stereo,rgbd), 7DoF otherwise (mono) + void static OptimizeEssentialGraph(Map* pMap, KeyFrame* pLoopKF, KeyFrame* pCurKF, + const LoopClosing::KeyFrameAndPose &NonCorrectedSim3, + const LoopClosing::KeyFrameAndPose &CorrectedSim3, + const map > &LoopConnections, + const bool &bFixScale); + + // if bFixScale is true, optimize SE3 (stereo,rgbd), Sim3 otherwise (mono) + static int OptimizeSim3(KeyFrame* pKF1, KeyFrame* pKF2, std::vector &vpMatches1, + g2o::Sim3 &g2oS12, const float th2, const bool bFixScale); +}; + +} //namespace ORB_SLAM + +#endif // OPTIMIZER_H diff --git a/for_orb_slam2/include/PnPsolver.h b/for_orb_slam2/include/PnPsolver.h new file mode 100644 index 00000000000..f92544fc8fe --- /dev/null +++ b/for_orb_slam2/include/PnPsolver.h @@ -0,0 +1,198 @@ +/** +* This file is part of ORB-SLAM2. +* This file is a modified version of EPnP , see FreeBSD license below. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +/** +* Copyright (c) 2009, V. Lepetit, EPFL +* All rights reserved. +* +* Redistribution and use in source and binary forms, with or without +* modification, are permitted provided that the following conditions are met: +* +* 1. Redistributions of source code must retain the above copyright notice, this +* list of conditions and the following disclaimer. +* 2. Redistributions in binary form must reproduce the above copyright notice, +* this list of conditions and the following disclaimer in the documentation +* and/or other materials provided with the distribution. +* +* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR +* ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +* +* The views and conclusions contained in the software and documentation are those +* of the authors and should not be interpreted as representing official policies, +* either expressed or implied, of the FreeBSD Project +*/ + +#ifndef PNPSOLVER_H +#define PNPSOLVER_H + +#include +#include "MapPoint.h" +#include "Frame.h" + +namespace ORB_SLAM2 +{ + +class PnPsolver { + public: + PnPsolver(const Frame &F, const vector &vpMapPointMatches); + + ~PnPsolver(); + + void SetRansacParameters(double probability = 0.99, int minInliers = 8 , int maxIterations = 300, int minSet = 4, float epsilon = 0.4, + float th2 = 5.991); + + cv::Mat find(vector &vbInliers, int &nInliers); + + cv::Mat iterate(int nIterations, bool &bNoMore, vector &vbInliers, int &nInliers); + + private: + + void CheckInliers(); + bool Refine(); + + // Functions from the original EPnP code + void set_maximum_number_of_correspondences(const int n); + void reset_correspondences(void); + void add_correspondence(const double X, const double Y, const double Z, + const double u, const double v); + + double compute_pose(double R[3][3], double T[3]); + + void relative_error(double & rot_err, double & transl_err, + const double Rtrue[3][3], const double ttrue[3], + const double Rest[3][3], const double test[3]); + + void print_pose(const double R[3][3], const double t[3]); + double reprojection_error(const double R[3][3], const double t[3]); + + void choose_control_points(void); + void compute_barycentric_coordinates(void); + void fill_M(CvMat * M, const int row, const double * alphas, const double u, const double v); + void compute_ccs(const double * betas, const double * ut); + void compute_pcs(void); + + void solve_for_sign(void); + + void find_betas_approx_1(const CvMat * L_6x10, const CvMat * Rho, double * betas); + void find_betas_approx_2(const CvMat * L_6x10, const CvMat * Rho, double * betas); + void find_betas_approx_3(const CvMat * L_6x10, const CvMat * Rho, double * betas); + void qr_solve(CvMat * A, CvMat * b, CvMat * X); + + double dot(const double * v1, const double * v2); + double dist2(const double * p1, const double * p2); + + void compute_rho(double * rho); + void compute_L_6x10(const double * ut, double * l_6x10); + + void gauss_newton(const CvMat * L_6x10, const CvMat * Rho, double current_betas[4]); + void compute_A_and_b_gauss_newton(const double * l_6x10, const double * rho, + double cb[4], CvMat * A, CvMat * b); + + double compute_R_and_t(const double * ut, const double * betas, + double R[3][3], double t[3]); + + void estimate_R_and_t(double R[3][3], double t[3]); + + void copy_R_and_t(const double R_dst[3][3], const double t_dst[3], + double R_src[3][3], double t_src[3]); + + void mat_to_quat(const double R[3][3], double q[4]); + + + double uc, vc, fu, fv; + + double * pws, * us, * alphas, * pcs; + int maximum_number_of_correspondences; + int number_of_correspondences; + + double cws[4][3], ccs[4][3]; + double cws_determinant; + + vector mvpMapPointMatches; + + // 2D Points + vector mvP2D; + vector mvSigma2; + + // 3D Points + vector mvP3Dw; + + // Index in Frame + vector mvKeyPointIndices; + + // Current Estimation + double mRi[3][3]; + double mti[3]; + cv::Mat mTcwi; + vector mvbInliersi; + int mnInliersi; + + // Current Ransac State + int mnIterations; + vector mvbBestInliers; + int mnBestInliers; + cv::Mat mBestTcw; + + // Refined + cv::Mat mRefinedTcw; + vector mvbRefinedInliers; + int mnRefinedInliers; + + // Number of Correspondences + int N; + + // Indices for random selection [0 .. N-1] + vector mvAllIndices; + + // RANSAC probability + double mRansacProb; + + // RANSAC min inliers + int mRansacMinInliers; + + // RANSAC max iterations + int mRansacMaxIts; + + // RANSAC expected inliers/total ratio + float mRansacEpsilon; + + // RANSAC Threshold inlier/outlier. Max error e = dist(P1,T_12*P2)^2 + float mRansacTh; + + // RANSAC Minimun Set used at each iteration + int mRansacMinSet; + + // Max square error associated with scale level. Max error = th*th*sigma(level)*sigma(level) + vector mvMaxError; + +}; + +} //namespace ORB_SLAM + +#endif //PNPSOLVER_H diff --git a/for_orb_slam2/include/Sim3Solver.h b/for_orb_slam2/include/Sim3Solver.h new file mode 100644 index 00000000000..9af66cb26fb --- /dev/null +++ b/for_orb_slam2/include/Sim3Solver.h @@ -0,0 +1,133 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + + +#ifndef SIM3SOLVER_H +#define SIM3SOLVER_H + +#include +#include + +#include "KeyFrame.h" + + + +namespace ORB_SLAM2 +{ + +class Sim3Solver +{ +public: + + Sim3Solver(KeyFrame* pKF1, KeyFrame* pKF2, const std::vector &vpMatched12, const bool bFixScale = true); + + void SetRansacParameters(double probability = 0.99, int minInliers = 6 , int maxIterations = 300); + + cv::Mat find(std::vector &vbInliers12, int &nInliers); + + cv::Mat iterate(int nIterations, bool &bNoMore, std::vector &vbInliers, int &nInliers); + + cv::Mat GetEstimatedRotation(); + cv::Mat GetEstimatedTranslation(); + float GetEstimatedScale(); + + +protected: + + void ComputeCentroid(cv::Mat &P, cv::Mat &Pr, cv::Mat &C); + + void ComputeSim3(cv::Mat &P1, cv::Mat &P2); + + void CheckInliers(); + + void Project(const std::vector &vP3Dw, std::vector &vP2D, cv::Mat Tcw, cv::Mat K); + void FromCameraToImage(const std::vector &vP3Dc, std::vector &vP2D, cv::Mat K); + + +protected: + + // KeyFrames and matches + KeyFrame* mpKF1; + KeyFrame* mpKF2; + + std::vector mvX3Dc1; + std::vector mvX3Dc2; + std::vector mvpMapPoints1; + std::vector mvpMapPoints2; + std::vector mvpMatches12; + std::vector mvnIndices1; + std::vector mvSigmaSquare1; + std::vector mvSigmaSquare2; + std::vector mvnMaxError1; + std::vector mvnMaxError2; + + int N; + int mN1; + + // Current Estimation + cv::Mat mR12i; + cv::Mat mt12i; + float ms12i; + cv::Mat mT12i; + cv::Mat mT21i; + std::vector mvbInliersi; + int mnInliersi; + + // Current Ransac State + int mnIterations; + std::vector mvbBestInliers; + int mnBestInliers; + cv::Mat mBestT12; + cv::Mat mBestRotation; + cv::Mat mBestTranslation; + float mBestScale; + + // Scale is fixed to 1 in the stereo/RGBD case + bool mbFixScale; + + // Indices for random selection + std::vector mvAllIndices; + + // Projections + std::vector mvP1im1; + std::vector mvP2im2; + + // RANSAC probability + double mRansacProb; + + // RANSAC min inliers + int mRansacMinInliers; + + // RANSAC max iterations + int mRansacMaxIts; + + // Threshold inlier/outlier. e = dist(Pi,T_ij*Pj)^2 < 5.991*mSigma2 + float mTh; + float mSigma2; + + // Calibration + cv::Mat mK1; + cv::Mat mK2; + +}; + +} //namespace ORB_SLAM + +#endif // SIM3SOLVER_H diff --git a/for_orb_slam2/include/System.h b/for_orb_slam2/include/System.h new file mode 100644 index 00000000000..b377b453d1c --- /dev/null +++ b/for_orb_slam2/include/System.h @@ -0,0 +1,181 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + + +#ifndef SYSTEM_H +#define SYSTEM_H + +#include +#include +#include + +#include "Tracking.h" +#include "FrameDrawer.h" +#include "MapDrawer.h" +#include "Map.h" +#include "LocalMapping.h" +#include "LoopClosing.h" +#include "KeyFrameDatabase.h" +#include "ORBVocabulary.h" +#include "Viewer.h" + +namespace ORB_SLAM2 +{ + +class Viewer; +class FrameDrawer; +class Map; +class Tracking; +class LocalMapping; +class LoopClosing; + +class System +{ +public: + // Input sensor + enum eSensor{ + MONOCULAR=0, + STEREO=1, + RGBD=2 + }; + +public: + + // Initialize the SLAM system. It launches the Local Mapping, Loop Closing and Viewer threads. + System(const string &strVocFile, const string &strSettingsFile, const eSensor sensor, const bool bUseViewer = true); + + // Proccess the given stereo frame. Images must be synchronized and rectified. + // Input images: RGB (CV_8UC3) or grayscale (CV_8U). RGB is converted to grayscale. + // Returns the camera pose (empty if tracking fails). + cv::Mat TrackStereo(const cv::Mat &imLeft, const cv::Mat &imRight, const double ×tamp); + + // Process the given rgbd frame. Depthmap must be registered to the RGB frame. + // Input image: RGB (CV_8UC3) or grayscale (CV_8U). RGB is converted to grayscale. + // Input depthmap: Float (CV_32F). + // Returns the camera pose (empty if tracking fails). + cv::Mat TrackRGBD(const cv::Mat &im, const cv::Mat &depthmap, const double ×tamp); + + // Proccess the given monocular frame + // Input images: RGB (CV_8UC3) or grayscale (CV_8U). RGB is converted to grayscale. + // Returns the camera pose (empty if tracking fails). + cv::Mat TrackMonocular(const cv::Mat &im, const double ×tamp); + + // This stops local mapping thread (map building) and performs only camera tracking. + void ActivateLocalizationMode(); + // This resumes local mapping thread and performs SLAM again. + void DeactivateLocalizationMode(); + + // Returns true if there have been a big map change (loop closure, global BA) + // since last call to this function + bool MapChanged(); + + // Reset the system (clear map) + void Reset(); + + // All threads will be requested to finish. + // It waits until all threads have finished. + // This function must be called before saving the trajectory. + void Shutdown(); + + // Save camera trajectory in the TUM RGB-D dataset format. + // Only for stereo and RGB-D. This method does not work for monocular. + // Call first Shutdown() + // See format details at: http://vision.in.tum.de/data/datasets/rgbd-dataset + void SaveTrajectoryTUM(const string &filename); + + // Save keyframe poses in the TUM RGB-D dataset format. + // This method works for all sensor input. + // Call first Shutdown() + // See format details at: http://vision.in.tum.de/data/datasets/rgbd-dataset + void SaveKeyFrameTrajectoryTUM(const string &filename); + + // Save camera trajectory in the KITTI dataset format. + // Only for stereo and RGB-D. This method does not work for monocular. + // Call first Shutdown() + // See format details at: http://www.cvlibs.net/datasets/kitti/eval_odometry.php + void SaveTrajectoryKITTI(const string &filename); + + // TODO: Save/Load functions + // SaveMap(const string &filename); + // LoadMap(const string &filename); + + // Information from most recent processed frame + // You can call this right after TrackMonocular (or stereo or RGBD) + int GetTrackingState(); + std::vector GetTrackedMapPoints(); + std::vector GetTrackedKeyPointsUn(); + +private: + + // Input sensor + eSensor mSensor; + + // ORB vocabulary used for place recognition and feature matching. + ORBVocabulary* mpVocabulary; + + // KeyFrame database for place recognition (relocalization and loop detection). + KeyFrameDatabase* mpKeyFrameDatabase; + + // Map structure that stores the pointers to all KeyFrames and MapPoints. + Map* mpMap; + + // Tracker. It receives a frame and computes the associated camera pose. + // It also decides when to insert a new keyframe, create some new MapPoints and + // performs relocalization if tracking fails. + Tracking* mpTracker; + + // Local Mapper. It manages the local map and performs local bundle adjustment. + LocalMapping* mpLocalMapper; + + // Loop Closer. It searches loops with every new keyframe. If there is a loop it performs + // a pose graph optimization and full bundle adjustment (in a new thread) afterwards. + LoopClosing* mpLoopCloser; + + // The viewer draws the map and the current camera pose. It uses Pangolin. + Viewer* mpViewer; + + FrameDrawer* mpFrameDrawer; + MapDrawer* mpMapDrawer; + + // System threads: Local Mapping, Loop Closing, Viewer. + // The Tracking thread "lives" in the main execution thread that creates the System object. + std::thread* mptLocalMapping; + std::thread* mptLoopClosing; + std::thread* mptViewer; + + // Reset flag + std::mutex mMutexReset; + bool mbReset; + + // Change mode flags + std::mutex mMutexMode; + bool mbActivateLocalizationMode; + bool mbDeactivateLocalizationMode; + + // Tracking state + int mTrackingState; + std::vector mTrackedMapPoints; + std::vector mTrackedKeyPointsUn; + std::mutex mMutexState; +}; + +}// namespace ORB_SLAM + +#endif // SYSTEM_H diff --git a/for_orb_slam2/include/Tracking.h b/for_orb_slam2/include/Tracking.h new file mode 100644 index 00000000000..5aaa93ef264 --- /dev/null +++ b/for_orb_slam2/include/Tracking.h @@ -0,0 +1,221 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + + +#ifndef TRACKING_H +#define TRACKING_H + +#include +#include + +#include"Viewer.h" +#include"FrameDrawer.h" +#include"Map.h" +#include"LocalMapping.h" +#include"LoopClosing.h" +#include"Frame.h" +#include "ORBVocabulary.h" +#include"KeyFrameDatabase.h" +#include"ORBextractor.h" +#include "Initializer.h" +#include "MapDrawer.h" +#include "System.h" + +#include + +namespace ORB_SLAM2 +{ + +class Viewer; +class FrameDrawer; +class Map; +class LocalMapping; +class LoopClosing; +class System; + +class Tracking +{ + +public: + Tracking(System* pSys, ORBVocabulary* pVoc, FrameDrawer* pFrameDrawer, MapDrawer* pMapDrawer, Map* pMap, + KeyFrameDatabase* pKFDB, const string &strSettingPath, const int sensor); + + // Preprocess the input and call Track(). Extract features and performs stereo matching. + cv::Mat GrabImageStereo(const cv::Mat &imRectLeft,const cv::Mat &imRectRight, const double ×tamp); + cv::Mat GrabImageRGBD(const cv::Mat &imRGB,const cv::Mat &imD, const double ×tamp); + cv::Mat GrabImageMonocular(const cv::Mat &im, const double ×tamp); + + void SetLocalMapper(LocalMapping* pLocalMapper); + void SetLoopClosing(LoopClosing* pLoopClosing); + void SetViewer(Viewer* pViewer); + + // Load new settings + // The focal lenght should be similar or scale prediction will fail when projecting points + // TODO: Modify MapPoint::PredictScale to take into account focal lenght + void ChangeCalibration(const string &strSettingPath); + + // Use this function if you have deactivated local mapping and you only want to localize the camera. + void InformOnlyTracking(const bool &flag); + + +public: + + // Tracking states + enum eTrackingState{ + SYSTEM_NOT_READY=-1, + NO_IMAGES_YET=0, + NOT_INITIALIZED=1, + OK=2, + LOST=3 + }; + + eTrackingState mState; + eTrackingState mLastProcessedState; + + // Input sensor + int mSensor; + + // Current Frame + Frame mCurrentFrame; + cv::Mat mImGray; + + // Initialization Variables (Monocular) + std::vector mvIniLastMatches; + std::vector mvIniMatches; + std::vector mvbPrevMatched; + std::vector mvIniP3D; + Frame mInitialFrame; + + // Lists used to recover the full camera trajectory at the end of the execution. + // Basically we store the reference keyframe for each frame and its relative transformation + list mlRelativeFramePoses; + list mlpReferences; + list mlFrameTimes; + list mlbLost; + + // True if local mapping is deactivated and we are performing only localization + bool mbOnlyTracking; + + void Reset(); + +protected: + + // Main tracking function. It is independent of the input sensor. + void Track(); + + // Map initialization for stereo and RGB-D + void StereoInitialization(); + + // Map initialization for monocular + void MonocularInitialization(); + void CreateInitialMapMonocular(); + + void CheckReplacedInLastFrame(); + bool TrackReferenceKeyFrame(); + void UpdateLastFrame(); + bool TrackWithMotionModel(); + + bool Relocalization(); + + void UpdateLocalMap(); + void UpdateLocalPoints(); + void UpdateLocalKeyFrames(); + + bool TrackLocalMap(); + void SearchLocalPoints(); + + bool NeedNewKeyFrame(); + void CreateNewKeyFrame(); + + // In case of performing only localization, this flag is true when there are no matches to + // points in the map. Still tracking will continue if there are enough matches with temporal points. + // In that case we are doing visual odometry. The system will try to do relocalization to recover + // "zero-drift" localization to the map. + bool mbVO; + + //Other Thread Pointers + LocalMapping* mpLocalMapper; + LoopClosing* mpLoopClosing; + + //ORB + ORBextractor* mpORBextractorLeft, *mpORBextractorRight; + ORBextractor* mpIniORBextractor; + + //BoW + ORBVocabulary* mpORBVocabulary; + KeyFrameDatabase* mpKeyFrameDB; + + // Initalization (only for monocular) + Initializer* mpInitializer; + + //Local Map + KeyFrame* mpReferenceKF; + std::vector mvpLocalKeyFrames; + std::vector mvpLocalMapPoints; + + // System + System* mpSystem; + + //Drawers + Viewer* mpViewer; + FrameDrawer* mpFrameDrawer; + MapDrawer* mpMapDrawer; + + //Map + Map* mpMap; + + //Calibration matrix + cv::Mat mK; + cv::Mat mDistCoef; + float mbf; + + //New KeyFrame rules (according to fps) + int mMinFrames; + int mMaxFrames; + + // Threshold close/far points + // Points seen as close by the stereo/RGBD sensor are considered reliable + // and inserted from just one frame. Far points requiere a match in two keyframes. + float mThDepth; + + // For RGB-D inputs only. For some datasets (e.g. TUM) the depthmap values are scaled. + float mDepthMapFactor; + + //Current matches in frame + int mnMatchesInliers; + + //Last Frame, KeyFrame and Relocalisation Info + KeyFrame* mpLastKeyFrame; + Frame mLastFrame; + unsigned int mnLastKeyFrameId; + unsigned int mnLastRelocFrameId; + + //Motion Model + cv::Mat mVelocity; + + //Color order (true RGB, false BGR, ignored if grayscale) + bool mbRGB; + + list mlpTemporalPoints; +}; + +} //namespace ORB_SLAM + +#endif // TRACKING_H diff --git a/for_orb_slam2/include/Viewer.h b/for_orb_slam2/include/Viewer.h new file mode 100644 index 00000000000..251e223c54f --- /dev/null +++ b/for_orb_slam2/include/Viewer.h @@ -0,0 +1,91 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + + +#ifndef VIEWER_H +#define VIEWER_H + +#include "FrameDrawer.h" +#include "MapDrawer.h" +#include "Tracking.h" +#include "System.h" + +#include + +namespace ORB_SLAM2 +{ + +class Tracking; +class FrameDrawer; +class MapDrawer; +class System; + +class Viewer +{ +public: + Viewer(System* pSystem, FrameDrawer* pFrameDrawer, MapDrawer* pMapDrawer, Tracking *pTracking, const string &strSettingPath); + + // Main thread function. Draw points, keyframes, the current camera pose and the last processed + // frame. Drawing is refreshed according to the camera fps. We use Pangolin. + void Run(); + + void RequestFinish(); + + void RequestStop(); + + bool isFinished(); + + bool isStopped(); + + void Release(); + +private: + + bool Stop(); + + System* mpSystem; + FrameDrawer* mpFrameDrawer; + MapDrawer* mpMapDrawer; + Tracking* mpTracker; + + // 1/fps in ms + double mT; + float mImageWidth, mImageHeight; + + float mViewpointX, mViewpointY, mViewpointZ, mViewpointF; + + bool CheckFinish(); + void SetFinish(); + bool mbFinishRequested; + bool mbFinished; + std::mutex mMutexFinish; + + bool mbStopped; + bool mbStopRequested; + std::mutex mMutexStop; + +}; + +} + + +#endif // VIEWER_H + + diff --git a/for_orb_slam2/src/Converter.cc b/for_orb_slam2/src/Converter.cc new file mode 100644 index 00000000000..00ea8ee5978 --- /dev/null +++ b/for_orb_slam2/src/Converter.cc @@ -0,0 +1,151 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + + +#include "Converter.h" + +namespace ORB_SLAM2 +{ + +std::vector Converter::toDescriptorVector(const cv::Mat &Descriptors) +{ + std::vector vDesc; + vDesc.reserve(Descriptors.rows); + for (int j=0;j R; + R << cvT.at(0,0), cvT.at(0,1), cvT.at(0,2), + cvT.at(1,0), cvT.at(1,1), cvT.at(1,2), + cvT.at(2,0), cvT.at(2,1), cvT.at(2,2); + + Eigen::Matrix t(cvT.at(0,3), cvT.at(1,3), cvT.at(2,3)); + + return g2o::SE3Quat(R,t); +} + +cv::Mat Converter::toCvMat(const g2o::SE3Quat &SE3) +{ + Eigen::Matrix eigMat = SE3.to_homogeneous_matrix(); + return toCvMat(eigMat); +} + +cv::Mat Converter::toCvMat(const g2o::Sim3 &Sim3) +{ + Eigen::Matrix3d eigR = Sim3.rotation().toRotationMatrix(); + Eigen::Vector3d eigt = Sim3.translation(); + double s = Sim3.scale(); + return toCvSE3(s*eigR,eigt); +} + +cv::Mat Converter::toCvMat(const Eigen::Matrix &m) +{ + cv::Mat cvMat(4,4,CV_32F); + for(int i=0;i<4;i++) + for(int j=0; j<4; j++) + cvMat.at(i,j)=m(i,j); + + return cvMat.clone(); +} + +cv::Mat Converter::toCvMat(const Eigen::Matrix3d &m) +{ + cv::Mat cvMat(3,3,CV_32F); + for(int i=0;i<3;i++) + for(int j=0; j<3; j++) + cvMat.at(i,j)=m(i,j); + + return cvMat.clone(); +} + +cv::Mat Converter::toCvMat(const Eigen::Matrix &m) +{ + cv::Mat cvMat(3,1,CV_32F); + for(int i=0;i<3;i++) + cvMat.at(i)=m(i); + + return cvMat.clone(); +} + +cv::Mat Converter::toCvSE3(const Eigen::Matrix &R, const Eigen::Matrix &t) +{ + cv::Mat cvMat = cv::Mat::eye(4,4,CV_32F); + for(int i=0;i<3;i++) + { + for(int j=0;j<3;j++) + { + cvMat.at(i,j)=R(i,j); + } + } + for(int i=0;i<3;i++) + { + cvMat.at(i,3)=t(i); + } + + return cvMat.clone(); +} + +Eigen::Matrix Converter::toVector3d(const cv::Mat &cvVector) +{ + Eigen::Matrix v; + v << cvVector.at(0), cvVector.at(1), cvVector.at(2); + + return v; +} + +Eigen::Matrix Converter::toVector3d(const cv::Point3f &cvPoint) +{ + Eigen::Matrix v; + v << cvPoint.x, cvPoint.y, cvPoint.z; + + return v; +} + +Eigen::Matrix Converter::toMatrix3d(const cv::Mat &cvMat3) +{ + Eigen::Matrix M; + + M << cvMat3.at(0,0), cvMat3.at(0,1), cvMat3.at(0,2), + cvMat3.at(1,0), cvMat3.at(1,1), cvMat3.at(1,2), + cvMat3.at(2,0), cvMat3.at(2,1), cvMat3.at(2,2); + + return M; +} + +std::vector Converter::toQuaternion(const cv::Mat &M) +{ + Eigen::Matrix eigMat = toMatrix3d(M); + Eigen::Quaterniond q(eigMat); + + std::vector v(4); + v[0] = q.x(); + v[1] = q.y(); + v[2] = q.z(); + v[3] = q.w(); + + return v; +} + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/Frame.cc b/for_orb_slam2/src/Frame.cc new file mode 100644 index 00000000000..0e37d493358 --- /dev/null +++ b/for_orb_slam2/src/Frame.cc @@ -0,0 +1,682 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#include "Frame.h" +#include "Converter.h" +#include "ORBmatcher.h" +#include + +namespace ORB_SLAM2 +{ + +long unsigned int Frame::nNextId=0; +bool Frame::mbInitialComputations=true; +float Frame::cx, Frame::cy, Frame::fx, Frame::fy, Frame::invfx, Frame::invfy; +float Frame::mnMinX, Frame::mnMinY, Frame::mnMaxX, Frame::mnMaxY; +float Frame::mfGridElementWidthInv, Frame::mfGridElementHeightInv; + +Frame::Frame() +{} + +//Copy Constructor +Frame::Frame(const Frame &frame) + :mpORBvocabulary(frame.mpORBvocabulary), mpORBextractorLeft(frame.mpORBextractorLeft), mpORBextractorRight(frame.mpORBextractorRight), + mTimeStamp(frame.mTimeStamp), mK(frame.mK.clone()), mDistCoef(frame.mDistCoef.clone()), + mbf(frame.mbf), mb(frame.mb), mThDepth(frame.mThDepth), N(frame.N), mvKeys(frame.mvKeys), + mvKeysRight(frame.mvKeysRight), mvKeysUn(frame.mvKeysUn), mvuRight(frame.mvuRight), + mvDepth(frame.mvDepth), mBowVec(frame.mBowVec), mFeatVec(frame.mFeatVec), + mDescriptors(frame.mDescriptors.clone()), mDescriptorsRight(frame.mDescriptorsRight.clone()), + mvpMapPoints(frame.mvpMapPoints), mvbOutlier(frame.mvbOutlier), mnId(frame.mnId), + mpReferenceKF(frame.mpReferenceKF), mnScaleLevels(frame.mnScaleLevels), + mfScaleFactor(frame.mfScaleFactor), mfLogScaleFactor(frame.mfLogScaleFactor), + mvScaleFactors(frame.mvScaleFactors), mvInvScaleFactors(frame.mvInvScaleFactors), + mvLevelSigma2(frame.mvLevelSigma2), mvInvLevelSigma2(frame.mvInvLevelSigma2) +{ + for(int i=0;i(NULL)) +{ + // Frame ID + mnId=nNextId++; + + // Scale Level Info + mnScaleLevels = mpORBextractorLeft->GetLevels(); + mfScaleFactor = mpORBextractorLeft->GetScaleFactor(); + mfLogScaleFactor = log(mfScaleFactor); + mvScaleFactors = mpORBextractorLeft->GetScaleFactors(); + mvInvScaleFactors = mpORBextractorLeft->GetInverseScaleFactors(); + mvLevelSigma2 = mpORBextractorLeft->GetScaleSigmaSquares(); + mvInvLevelSigma2 = mpORBextractorLeft->GetInverseScaleSigmaSquares(); + + // ORB extraction + thread threadLeft(&Frame::ExtractORB,this,0,imLeft); + thread threadRight(&Frame::ExtractORB,this,1,imRight); + threadLeft.join(); + threadRight.join(); + + N = mvKeys.size(); + + if(mvKeys.empty()) + return; + + UndistortKeyPoints(); + + ComputeStereoMatches(); + + mvpMapPoints = vector(N,static_cast(NULL)); + mvbOutlier = vector(N,false); + + + // This is done only for the first Frame (or after a change in the calibration) + if(mbInitialComputations) + { + ComputeImageBounds(imLeft); + + mfGridElementWidthInv=static_cast(FRAME_GRID_COLS)/(mnMaxX-mnMinX); + mfGridElementHeightInv=static_cast(FRAME_GRID_ROWS)/(mnMaxY-mnMinY); + + fx = K.at(0,0); + fy = K.at(1,1); + cx = K.at(0,2); + cy = K.at(1,2); + invfx = 1.0f/fx; + invfy = 1.0f/fy; + + mbInitialComputations=false; + } + + mb = mbf/fx; + + AssignFeaturesToGrid(); +} + +Frame::Frame(const cv::Mat &imGray, const cv::Mat &imDepth, const double &timeStamp, ORBextractor* extractor,ORBVocabulary* voc, cv::Mat &K, cv::Mat &distCoef, const float &bf, const float &thDepth) + :mpORBvocabulary(voc),mpORBextractorLeft(extractor),mpORBextractorRight(static_cast(NULL)), + mTimeStamp(timeStamp), mK(K.clone()),mDistCoef(distCoef.clone()), mbf(bf), mThDepth(thDepth) +{ + // Frame ID + mnId=nNextId++; + + // Scale Level Info + mnScaleLevels = mpORBextractorLeft->GetLevels(); + mfScaleFactor = mpORBextractorLeft->GetScaleFactor(); + mfLogScaleFactor = log(mfScaleFactor); + mvScaleFactors = mpORBextractorLeft->GetScaleFactors(); + mvInvScaleFactors = mpORBextractorLeft->GetInverseScaleFactors(); + mvLevelSigma2 = mpORBextractorLeft->GetScaleSigmaSquares(); + mvInvLevelSigma2 = mpORBextractorLeft->GetInverseScaleSigmaSquares(); + + // ORB extraction + ExtractORB(0,imGray); + + N = mvKeys.size(); + + if(mvKeys.empty()) + return; + + UndistortKeyPoints(); + + ComputeStereoFromRGBD(imDepth); + + mvpMapPoints = vector(N,static_cast(NULL)); + mvbOutlier = vector(N,false); + + // This is done only for the first Frame (or after a change in the calibration) + if(mbInitialComputations) + { + ComputeImageBounds(imGray); + + mfGridElementWidthInv=static_cast(FRAME_GRID_COLS)/static_cast(mnMaxX-mnMinX); + mfGridElementHeightInv=static_cast(FRAME_GRID_ROWS)/static_cast(mnMaxY-mnMinY); + + fx = K.at(0,0); + fy = K.at(1,1); + cx = K.at(0,2); + cy = K.at(1,2); + invfx = 1.0f/fx; + invfy = 1.0f/fy; + + mbInitialComputations=false; + } + + mb = mbf/fx; + + AssignFeaturesToGrid(); +} + + +Frame::Frame(const cv::Mat &imGray, const double &timeStamp, ORBextractor* extractor,ORBVocabulary* voc, cv::Mat &K, cv::Mat &distCoef, const float &bf, const float &thDepth) + :mpORBvocabulary(voc),mpORBextractorLeft(extractor),mpORBextractorRight(static_cast(NULL)), + mTimeStamp(timeStamp), mK(K.clone()),mDistCoef(distCoef.clone()), mbf(bf), mThDepth(thDepth) +{ + // Frame ID + mnId=nNextId++; + + // Scale Level Info + mnScaleLevels = mpORBextractorLeft->GetLevels(); + mfScaleFactor = mpORBextractorLeft->GetScaleFactor(); + mfLogScaleFactor = log(mfScaleFactor); + mvScaleFactors = mpORBextractorLeft->GetScaleFactors(); + mvInvScaleFactors = mpORBextractorLeft->GetInverseScaleFactors(); + mvLevelSigma2 = mpORBextractorLeft->GetScaleSigmaSquares(); + mvInvLevelSigma2 = mpORBextractorLeft->GetInverseScaleSigmaSquares(); + + // ORB extraction + ExtractORB(0,imGray); + + N = mvKeys.size(); + + if(mvKeys.empty()) + return; + + UndistortKeyPoints(); + + // Set no stereo information + mvuRight = vector(N,-1); + mvDepth = vector(N,-1); + + mvpMapPoints = vector(N,static_cast(NULL)); + mvbOutlier = vector(N,false); + + // This is done only for the first Frame (or after a change in the calibration) + if(mbInitialComputations) + { + ComputeImageBounds(imGray); + + mfGridElementWidthInv=static_cast(FRAME_GRID_COLS)/static_cast(mnMaxX-mnMinX); + mfGridElementHeightInv=static_cast(FRAME_GRID_ROWS)/static_cast(mnMaxY-mnMinY); + + fx = K.at(0,0); + fy = K.at(1,1); + cx = K.at(0,2); + cy = K.at(1,2); + invfx = 1.0f/fx; + invfy = 1.0f/fy; + + mbInitialComputations=false; + } + + mb = mbf/fx; + + AssignFeaturesToGrid(); +} + +void Frame::AssignFeaturesToGrid() +{ + int nReserve = 0.5f*N/(FRAME_GRID_COLS*FRAME_GRID_ROWS); + for(unsigned int i=0; imbTrackInView = false; + + // 3D in absolute coordinates + cv::Mat P = pMP->GetWorldPos(); + + // 3D in camera coordinates + const cv::Mat Pc = mRcw*P+mtcw; + const float &PcX = Pc.at(0); + const float &PcY= Pc.at(1); + const float &PcZ = Pc.at(2); + + // Check positive depth + if(PcZ<0.0f) + return false; + + // Project in image and check it is not outside + const float invz = 1.0f/PcZ; + const float u=fx*PcX*invz+cx; + const float v=fy*PcY*invz+cy; + + if(umnMaxX) + return false; + if(vmnMaxY) + return false; + + // Check distance is in the scale invariance region of the MapPoint + const float maxDistance = pMP->GetMaxDistanceInvariance(); + const float minDistance = pMP->GetMinDistanceInvariance(); + const cv::Mat PO = P-mOw; + const float dist = cv::norm(PO); + + if(distmaxDistance) + return false; + + // Check viewing angle + cv::Mat Pn = pMP->GetNormal(); + + const float viewCos = PO.dot(Pn)/dist; + + if(viewCosPredictScale(dist,this); + + // Data used by the tracking + pMP->mbTrackInView = true; + pMP->mTrackProjX = u; + pMP->mTrackProjXR = u - mbf*invz; + pMP->mTrackProjY = v; + pMP->mnTrackScaleLevel= nPredictedLevel; + pMP->mTrackViewCos = viewCos; + + return true; +} + +vector Frame::GetFeaturesInArea(const float &x, const float &y, const float &r, const int minLevel, const int maxLevel) const +{ + vector vIndices; + vIndices.reserve(N); + + const int nMinCellX = max(0,(int)floor((x-mnMinX-r)*mfGridElementWidthInv)); + if(nMinCellX>=FRAME_GRID_COLS) + return vIndices; + + const int nMaxCellX = min((int)FRAME_GRID_COLS-1,(int)ceil((x-mnMinX+r)*mfGridElementWidthInv)); + if(nMaxCellX<0) + return vIndices; + + const int nMinCellY = max(0,(int)floor((y-mnMinY-r)*mfGridElementHeightInv)); + if(nMinCellY>=FRAME_GRID_ROWS) + return vIndices; + + const int nMaxCellY = min((int)FRAME_GRID_ROWS-1,(int)ceil((y-mnMinY+r)*mfGridElementHeightInv)); + if(nMaxCellY<0) + return vIndices; + + const bool bCheckLevels = (minLevel>0) || (maxLevel>=0); + + for(int ix = nMinCellX; ix<=nMaxCellX; ix++) + { + for(int iy = nMinCellY; iy<=nMaxCellY; iy++) + { + const vector vCell = mGrid[ix][iy]; + if(vCell.empty()) + continue; + + for(size_t j=0, jend=vCell.size(); j=0) + if(kpUn.octave>maxLevel) + continue; + } + + const float distx = kpUn.pt.x-x; + const float disty = kpUn.pt.y-y; + + if(fabs(distx)=FRAME_GRID_COLS || posY<0 || posY>=FRAME_GRID_ROWS) + return false; + + return true; +} + + +void Frame::ComputeBoW() +{ + if(mBowVec.empty()) + { + vector vCurrentDesc = Converter::toDescriptorVector(mDescriptors); + mpORBvocabulary->transform(vCurrentDesc,mBowVec,mFeatVec,4); + } +} + +void Frame::UndistortKeyPoints() +{ + if(mDistCoef.at(0)==0.0) + { + mvKeysUn=mvKeys; + return; + } + + // Fill matrix with points + cv::Mat mat(N,2,CV_32F); + for(int i=0; i(i,0)=mvKeys[i].pt.x; + mat.at(i,1)=mvKeys[i].pt.y; + } + + // Undistort points + mat=mat.reshape(2); + cv::undistortPoints(mat,mat,mK,mDistCoef,cv::Mat(),mK); + mat=mat.reshape(1); + + // Fill undistorted keypoint vector + mvKeysUn.resize(N); + for(int i=0; i(i,0); + kp.pt.y=mat.at(i,1); + mvKeysUn[i]=kp; + } +} + +void Frame::ComputeImageBounds(const cv::Mat &imLeft) +{ + if(mDistCoef.at(0)!=0.0) + { + cv::Mat mat(4,2,CV_32F); + mat.at(0,0)=0.0; mat.at(0,1)=0.0; + mat.at(1,0)=imLeft.cols; mat.at(1,1)=0.0; + mat.at(2,0)=0.0; mat.at(2,1)=imLeft.rows; + mat.at(3,0)=imLeft.cols; mat.at(3,1)=imLeft.rows; + + // Undistort corners + mat=mat.reshape(2); + cv::undistortPoints(mat,mat,mK,mDistCoef,cv::Mat(),mK); + mat=mat.reshape(1); + + mnMinX = min(mat.at(0,0),mat.at(2,0)); + mnMaxX = max(mat.at(1,0),mat.at(3,0)); + mnMinY = min(mat.at(0,1),mat.at(1,1)); + mnMaxY = max(mat.at(2,1),mat.at(3,1)); + + } + else + { + mnMinX = 0.0f; + mnMaxX = imLeft.cols; + mnMinY = 0.0f; + mnMaxY = imLeft.rows; + } +} + +void Frame::ComputeStereoMatches() +{ + mvuRight = vector(N,-1.0f); + mvDepth = vector(N,-1.0f); + + const int thOrbDist = (ORBmatcher::TH_HIGH+ORBmatcher::TH_LOW)/2; + + const int nRows = mpORBextractorLeft->mvImagePyramid[0].rows; + + //Assign keypoints to row table + vector > vRowIndices(nRows,vector()); + + for(int i=0; i > vDistIdx; + vDistIdx.reserve(N); + + for(int iL=0; iL &vCandidates = vRowIndices[vL]; + + if(vCandidates.empty()) + continue; + + const float minU = uL-maxD; + const float maxU = uL-minD; + + if(maxU<0) + continue; + + int bestDist = ORBmatcher::TH_HIGH; + size_t bestIdxR = 0; + + const cv::Mat &dL = mDescriptors.row(iL); + + // Compare descriptor to right keypoints + for(size_t iC=0; iClevelL+1) + continue; + + const float &uR = kpR.pt.x; + + if(uR>=minU && uR<=maxU) + { + const cv::Mat &dR = mDescriptorsRight.row(iR); + const int dist = ORBmatcher::DescriptorDistance(dL,dR); + + if(distmvImagePyramid[kpL.octave].rowRange(scaledvL-w,scaledvL+w+1).colRange(scaleduL-w,scaleduL+w+1); + IL.convertTo(IL,CV_32F); + IL = IL - IL.at(w,w) *cv::Mat::ones(IL.rows,IL.cols,CV_32F); + + int bestDist = INT_MAX; + int bestincR = 0; + const int L = 5; + vector vDists; + vDists.resize(2*L+1); + + const float iniu = scaleduR0+L-w; + const float endu = scaleduR0+L+w+1; + if(iniu<0 || endu >= mpORBextractorRight->mvImagePyramid[kpL.octave].cols) + continue; + + for(int incR=-L; incR<=+L; incR++) + { + cv::Mat IR = mpORBextractorRight->mvImagePyramid[kpL.octave].rowRange(scaledvL-w,scaledvL+w+1).colRange(scaleduR0+incR-w,scaleduR0+incR+w+1); + IR.convertTo(IR,CV_32F); + IR = IR - IR.at(w,w) *cv::Mat::ones(IR.rows,IR.cols,CV_32F); + + float dist = cv::norm(IL,IR,cv::NORM_L1); + if(dist1) + continue; + + // Re-scaled coordinate + float bestuR = mvScaleFactors[kpL.octave]*((float)scaleduR0+(float)bestincR+deltaR); + + float disparity = (uL-bestuR); + + if(disparity>=minD && disparity(bestDist,iL)); + } + } + } + + sort(vDistIdx.begin(),vDistIdx.end()); + const float median = vDistIdx[vDistIdx.size()/2].first; + const float thDist = 1.5f*1.4f*median; + + for(int i=vDistIdx.size()-1;i>=0;i--) + { + if(vDistIdx[i].first(N,-1); + mvDepth = vector(N,-1); + + for(int i=0; i(v,u); + + if(d>0) + { + mvDepth[i] = d; + mvuRight[i] = kpU.pt.x-mbf/d; + } + } +} + +cv::Mat Frame::UnprojectStereo(const int &i) +{ + const float z = mvDepth[i]; + if(z>0) + { + const float u = mvKeysUn[i].pt.x; + const float v = mvKeysUn[i].pt.y; + const float x = (u-cx)*z*invfx; + const float y = (v-cy)*z*invfy; + cv::Mat x3Dc = (cv::Mat_(3,1) << x, y, z); + return mRwc*x3Dc+mOw; + } + else + return cv::Mat(); +} + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/FrameDrawer.cc b/for_orb_slam2/src/FrameDrawer.cc new file mode 100644 index 00000000000..e23b86c207c --- /dev/null +++ b/for_orb_slam2/src/FrameDrawer.cc @@ -0,0 +1,203 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#include "FrameDrawer.h" +#include "Tracking.h" + +#include +#include + +#include + +namespace ORB_SLAM2 +{ + +FrameDrawer::FrameDrawer(Map* pMap):mpMap(pMap) +{ + mState=Tracking::SYSTEM_NOT_READY; + mIm = cv::Mat(480,640,CV_8UC3, cv::Scalar(0,0,0)); +} + +cv::Mat FrameDrawer::DrawFrame() +{ + cv::Mat im; + vector vIniKeys; // Initialization: KeyPoints in reference frame + vector vMatches; // Initialization: correspondeces with reference keypoints + vector vCurrentKeys; // KeyPoints in current frame + vector vbVO, vbMap; // Tracked MapPoints in current frame + int state; // Tracking state + + //Copy variables within scoped mutex + { + unique_lock lock(mMutex); + state=mState; + if(mState==Tracking::SYSTEM_NOT_READY) + mState=Tracking::NO_IMAGES_YET; + + mIm.copyTo(im); + + if(mState==Tracking::NOT_INITIALIZED) + { + vCurrentKeys = mvCurrentKeys; + vIniKeys = mvIniKeys; + vMatches = mvIniMatches; + } + else if(mState==Tracking::OK) + { + vCurrentKeys = mvCurrentKeys; + vbVO = mvbVO; + vbMap = mvbMap; + } + else if(mState==Tracking::LOST) + { + vCurrentKeys = mvCurrentKeys; + } + } // destroy scoped mutex -> release mutex + + if(im.channels()<3) //this should be always true + cvtColor(im,im,CV_GRAY2BGR); + + //Draw + if(state==Tracking::NOT_INITIALIZED) //INITIALIZING + { + for(unsigned int i=0; i=0) + { + cv::line(im,vIniKeys[i].pt,vCurrentKeys[vMatches[i]].pt, + cv::Scalar(0,255,0)); + } + } + } + else if(state==Tracking::OK) //TRACKING + { + mnTracked=0; + mnTrackedVO=0; + const float r = 5; + const int n = vCurrentKeys.size(); + for(int i=0;iKeyFramesInMap(); + int nMPs = mpMap->MapPointsInMap(); + s << "KFs: " << nKFs << ", MPs: " << nMPs << ", Matches: " << mnTracked; + if(mnTrackedVO>0) + s << ", + VO matches: " << mnTrackedVO; + } + else if(nState==Tracking::LOST) + { + s << " TRACK LOST. TRYING TO RELOCALIZE "; + } + else if(nState==Tracking::SYSTEM_NOT_READY) + { + s << " LOADING ORB VOCABULARY. PLEASE WAIT..."; + } + + int baseline=0; + cv::Size textSize = cv::getTextSize(s.str(),cv::FONT_HERSHEY_PLAIN,1,1,&baseline); + + imText = cv::Mat(im.rows+textSize.height+10,im.cols,im.type()); + im.copyTo(imText.rowRange(0,im.rows).colRange(0,im.cols)); + imText.rowRange(im.rows,imText.rows) = cv::Mat::zeros(textSize.height+10,im.cols,im.type()); + cv::putText(imText,s.str(),cv::Point(5,imText.rows-5),cv::FONT_HERSHEY_PLAIN,1,cv::Scalar(255,255,255),1,8); + +} + +void FrameDrawer::Update(Tracking *pTracker) +{ + unique_lock lock(mMutex); + pTracker->mImGray.copyTo(mIm); + mvCurrentKeys=pTracker->mCurrentFrame.mvKeys; + N = mvCurrentKeys.size(); + mvbVO = vector(N,false); + mvbMap = vector(N,false); + mbOnlyTracking = pTracker->mbOnlyTracking; + + + if(pTracker->mLastProcessedState==Tracking::NOT_INITIALIZED) + { + mvIniKeys=pTracker->mInitialFrame.mvKeys; + mvIniMatches=pTracker->mvIniMatches; + } + else if(pTracker->mLastProcessedState==Tracking::OK) + { + for(int i=0;imCurrentFrame.mvpMapPoints[i]; + if(pMP) + { + if(!pTracker->mCurrentFrame.mvbOutlier[i]) + { + if(pMP->Observations()>0) + mvbMap[i]=true; + else + mvbVO[i]=true; + } + } + } + } + mState=static_cast(pTracker->mLastProcessedState); +} + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/Initializer.cc b/for_orb_slam2/src/Initializer.cc new file mode 100644 index 00000000000..6094b8f51b3 --- /dev/null +++ b/for_orb_slam2/src/Initializer.cc @@ -0,0 +1,931 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#include "Initializer.h" + +#include "Thirdparty/DBoW2/DUtils/Random.h" + +#include "Optimizer.h" +#include "ORBmatcher.h" + +#include + +namespace ORB_SLAM2 +{ + +Initializer::Initializer(const Frame &ReferenceFrame, float sigma, int iterations) +{ + mK = ReferenceFrame.mK.clone(); + + mvKeys1 = ReferenceFrame.mvKeysUn; + + mSigma = sigma; + mSigma2 = sigma*sigma; + mMaxIterations = iterations; +} + +bool Initializer::Initialize(const Frame &CurrentFrame, const vector &vMatches12, cv::Mat &R21, cv::Mat &t21, + vector &vP3D, vector &vbTriangulated) +{ + // Fill structures with current keypoints and matches with reference frame + // Reference Frame: 1, Current Frame: 2 + mvKeys2 = CurrentFrame.mvKeysUn; + + mvMatches12.clear(); + mvMatches12.reserve(mvKeys2.size()); + mvbMatched1.resize(mvKeys1.size()); + for(size_t i=0, iend=vMatches12.size();i=0) + { + mvMatches12.push_back(make_pair(i,vMatches12[i])); + mvbMatched1[i]=true; + } + else + mvbMatched1[i]=false; + } + + const int N = mvMatches12.size(); + + // Indices for minimum set selection + vector vAllIndices; + vAllIndices.reserve(N); + vector vAvailableIndices; + + for(int i=0; i >(mMaxIterations,vector(8,0)); + + DUtils::Random::SeedRandOnce(0); + + for(int it=0; it vbMatchesInliersH, vbMatchesInliersF; + float SH, SF; + cv::Mat H, F; + + thread threadH(&Initializer::FindHomography,this,ref(vbMatchesInliersH), ref(SH), ref(H)); + thread threadF(&Initializer::FindFundamental,this,ref(vbMatchesInliersF), ref(SF), ref(F)); + + // Wait until both threads have finished + threadH.join(); + threadF.join(); + + // Compute ratio of scores + float RH = SH/(SH+SF); + + // Try to reconstruct from homography or fundamental depending on the ratio (0.40-0.45) + if(RH>0.40) + return ReconstructH(vbMatchesInliersH,H,mK,R21,t21,vP3D,vbTriangulated,1.0,50); + else //if(pF_HF>0.6) + return ReconstructF(vbMatchesInliersF,F,mK,R21,t21,vP3D,vbTriangulated,1.0,50); + + return false; +} + + +void Initializer::FindHomography(vector &vbMatchesInliers, float &score, cv::Mat &H21) +{ + // Number of putative matches + const int N = mvMatches12.size(); + + // Normalize coordinates + vector vPn1, vPn2; + cv::Mat T1, T2; + Normalize(mvKeys1,vPn1, T1); + Normalize(mvKeys2,vPn2, T2); + cv::Mat T2inv = T2.inv(); + + // Best Results variables + score = 0.0; + vbMatchesInliers = vector(N,false); + + // Iteration variables + vector vPn1i(8); + vector vPn2i(8); + cv::Mat H21i, H12i; + vector vbCurrentInliers(N,false); + float currentScore; + + // Perform all RANSAC iterations and save the solution with highest score + for(int it=0; itscore) + { + H21 = H21i.clone(); + vbMatchesInliers = vbCurrentInliers; + score = currentScore; + } + } +} + + +void Initializer::FindFundamental(vector &vbMatchesInliers, float &score, cv::Mat &F21) +{ + // Number of putative matches + const int N = vbMatchesInliers.size(); + + // Normalize coordinates + vector vPn1, vPn2; + cv::Mat T1, T2; + Normalize(mvKeys1,vPn1, T1); + Normalize(mvKeys2,vPn2, T2); + cv::Mat T2t = T2.t(); + + // Best Results variables + score = 0.0; + vbMatchesInliers = vector(N,false); + + // Iteration variables + vector vPn1i(8); + vector vPn2i(8); + cv::Mat F21i; + vector vbCurrentInliers(N,false); + float currentScore; + + // Perform all RANSAC iterations and save the solution with highest score + for(int it=0; itscore) + { + F21 = F21i.clone(); + vbMatchesInliers = vbCurrentInliers; + score = currentScore; + } + } +} + + +cv::Mat Initializer::ComputeH21(const vector &vP1, const vector &vP2) +{ + const int N = vP1.size(); + + cv::Mat A(2*N,9,CV_32F); + + for(int i=0; i(2*i,0) = 0.0; + A.at(2*i,1) = 0.0; + A.at(2*i,2) = 0.0; + A.at(2*i,3) = -u1; + A.at(2*i,4) = -v1; + A.at(2*i,5) = -1; + A.at(2*i,6) = v2*u1; + A.at(2*i,7) = v2*v1; + A.at(2*i,8) = v2; + + A.at(2*i+1,0) = u1; + A.at(2*i+1,1) = v1; + A.at(2*i+1,2) = 1; + A.at(2*i+1,3) = 0.0; + A.at(2*i+1,4) = 0.0; + A.at(2*i+1,5) = 0.0; + A.at(2*i+1,6) = -u2*u1; + A.at(2*i+1,7) = -u2*v1; + A.at(2*i+1,8) = -u2; + + } + + cv::Mat u,w,vt; + + cv::SVDecomp(A,w,u,vt,cv::SVD::MODIFY_A | cv::SVD::FULL_UV); + + return vt.row(8).reshape(0, 3); +} + +cv::Mat Initializer::ComputeF21(const vector &vP1,const vector &vP2) +{ + const int N = vP1.size(); + + cv::Mat A(N,9,CV_32F); + + for(int i=0; i(i,0) = u2*u1; + A.at(i,1) = u2*v1; + A.at(i,2) = u2; + A.at(i,3) = v2*u1; + A.at(i,4) = v2*v1; + A.at(i,5) = v2; + A.at(i,6) = u1; + A.at(i,7) = v1; + A.at(i,8) = 1; + } + + cv::Mat u,w,vt; + + cv::SVDecomp(A,w,u,vt,cv::SVD::MODIFY_A | cv::SVD::FULL_UV); + + cv::Mat Fpre = vt.row(8).reshape(0, 3); + + cv::SVDecomp(Fpre,w,u,vt,cv::SVD::MODIFY_A | cv::SVD::FULL_UV); + + w.at(2)=0; + + return u*cv::Mat::diag(w)*vt; +} + +float Initializer::CheckHomography(const cv::Mat &H21, const cv::Mat &H12, vector &vbMatchesInliers, float sigma) +{ + const int N = mvMatches12.size(); + + const float h11 = H21.at(0,0); + const float h12 = H21.at(0,1); + const float h13 = H21.at(0,2); + const float h21 = H21.at(1,0); + const float h22 = H21.at(1,1); + const float h23 = H21.at(1,2); + const float h31 = H21.at(2,0); + const float h32 = H21.at(2,1); + const float h33 = H21.at(2,2); + + const float h11inv = H12.at(0,0); + const float h12inv = H12.at(0,1); + const float h13inv = H12.at(0,2); + const float h21inv = H12.at(1,0); + const float h22inv = H12.at(1,1); + const float h23inv = H12.at(1,2); + const float h31inv = H12.at(2,0); + const float h32inv = H12.at(2,1); + const float h33inv = H12.at(2,2); + + vbMatchesInliers.resize(N); + + float score = 0; + + const float th = 5.991; + + const float invSigmaSquare = 1.0/(sigma*sigma); + + for(int i=0; ith) + bIn = false; + else + score += th - chiSquare1; + + // Reprojection error in second image + // x1in2 = H21*x1 + + const float w1in2inv = 1.0/(h31*u1+h32*v1+h33); + const float u1in2 = (h11*u1+h12*v1+h13)*w1in2inv; + const float v1in2 = (h21*u1+h22*v1+h23)*w1in2inv; + + const float squareDist2 = (u2-u1in2)*(u2-u1in2)+(v2-v1in2)*(v2-v1in2); + + const float chiSquare2 = squareDist2*invSigmaSquare; + + if(chiSquare2>th) + bIn = false; + else + score += th - chiSquare2; + + if(bIn) + vbMatchesInliers[i]=true; + else + vbMatchesInliers[i]=false; + } + + return score; +} + +float Initializer::CheckFundamental(const cv::Mat &F21, vector &vbMatchesInliers, float sigma) +{ + const int N = mvMatches12.size(); + + const float f11 = F21.at(0,0); + const float f12 = F21.at(0,1); + const float f13 = F21.at(0,2); + const float f21 = F21.at(1,0); + const float f22 = F21.at(1,1); + const float f23 = F21.at(1,2); + const float f31 = F21.at(2,0); + const float f32 = F21.at(2,1); + const float f33 = F21.at(2,2); + + vbMatchesInliers.resize(N); + + float score = 0; + + const float th = 3.841; + const float thScore = 5.991; + + const float invSigmaSquare = 1.0/(sigma*sigma); + + for(int i=0; ith) + bIn = false; + else + score += thScore - chiSquare1; + + // Reprojection error in second image + // l1 =x2tF21=(a1,b1,c1) + + const float a1 = f11*u2+f21*v2+f31; + const float b1 = f12*u2+f22*v2+f32; + const float c1 = f13*u2+f23*v2+f33; + + const float num1 = a1*u1+b1*v1+c1; + + const float squareDist2 = num1*num1/(a1*a1+b1*b1); + + const float chiSquare2 = squareDist2*invSigmaSquare; + + if(chiSquare2>th) + bIn = false; + else + score += thScore - chiSquare2; + + if(bIn) + vbMatchesInliers[i]=true; + else + vbMatchesInliers[i]=false; + } + + return score; +} + +bool Initializer::ReconstructF(vector &vbMatchesInliers, cv::Mat &F21, cv::Mat &K, + cv::Mat &R21, cv::Mat &t21, vector &vP3D, vector &vbTriangulated, float minParallax, int minTriangulated) +{ + int N=0; + for(size_t i=0, iend = vbMatchesInliers.size() ; i vP3D1, vP3D2, vP3D3, vP3D4; + vector vbTriangulated1,vbTriangulated2,vbTriangulated3, vbTriangulated4; + float parallax1,parallax2, parallax3, parallax4; + + int nGood1 = CheckRT(R1,t1,mvKeys1,mvKeys2,mvMatches12,vbMatchesInliers,K, vP3D1, 4.0*mSigma2, vbTriangulated1, parallax1); + int nGood2 = CheckRT(R2,t1,mvKeys1,mvKeys2,mvMatches12,vbMatchesInliers,K, vP3D2, 4.0*mSigma2, vbTriangulated2, parallax2); + int nGood3 = CheckRT(R1,t2,mvKeys1,mvKeys2,mvMatches12,vbMatchesInliers,K, vP3D3, 4.0*mSigma2, vbTriangulated3, parallax3); + int nGood4 = CheckRT(R2,t2,mvKeys1,mvKeys2,mvMatches12,vbMatchesInliers,K, vP3D4, 4.0*mSigma2, vbTriangulated4, parallax4); + + int maxGood = max(nGood1,max(nGood2,max(nGood3,nGood4))); + + R21 = cv::Mat(); + t21 = cv::Mat(); + + int nMinGood = max(static_cast(0.9*N),minTriangulated); + + int nsimilar = 0; + if(nGood1>0.7*maxGood) + nsimilar++; + if(nGood2>0.7*maxGood) + nsimilar++; + if(nGood3>0.7*maxGood) + nsimilar++; + if(nGood4>0.7*maxGood) + nsimilar++; + + // If there is not a clear winner or not enough triangulated points reject initialization + if(maxGood1) + { + return false; + } + + // If best reconstruction has enough parallax initialize + if(maxGood==nGood1) + { + if(parallax1>minParallax) + { + vP3D = vP3D1; + vbTriangulated = vbTriangulated1; + + R1.copyTo(R21); + t1.copyTo(t21); + return true; + } + }else if(maxGood==nGood2) + { + if(parallax2>minParallax) + { + vP3D = vP3D2; + vbTriangulated = vbTriangulated2; + + R2.copyTo(R21); + t1.copyTo(t21); + return true; + } + }else if(maxGood==nGood3) + { + if(parallax3>minParallax) + { + vP3D = vP3D3; + vbTriangulated = vbTriangulated3; + + R1.copyTo(R21); + t2.copyTo(t21); + return true; + } + }else if(maxGood==nGood4) + { + if(parallax4>minParallax) + { + vP3D = vP3D4; + vbTriangulated = vbTriangulated4; + + R2.copyTo(R21); + t2.copyTo(t21); + return true; + } + } + + return false; +} + +bool Initializer::ReconstructH(vector &vbMatchesInliers, cv::Mat &H21, cv::Mat &K, + cv::Mat &R21, cv::Mat &t21, vector &vP3D, vector &vbTriangulated, float minParallax, int minTriangulated) +{ + int N=0; + for(size_t i=0, iend = vbMatchesInliers.size() ; i(0); + float d2 = w.at(1); + float d3 = w.at(2); + + if(d1/d2<1.00001 || d2/d3<1.00001) + { + return false; + } + + vector vR, vt, vn; + vR.reserve(8); + vt.reserve(8); + vn.reserve(8); + + //n'=[x1 0 x3] 4 posibilities e1=e3=1, e1=1 e3=-1, e1=-1 e3=1, e1=e3=-1 + float aux1 = sqrt((d1*d1-d2*d2)/(d1*d1-d3*d3)); + float aux3 = sqrt((d2*d2-d3*d3)/(d1*d1-d3*d3)); + float x1[] = {aux1,aux1,-aux1,-aux1}; + float x3[] = {aux3,-aux3,aux3,-aux3}; + + //case d'=d2 + float aux_stheta = sqrt((d1*d1-d2*d2)*(d2*d2-d3*d3))/((d1+d3)*d2); + + float ctheta = (d2*d2+d1*d3)/((d1+d3)*d2); + float stheta[] = {aux_stheta, -aux_stheta, -aux_stheta, aux_stheta}; + + for(int i=0; i<4; i++) + { + cv::Mat Rp=cv::Mat::eye(3,3,CV_32F); + Rp.at(0,0)=ctheta; + Rp.at(0,2)=-stheta[i]; + Rp.at(2,0)=stheta[i]; + Rp.at(2,2)=ctheta; + + cv::Mat R = s*U*Rp*Vt; + vR.push_back(R); + + cv::Mat tp(3,1,CV_32F); + tp.at(0)=x1[i]; + tp.at(1)=0; + tp.at(2)=-x3[i]; + tp*=d1-d3; + + cv::Mat t = U*tp; + vt.push_back(t/cv::norm(t)); + + cv::Mat np(3,1,CV_32F); + np.at(0)=x1[i]; + np.at(1)=0; + np.at(2)=x3[i]; + + cv::Mat n = V*np; + if(n.at(2)<0) + n=-n; + vn.push_back(n); + } + + //case d'=-d2 + float aux_sphi = sqrt((d1*d1-d2*d2)*(d2*d2-d3*d3))/((d1-d3)*d2); + + float cphi = (d1*d3-d2*d2)/((d1-d3)*d2); + float sphi[] = {aux_sphi, -aux_sphi, -aux_sphi, aux_sphi}; + + for(int i=0; i<4; i++) + { + cv::Mat Rp=cv::Mat::eye(3,3,CV_32F); + Rp.at(0,0)=cphi; + Rp.at(0,2)=sphi[i]; + Rp.at(1,1)=-1; + Rp.at(2,0)=sphi[i]; + Rp.at(2,2)=-cphi; + + cv::Mat R = s*U*Rp*Vt; + vR.push_back(R); + + cv::Mat tp(3,1,CV_32F); + tp.at(0)=x1[i]; + tp.at(1)=0; + tp.at(2)=x3[i]; + tp*=d1+d3; + + cv::Mat t = U*tp; + vt.push_back(t/cv::norm(t)); + + cv::Mat np(3,1,CV_32F); + np.at(0)=x1[i]; + np.at(1)=0; + np.at(2)=x3[i]; + + cv::Mat n = V*np; + if(n.at(2)<0) + n=-n; + vn.push_back(n); + } + + + int bestGood = 0; + int secondBestGood = 0; + int bestSolutionIdx = -1; + float bestParallax = -1; + vector bestP3D; + vector bestTriangulated; + + // Instead of applying the visibility constraints proposed in the Faugeras' paper (which could fail for points seen with low parallax) + // We reconstruct all hypotheses and check in terms of triangulated points and parallax + for(size_t i=0; i<8; i++) + { + float parallaxi; + vector vP3Di; + vector vbTriangulatedi; + int nGood = CheckRT(vR[i],vt[i],mvKeys1,mvKeys2,mvMatches12,vbMatchesInliers,K,vP3Di, 4.0*mSigma2, vbTriangulatedi, parallaxi); + + if(nGood>bestGood) + { + secondBestGood = bestGood; + bestGood = nGood; + bestSolutionIdx = i; + bestParallax = parallaxi; + bestP3D = vP3Di; + bestTriangulated = vbTriangulatedi; + } + else if(nGood>secondBestGood) + { + secondBestGood = nGood; + } + } + + + if(secondBestGood<0.75*bestGood && bestParallax>=minParallax && bestGood>minTriangulated && bestGood>0.9*N) + { + vR[bestSolutionIdx].copyTo(R21); + vt[bestSolutionIdx].copyTo(t21); + vP3D = bestP3D; + vbTriangulated = bestTriangulated; + + return true; + } + + return false; +} + +void Initializer::Triangulate(const cv::KeyPoint &kp1, const cv::KeyPoint &kp2, const cv::Mat &P1, const cv::Mat &P2, cv::Mat &x3D) +{ + cv::Mat A(4,4,CV_32F); + + A.row(0) = kp1.pt.x*P1.row(2)-P1.row(0); + A.row(1) = kp1.pt.y*P1.row(2)-P1.row(1); + A.row(2) = kp2.pt.x*P2.row(2)-P2.row(0); + A.row(3) = kp2.pt.y*P2.row(2)-P2.row(1); + + cv::Mat u,w,vt; + cv::SVD::compute(A,w,u,vt,cv::SVD::MODIFY_A| cv::SVD::FULL_UV); + x3D = vt.row(3).t(); + x3D = x3D.rowRange(0,3)/x3D.at(3); +} + +void Initializer::Normalize(const vector &vKeys, vector &vNormalizedPoints, cv::Mat &T) +{ + float meanX = 0; + float meanY = 0; + const int N = vKeys.size(); + + vNormalizedPoints.resize(N); + + for(int i=0; i(0,0) = sX; + T.at(1,1) = sY; + T.at(0,2) = -meanX*sX; + T.at(1,2) = -meanY*sY; +} + + +int Initializer::CheckRT(const cv::Mat &R, const cv::Mat &t, const vector &vKeys1, const vector &vKeys2, + const vector &vMatches12, vector &vbMatchesInliers, + const cv::Mat &K, vector &vP3D, float th2, vector &vbGood, float ¶llax) +{ + // Calibration parameters + const float fx = K.at(0,0); + const float fy = K.at(1,1); + const float cx = K.at(0,2); + const float cy = K.at(1,2); + + vbGood = vector(vKeys1.size(),false); + vP3D.resize(vKeys1.size()); + + vector vCosParallax; + vCosParallax.reserve(vKeys1.size()); + + // Camera 1 Projection Matrix K[I|0] + cv::Mat P1(3,4,CV_32F,cv::Scalar(0)); + K.copyTo(P1.rowRange(0,3).colRange(0,3)); + + cv::Mat O1 = cv::Mat::zeros(3,1,CV_32F); + + // Camera 2 Projection Matrix K[R|t] + cv::Mat P2(3,4,CV_32F); + R.copyTo(P2.rowRange(0,3).colRange(0,3)); + t.copyTo(P2.rowRange(0,3).col(3)); + P2 = K*P2; + + cv::Mat O2 = -R.t()*t; + + int nGood=0; + + for(size_t i=0, iend=vMatches12.size();i(0)) || !isfinite(p3dC1.at(1)) || !isfinite(p3dC1.at(2))) + { + vbGood[vMatches12[i].first]=false; + continue; + } + + // Check parallax + cv::Mat normal1 = p3dC1 - O1; + float dist1 = cv::norm(normal1); + + cv::Mat normal2 = p3dC1 - O2; + float dist2 = cv::norm(normal2); + + float cosParallax = normal1.dot(normal2)/(dist1*dist2); + + // Check depth in front of first camera (only if enough parallax, as "infinite" points can easily go to negative depth) + if(p3dC1.at(2)<=0 && cosParallax<0.99998) + continue; + + // Check depth in front of second camera (only if enough parallax, as "infinite" points can easily go to negative depth) + cv::Mat p3dC2 = R*p3dC1+t; + + if(p3dC2.at(2)<=0 && cosParallax<0.99998) + continue; + + // Check reprojection error in first image + float im1x, im1y; + float invZ1 = 1.0/p3dC1.at(2); + im1x = fx*p3dC1.at(0)*invZ1+cx; + im1y = fy*p3dC1.at(1)*invZ1+cy; + + float squareError1 = (im1x-kp1.pt.x)*(im1x-kp1.pt.x)+(im1y-kp1.pt.y)*(im1y-kp1.pt.y); + + if(squareError1>th2) + continue; + + // Check reprojection error in second image + float im2x, im2y; + float invZ2 = 1.0/p3dC2.at(2); + im2x = fx*p3dC2.at(0)*invZ2+cx; + im2y = fy*p3dC2.at(1)*invZ2+cy; + + float squareError2 = (im2x-kp2.pt.x)*(im2x-kp2.pt.x)+(im2y-kp2.pt.y)*(im2y-kp2.pt.y); + + if(squareError2>th2) + continue; + + vCosParallax.push_back(cosParallax); + vP3D[vMatches12[i].first] = cv::Point3f(p3dC1.at(0),p3dC1.at(1),p3dC1.at(2)); + nGood++; + + if(cosParallax<0.99998) + vbGood[vMatches12[i].first]=true; + } + + if(nGood>0) + { + sort(vCosParallax.begin(),vCosParallax.end()); + + size_t idx = min(50,int(vCosParallax.size()-1)); + parallax = acos(vCosParallax[idx])*180/CV_PI; + } + else + parallax=0; + + return nGood; +} + +void Initializer::DecomposeE(const cv::Mat &E, cv::Mat &R1, cv::Mat &R2, cv::Mat &t) +{ + cv::Mat u,w,vt; + cv::SVD::compute(E,w,u,vt); + + u.col(2).copyTo(t); + t=t/cv::norm(t); + + cv::Mat W(3,3,CV_32F,cv::Scalar(0)); + W.at(0,1)=-1; + W.at(1,0)=1; + W.at(2,2)=1; + + R1 = u*W*vt; + if(cv::determinant(R1)<0) + R1=-R1; + + R2 = u*W.t()*vt; + if(cv::determinant(R2)<0) + R2=-R2; +} + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/KeyFrame.cc b/for_orb_slam2/src/KeyFrame.cc new file mode 100644 index 00000000000..4ef1e78e0f7 --- /dev/null +++ b/for_orb_slam2/src/KeyFrame.cc @@ -0,0 +1,665 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#include "KeyFrame.h" +#include "Converter.h" +#include "ORBmatcher.h" +#include + +namespace ORB_SLAM2 +{ + +long unsigned int KeyFrame::nNextId=0; + +KeyFrame::KeyFrame(Frame &F, Map *pMap, KeyFrameDatabase *pKFDB): + mnFrameId(F.mnId), mTimeStamp(F.mTimeStamp), mnGridCols(FRAME_GRID_COLS), mnGridRows(FRAME_GRID_ROWS), + mfGridElementWidthInv(F.mfGridElementWidthInv), mfGridElementHeightInv(F.mfGridElementHeightInv), + mnTrackReferenceForFrame(0), mnFuseTargetForKF(0), mnBALocalForKF(0), mnBAFixedForKF(0), + mnLoopQuery(0), mnLoopWords(0), mnRelocQuery(0), mnRelocWords(0), mnBAGlobalForKF(0), + fx(F.fx), fy(F.fy), cx(F.cx), cy(F.cy), invfx(F.invfx), invfy(F.invfy), + mbf(F.mbf), mb(F.mb), mThDepth(F.mThDepth), N(F.N), mvKeys(F.mvKeys), mvKeysUn(F.mvKeysUn), + mvuRight(F.mvuRight), mvDepth(F.mvDepth), mDescriptors(F.mDescriptors.clone()), + mBowVec(F.mBowVec), mFeatVec(F.mFeatVec), mnScaleLevels(F.mnScaleLevels), mfScaleFactor(F.mfScaleFactor), + mfLogScaleFactor(F.mfLogScaleFactor), mvScaleFactors(F.mvScaleFactors), mvLevelSigma2(F.mvLevelSigma2), + mvInvLevelSigma2(F.mvInvLevelSigma2), mnMinX(F.mnMinX), mnMinY(F.mnMinY), mnMaxX(F.mnMaxX), + mnMaxY(F.mnMaxY), mK(F.mK), mvpMapPoints(F.mvpMapPoints), mpKeyFrameDB(pKFDB), + mpORBvocabulary(F.mpORBvocabulary), mbFirstConnection(true), mpParent(NULL), mbNotErase(false), + mbToBeErased(false), mbBad(false), mHalfBaseline(F.mb/2), mpMap(pMap) +{ + mnId=nNextId++; + + mGrid.resize(mnGridCols); + for(int i=0; i vCurrentDesc = Converter::toDescriptorVector(mDescriptors); + // Feature vector associate features with nodes in the 4th level (from leaves up) + // We assume the vocabulary tree has 6 levels, change the 4 otherwise + mpORBvocabulary->transform(vCurrentDesc,mBowVec,mFeatVec,4); + } +} + +void KeyFrame::SetPose(const cv::Mat &Tcw_) +{ + unique_lock lock(mMutexPose); + Tcw_.copyTo(Tcw); + cv::Mat Rcw = Tcw.rowRange(0,3).colRange(0,3); + cv::Mat tcw = Tcw.rowRange(0,3).col(3); + cv::Mat Rwc = Rcw.t(); + Ow = -Rwc*tcw; + + Twc = cv::Mat::eye(4,4,Tcw.type()); + Rwc.copyTo(Twc.rowRange(0,3).colRange(0,3)); + Ow.copyTo(Twc.rowRange(0,3).col(3)); + cv::Mat center = (cv::Mat_(4,1) << mHalfBaseline, 0 , 0, 1); + Cw = Twc*center; +} + +cv::Mat KeyFrame::GetPose() +{ + unique_lock lock(mMutexPose); + return Tcw.clone(); +} + +cv::Mat KeyFrame::GetPoseInverse() +{ + unique_lock lock(mMutexPose); + return Twc.clone(); +} + +cv::Mat KeyFrame::GetCameraCenter() +{ + unique_lock lock(mMutexPose); + return Ow.clone(); +} + +cv::Mat KeyFrame::GetStereoCenter() +{ + unique_lock lock(mMutexPose); + return Cw.clone(); +} + + +cv::Mat KeyFrame::GetRotation() +{ + unique_lock lock(mMutexPose); + return Tcw.rowRange(0,3).colRange(0,3).clone(); +} + +cv::Mat KeyFrame::GetTranslation() +{ + unique_lock lock(mMutexPose); + return Tcw.rowRange(0,3).col(3).clone(); +} + +void KeyFrame::AddConnection(KeyFrame *pKF, const int &weight) +{ + { + unique_lock lock(mMutexConnections); + if(!mConnectedKeyFrameWeights.count(pKF)) + mConnectedKeyFrameWeights[pKF]=weight; + else if(mConnectedKeyFrameWeights[pKF]!=weight) + mConnectedKeyFrameWeights[pKF]=weight; + else + return; + } + + UpdateBestCovisibles(); +} + +void KeyFrame::UpdateBestCovisibles() +{ + unique_lock lock(mMutexConnections); + vector > vPairs; + vPairs.reserve(mConnectedKeyFrameWeights.size()); + for(map::iterator mit=mConnectedKeyFrameWeights.begin(), mend=mConnectedKeyFrameWeights.end(); mit!=mend; mit++) + vPairs.push_back(make_pair(mit->second,mit->first)); + + sort(vPairs.begin(),vPairs.end()); + list lKFs; + list lWs; + for(size_t i=0, iend=vPairs.size(); i(lKFs.begin(),lKFs.end()); + mvOrderedWeights = vector(lWs.begin(), lWs.end()); +} + +set KeyFrame::GetConnectedKeyFrames() +{ + unique_lock lock(mMutexConnections); + set s; + for(map::iterator mit=mConnectedKeyFrameWeights.begin();mit!=mConnectedKeyFrameWeights.end();mit++) + s.insert(mit->first); + return s; +} + +vector KeyFrame::GetVectorCovisibleKeyFrames() +{ + unique_lock lock(mMutexConnections); + return mvpOrderedConnectedKeyFrames; +} + +vector KeyFrame::GetBestCovisibilityKeyFrames(const int &N) +{ + unique_lock lock(mMutexConnections); + if((int)mvpOrderedConnectedKeyFrames.size()(mvpOrderedConnectedKeyFrames.begin(),mvpOrderedConnectedKeyFrames.begin()+N); + +} + +vector KeyFrame::GetCovisiblesByWeight(const int &w) +{ + unique_lock lock(mMutexConnections); + + if(mvpOrderedConnectedKeyFrames.empty()) + return vector(); + + vector::iterator it = upper_bound(mvOrderedWeights.begin(),mvOrderedWeights.end(),w,KeyFrame::weightComp); + if(it==mvOrderedWeights.end()) + return vector(); + else + { + int n = it-mvOrderedWeights.begin(); + return vector(mvpOrderedConnectedKeyFrames.begin(), mvpOrderedConnectedKeyFrames.begin()+n); + } +} + +int KeyFrame::GetWeight(KeyFrame *pKF) +{ + unique_lock lock(mMutexConnections); + if(mConnectedKeyFrameWeights.count(pKF)) + return mConnectedKeyFrameWeights[pKF]; + else + return 0; +} + +void KeyFrame::AddMapPoint(MapPoint *pMP, const size_t &idx) +{ + unique_lock lock(mMutexFeatures); + mvpMapPoints[idx]=pMP; +} + +void KeyFrame::EraseMapPointMatch(const size_t &idx) +{ + unique_lock lock(mMutexFeatures); + mvpMapPoints[idx]=static_cast(NULL); +} + +void KeyFrame::EraseMapPointMatch(MapPoint* pMP) +{ + int idx = pMP->GetIndexInKeyFrame(this); + if(idx>=0) + mvpMapPoints[idx]=static_cast(NULL); +} + + +void KeyFrame::ReplaceMapPointMatch(const size_t &idx, MapPoint* pMP) +{ + mvpMapPoints[idx]=pMP; +} + +set KeyFrame::GetMapPoints() +{ + unique_lock lock(mMutexFeatures); + set s; + for(size_t i=0, iend=mvpMapPoints.size(); iisBad()) + s.insert(pMP); + } + return s; +} + +int KeyFrame::TrackedMapPoints(const int &minObs) +{ + unique_lock lock(mMutexFeatures); + + int nPoints=0; + const bool bCheckObs = minObs>0; + for(int i=0; iisBad()) + { + if(bCheckObs) + { + if(mvpMapPoints[i]->Observations()>=minObs) + nPoints++; + } + else + nPoints++; + } + } + } + + return nPoints; +} + +vector KeyFrame::GetMapPointMatches() +{ + unique_lock lock(mMutexFeatures); + return mvpMapPoints; +} + +MapPoint* KeyFrame::GetMapPoint(const size_t &idx) +{ + unique_lock lock(mMutexFeatures); + return mvpMapPoints[idx]; +} + +void KeyFrame::UpdateConnections() +{ + map KFcounter; + + vector vpMP; + + { + unique_lock lockMPs(mMutexFeatures); + vpMP = mvpMapPoints; + } + + //For all map points in keyframe check in which other keyframes are they seen + //Increase counter for those keyframes + for(vector::iterator vit=vpMP.begin(), vend=vpMP.end(); vit!=vend; vit++) + { + MapPoint* pMP = *vit; + + if(!pMP) + continue; + + if(pMP->isBad()) + continue; + + map observations = pMP->GetObservations(); + + for(map::iterator mit=observations.begin(), mend=observations.end(); mit!=mend; mit++) + { + if(mit->first->mnId==mnId) + continue; + KFcounter[mit->first]++; + } + } + + // This should not happen + if(KFcounter.empty()) + return; + + //If the counter is greater than threshold add connection + //In case no keyframe counter is over threshold add the one with maximum counter + int nmax=0; + KeyFrame* pKFmax=NULL; + int th = 15; + + vector > vPairs; + vPairs.reserve(KFcounter.size()); + for(map::iterator mit=KFcounter.begin(), mend=KFcounter.end(); mit!=mend; mit++) + { + if(mit->second>nmax) + { + nmax=mit->second; + pKFmax=mit->first; + } + if(mit->second>=th) + { + vPairs.push_back(make_pair(mit->second,mit->first)); + (mit->first)->AddConnection(this,mit->second); + } + } + + if(vPairs.empty()) + { + vPairs.push_back(make_pair(nmax,pKFmax)); + pKFmax->AddConnection(this,nmax); + } + + sort(vPairs.begin(),vPairs.end()); + list lKFs; + list lWs; + for(size_t i=0; i lockCon(mMutexConnections); + + // mspConnectedKeyFrames = spConnectedKeyFrames; + mConnectedKeyFrameWeights = KFcounter; + mvpOrderedConnectedKeyFrames = vector(lKFs.begin(),lKFs.end()); + mvOrderedWeights = vector(lWs.begin(), lWs.end()); + + if(mbFirstConnection && mnId!=0) + { + mpParent = mvpOrderedConnectedKeyFrames.front(); + mpParent->AddChild(this); + mbFirstConnection = false; + } + + } +} + +void KeyFrame::AddChild(KeyFrame *pKF) +{ + unique_lock lockCon(mMutexConnections); + mspChildrens.insert(pKF); +} + +void KeyFrame::EraseChild(KeyFrame *pKF) +{ + unique_lock lockCon(mMutexConnections); + mspChildrens.erase(pKF); +} + +void KeyFrame::ChangeParent(KeyFrame *pKF) +{ + unique_lock lockCon(mMutexConnections); + mpParent = pKF; + pKF->AddChild(this); +} + +set KeyFrame::GetChilds() +{ + unique_lock lockCon(mMutexConnections); + return mspChildrens; +} + +KeyFrame* KeyFrame::GetParent() +{ + unique_lock lockCon(mMutexConnections); + return mpParent; +} + +bool KeyFrame::hasChild(KeyFrame *pKF) +{ + unique_lock lockCon(mMutexConnections); + return mspChildrens.count(pKF); +} + +void KeyFrame::AddLoopEdge(KeyFrame *pKF) +{ + unique_lock lockCon(mMutexConnections); + mbNotErase = true; + mspLoopEdges.insert(pKF); +} + +set KeyFrame::GetLoopEdges() +{ + unique_lock lockCon(mMutexConnections); + return mspLoopEdges; +} + +void KeyFrame::SetNotErase() +{ + unique_lock lock(mMutexConnections); + mbNotErase = true; +} + +void KeyFrame::SetErase() +{ + { + unique_lock lock(mMutexConnections); + if(mspLoopEdges.empty()) + { + mbNotErase = false; + } + } + + if(mbToBeErased) + { + SetBadFlag(); + } +} + +void KeyFrame::SetBadFlag() +{ + { + unique_lock lock(mMutexConnections); + if(mnId==0) + return; + else if(mbNotErase) + { + mbToBeErased = true; + return; + } + } + + for(map::iterator mit = mConnectedKeyFrameWeights.begin(), mend=mConnectedKeyFrameWeights.end(); mit!=mend; mit++) + mit->first->EraseConnection(this); + + for(size_t i=0; iEraseObservation(this); + { + unique_lock lock(mMutexConnections); + unique_lock lock1(mMutexFeatures); + + mConnectedKeyFrameWeights.clear(); + mvpOrderedConnectedKeyFrames.clear(); + + // Update Spanning Tree + set sParentCandidates; + sParentCandidates.insert(mpParent); + + // Assign at each iteration one children with a parent (the pair with highest covisibility weight) + // Include that children as new parent candidate for the rest + while(!mspChildrens.empty()) + { + bool bContinue = false; + + int max = -1; + KeyFrame* pC; + KeyFrame* pP; + + for(set::iterator sit=mspChildrens.begin(), send=mspChildrens.end(); sit!=send; sit++) + { + KeyFrame* pKF = *sit; + if(pKF->isBad()) + continue; + + // Check if a parent candidate is connected to the keyframe + vector vpConnected = pKF->GetVectorCovisibleKeyFrames(); + for(size_t i=0, iend=vpConnected.size(); i::iterator spcit=sParentCandidates.begin(), spcend=sParentCandidates.end(); spcit!=spcend; spcit++) + { + if(vpConnected[i]->mnId == (*spcit)->mnId) + { + int w = pKF->GetWeight(vpConnected[i]); + if(w>max) + { + pC = pKF; + pP = vpConnected[i]; + max = w; + bContinue = true; + } + } + } + } + } + + if(bContinue) + { + pC->ChangeParent(pP); + sParentCandidates.insert(pC); + mspChildrens.erase(pC); + } + else + break; + } + + // If a children has no covisibility links with any parent candidate, assign to the original parent of this KF + if(!mspChildrens.empty()) + for(set::iterator sit=mspChildrens.begin(); sit!=mspChildrens.end(); sit++) + { + (*sit)->ChangeParent(mpParent); + } + + mpParent->EraseChild(this); + mTcp = Tcw*mpParent->GetPoseInverse(); + mbBad = true; + } + + + mpMap->EraseKeyFrame(this); + mpKeyFrameDB->erase(this); +} + +bool KeyFrame::isBad() +{ + unique_lock lock(mMutexConnections); + return mbBad; +} + +void KeyFrame::EraseConnection(KeyFrame* pKF) +{ + bool bUpdate = false; + { + unique_lock lock(mMutexConnections); + if(mConnectedKeyFrameWeights.count(pKF)) + { + mConnectedKeyFrameWeights.erase(pKF); + bUpdate=true; + } + } + + if(bUpdate) + UpdateBestCovisibles(); +} + +vector KeyFrame::GetFeaturesInArea(const float &x, const float &y, const float &r) const +{ + vector vIndices; + vIndices.reserve(N); + + const int nMinCellX = max(0,(int)floor((x-mnMinX-r)*mfGridElementWidthInv)); + if(nMinCellX>=mnGridCols) + return vIndices; + + const int nMaxCellX = min((int)mnGridCols-1,(int)ceil((x-mnMinX+r)*mfGridElementWidthInv)); + if(nMaxCellX<0) + return vIndices; + + const int nMinCellY = max(0,(int)floor((y-mnMinY-r)*mfGridElementHeightInv)); + if(nMinCellY>=mnGridRows) + return vIndices; + + const int nMaxCellY = min((int)mnGridRows-1,(int)ceil((y-mnMinY+r)*mfGridElementHeightInv)); + if(nMaxCellY<0) + return vIndices; + + for(int ix = nMinCellX; ix<=nMaxCellX; ix++) + { + for(int iy = nMinCellY; iy<=nMaxCellY; iy++) + { + const vector vCell = mGrid[ix][iy]; + for(size_t j=0, jend=vCell.size(); j=mnMinX && x=mnMinY && y0) + { + const float u = mvKeys[i].pt.x; + const float v = mvKeys[i].pt.y; + const float x = (u-cx)*z*invfx; + const float y = (v-cy)*z*invfy; + cv::Mat x3Dc = (cv::Mat_(3,1) << x, y, z); + + unique_lock lock(mMutexPose); + return Twc.rowRange(0,3).colRange(0,3)*x3Dc+Twc.rowRange(0,3).col(3); + } + else + return cv::Mat(); +} + +float KeyFrame::ComputeSceneMedianDepth(const int q) +{ + vector vpMapPoints; + cv::Mat Tcw_; + { + unique_lock lock(mMutexFeatures); + unique_lock lock2(mMutexPose); + vpMapPoints = mvpMapPoints; + Tcw_ = Tcw.clone(); + } + + vector vDepths; + vDepths.reserve(N); + cv::Mat Rcw2 = Tcw_.row(2).colRange(0,3); + Rcw2 = Rcw2.t(); + float zcw = Tcw_.at(2,3); + for(int i=0; iGetWorldPos(); + float z = Rcw2.dot(x3Dw)+zcw; + vDepths.push_back(z); + } + } + + sort(vDepths.begin(),vDepths.end()); + + return vDepths[(vDepths.size()-1)/q]; +} + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/KeyFrameDatabase.cc b/for_orb_slam2/src/KeyFrameDatabase.cc new file mode 100644 index 00000000000..826860cab9e --- /dev/null +++ b/for_orb_slam2/src/KeyFrameDatabase.cc @@ -0,0 +1,311 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#include "KeyFrameDatabase.h" + +#include "KeyFrame.h" +#include "Thirdparty/DBoW2/DBoW2/BowVector.h" + +#include + +using namespace std; + +namespace ORB_SLAM2 +{ + +KeyFrameDatabase::KeyFrameDatabase (const ORBVocabulary &voc): + mpVoc(&voc) +{ + mvInvertedFile.resize(voc.size()); +} + + +void KeyFrameDatabase::add(KeyFrame *pKF) +{ + unique_lock lock(mMutex); + + for(DBoW2::BowVector::const_iterator vit= pKF->mBowVec.begin(), vend=pKF->mBowVec.end(); vit!=vend; vit++) + mvInvertedFile[vit->first].push_back(pKF); +} + +void KeyFrameDatabase::erase(KeyFrame* pKF) +{ + unique_lock lock(mMutex); + + // Erase elements in the Inverse File for the entry + for(DBoW2::BowVector::const_iterator vit=pKF->mBowVec.begin(), vend=pKF->mBowVec.end(); vit!=vend; vit++) + { + // List of keyframes that share the word + list &lKFs = mvInvertedFile[vit->first]; + + for(list::iterator lit=lKFs.begin(), lend= lKFs.end(); lit!=lend; lit++) + { + if(pKF==*lit) + { + lKFs.erase(lit); + break; + } + } + } +} + +void KeyFrameDatabase::clear() +{ + mvInvertedFile.clear(); + mvInvertedFile.resize(mpVoc->size()); +} + + +vector KeyFrameDatabase::DetectLoopCandidates(KeyFrame* pKF, float minScore) +{ + set spConnectedKeyFrames = pKF->GetConnectedKeyFrames(); + list lKFsSharingWords; + + // Search all keyframes that share a word with current keyframes + // Discard keyframes connected to the query keyframe + { + unique_lock lock(mMutex); + + for(DBoW2::BowVector::const_iterator vit=pKF->mBowVec.begin(), vend=pKF->mBowVec.end(); vit != vend; vit++) + { + list &lKFs = mvInvertedFile[vit->first]; + + for(list::iterator lit=lKFs.begin(), lend= lKFs.end(); lit!=lend; lit++) + { + KeyFrame* pKFi=*lit; + if(pKFi->mnLoopQuery!=pKF->mnId) + { + pKFi->mnLoopWords=0; + if(!spConnectedKeyFrames.count(pKFi)) + { + pKFi->mnLoopQuery=pKF->mnId; + lKFsSharingWords.push_back(pKFi); + } + } + pKFi->mnLoopWords++; + } + } + } + + if(lKFsSharingWords.empty()) + return vector(); + + list > lScoreAndMatch; + + // Only compare against those keyframes that share enough words + int maxCommonWords=0; + for(list::iterator lit=lKFsSharingWords.begin(), lend= lKFsSharingWords.end(); lit!=lend; lit++) + { + if((*lit)->mnLoopWords>maxCommonWords) + maxCommonWords=(*lit)->mnLoopWords; + } + + int minCommonWords = maxCommonWords*0.8f; + + int nscores=0; + + // Compute similarity score. Retain the matches whose score is higher than minScore + for(list::iterator lit=lKFsSharingWords.begin(), lend= lKFsSharingWords.end(); lit!=lend; lit++) + { + KeyFrame* pKFi = *lit; + + if(pKFi->mnLoopWords>minCommonWords) + { + nscores++; + + float si = mpVoc->score(pKF->mBowVec,pKFi->mBowVec); + + pKFi->mLoopScore = si; + if(si>=minScore) + lScoreAndMatch.push_back(make_pair(si,pKFi)); + } + } + + if(lScoreAndMatch.empty()) + return vector(); + + list > lAccScoreAndMatch; + float bestAccScore = minScore; + + // Lets now accumulate score by covisibility + for(list >::iterator it=lScoreAndMatch.begin(), itend=lScoreAndMatch.end(); it!=itend; it++) + { + KeyFrame* pKFi = it->second; + vector vpNeighs = pKFi->GetBestCovisibilityKeyFrames(10); + + float bestScore = it->first; + float accScore = it->first; + KeyFrame* pBestKF = pKFi; + for(vector::iterator vit=vpNeighs.begin(), vend=vpNeighs.end(); vit!=vend; vit++) + { + KeyFrame* pKF2 = *vit; + if(pKF2->mnLoopQuery==pKF->mnId && pKF2->mnLoopWords>minCommonWords) + { + accScore+=pKF2->mLoopScore; + if(pKF2->mLoopScore>bestScore) + { + pBestKF=pKF2; + bestScore = pKF2->mLoopScore; + } + } + } + + lAccScoreAndMatch.push_back(make_pair(accScore,pBestKF)); + if(accScore>bestAccScore) + bestAccScore=accScore; + } + + // Return all those keyframes with a score higher than 0.75*bestScore + float minScoreToRetain = 0.75f*bestAccScore; + + set spAlreadyAddedKF; + vector vpLoopCandidates; + vpLoopCandidates.reserve(lAccScoreAndMatch.size()); + + for(list >::iterator it=lAccScoreAndMatch.begin(), itend=lAccScoreAndMatch.end(); it!=itend; it++) + { + if(it->first>minScoreToRetain) + { + KeyFrame* pKFi = it->second; + if(!spAlreadyAddedKF.count(pKFi)) + { + vpLoopCandidates.push_back(pKFi); + spAlreadyAddedKF.insert(pKFi); + } + } + } + + + return vpLoopCandidates; +} + +vector KeyFrameDatabase::DetectRelocalizationCandidates(Frame *F) +{ + list lKFsSharingWords; + + // Search all keyframes that share a word with current frame + { + unique_lock lock(mMutex); + + for(DBoW2::BowVector::const_iterator vit=F->mBowVec.begin(), vend=F->mBowVec.end(); vit != vend; vit++) + { + list &lKFs = mvInvertedFile[vit->first]; + + for(list::iterator lit=lKFs.begin(), lend= lKFs.end(); lit!=lend; lit++) + { + KeyFrame* pKFi=*lit; + if(pKFi->mnRelocQuery!=F->mnId) + { + pKFi->mnRelocWords=0; + pKFi->mnRelocQuery=F->mnId; + lKFsSharingWords.push_back(pKFi); + } + pKFi->mnRelocWords++; + } + } + } + if(lKFsSharingWords.empty()) + return vector(); + + // Only compare against those keyframes that share enough words + int maxCommonWords=0; + for(list::iterator lit=lKFsSharingWords.begin(), lend= lKFsSharingWords.end(); lit!=lend; lit++) + { + if((*lit)->mnRelocWords>maxCommonWords) + maxCommonWords=(*lit)->mnRelocWords; + } + + int minCommonWords = maxCommonWords*0.8f; + + list > lScoreAndMatch; + + int nscores=0; + + // Compute similarity score. + for(list::iterator lit=lKFsSharingWords.begin(), lend= lKFsSharingWords.end(); lit!=lend; lit++) + { + KeyFrame* pKFi = *lit; + + if(pKFi->mnRelocWords>minCommonWords) + { + nscores++; + float si = mpVoc->score(F->mBowVec,pKFi->mBowVec); + pKFi->mRelocScore=si; + lScoreAndMatch.push_back(make_pair(si,pKFi)); + } + } + + if(lScoreAndMatch.empty()) + return vector(); + + list > lAccScoreAndMatch; + float bestAccScore = 0; + + // Lets now accumulate score by covisibility + for(list >::iterator it=lScoreAndMatch.begin(), itend=lScoreAndMatch.end(); it!=itend; it++) + { + KeyFrame* pKFi = it->second; + vector vpNeighs = pKFi->GetBestCovisibilityKeyFrames(10); + + float bestScore = it->first; + float accScore = bestScore; + KeyFrame* pBestKF = pKFi; + for(vector::iterator vit=vpNeighs.begin(), vend=vpNeighs.end(); vit!=vend; vit++) + { + KeyFrame* pKF2 = *vit; + if(pKF2->mnRelocQuery!=F->mnId) + continue; + + accScore+=pKF2->mRelocScore; + if(pKF2->mRelocScore>bestScore) + { + pBestKF=pKF2; + bestScore = pKF2->mRelocScore; + } + + } + lAccScoreAndMatch.push_back(make_pair(accScore,pBestKF)); + if(accScore>bestAccScore) + bestAccScore=accScore; + } + + // Return all those keyframes with a score higher than 0.75*bestScore + float minScoreToRetain = 0.75f*bestAccScore; + set spAlreadyAddedKF; + vector vpRelocCandidates; + vpRelocCandidates.reserve(lAccScoreAndMatch.size()); + for(list >::iterator it=lAccScoreAndMatch.begin(), itend=lAccScoreAndMatch.end(); it!=itend; it++) + { + const float &si = it->first; + if(si>minScoreToRetain) + { + KeyFrame* pKFi = it->second; + if(!spAlreadyAddedKF.count(pKFi)) + { + vpRelocCandidates.push_back(pKFi); + spAlreadyAddedKF.insert(pKFi); + } + } + } + + return vpRelocCandidates; +} + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/LocalMapping.cc b/for_orb_slam2/src/LocalMapping.cc new file mode 100644 index 00000000000..6c87a6e55a4 --- /dev/null +++ b/for_orb_slam2/src/LocalMapping.cc @@ -0,0 +1,760 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#include "LocalMapping.h" +#include "LoopClosing.h" +#include "ORBmatcher.h" +#include "Optimizer.h" + +#include + +namespace ORB_SLAM2 +{ + +LocalMapping::LocalMapping(Map *pMap, const float bMonocular): + mbMonocular(bMonocular), mbResetRequested(false), mbFinishRequested(false), mbFinished(true), mpMap(pMap), + mbAbortBA(false), mbStopped(false), mbStopRequested(false), mbNotStop(false), mbAcceptKeyFrames(true) +{ +} + +void LocalMapping::SetLoopCloser(LoopClosing* pLoopCloser) +{ + mpLoopCloser = pLoopCloser; +} + +void LocalMapping::SetTracker(Tracking *pTracker) +{ + mpTracker=pTracker; +} + +void LocalMapping::Run() +{ + + mbFinished = false; + + while(1) + { + // Tracking will see that Local Mapping is busy + SetAcceptKeyFrames(false); + + // Check if there are keyframes in the queue + if(CheckNewKeyFrames()) + { + // BoW conversion and insertion in Map + ProcessNewKeyFrame(); + + // Check recent MapPoints + MapPointCulling(); + + // Triangulate new MapPoints + CreateNewMapPoints(); + + if(!CheckNewKeyFrames()) + { + // Find more matches in neighbor keyframes and fuse point duplications + SearchInNeighbors(); + } + + mbAbortBA = false; + + if(!CheckNewKeyFrames() && !stopRequested()) + { + // Local BA + if(mpMap->KeyFramesInMap()>2) + Optimizer::LocalBundleAdjustment(mpCurrentKeyFrame,&mbAbortBA, mpMap); + + // Check redundant local Keyframes + KeyFrameCulling(); + } + + mpLoopCloser->InsertKeyFrame(mpCurrentKeyFrame); + } + else if(Stop()) + { + // Safe area to stop + while(isStopped() && !CheckFinish()) + { + usleep(3000); + } + if(CheckFinish()) + break; + } + + ResetIfRequested(); + + // Tracking will see that Local Mapping is busy + SetAcceptKeyFrames(true); + + if(CheckFinish()) + break; + + usleep(3000); + } + + SetFinish(); +} + +void LocalMapping::InsertKeyFrame(KeyFrame *pKF) +{ + unique_lock lock(mMutexNewKFs); + mlNewKeyFrames.push_back(pKF); + mbAbortBA=true; +} + + +bool LocalMapping::CheckNewKeyFrames() +{ + unique_lock lock(mMutexNewKFs); + return(!mlNewKeyFrames.empty()); +} + +void LocalMapping::ProcessNewKeyFrame() +{ + { + unique_lock lock(mMutexNewKFs); + mpCurrentKeyFrame = mlNewKeyFrames.front(); + mlNewKeyFrames.pop_front(); + } + + // Compute Bags of Words structures + mpCurrentKeyFrame->ComputeBoW(); + + // Associate MapPoints to the new keyframe and update normal and descriptor + const vector vpMapPointMatches = mpCurrentKeyFrame->GetMapPointMatches(); + + for(size_t i=0; iisBad()) + { + if(!pMP->IsInKeyFrame(mpCurrentKeyFrame)) + { + pMP->AddObservation(mpCurrentKeyFrame, i); + pMP->UpdateNormalAndDepth(); + pMP->ComputeDistinctiveDescriptors(); + } + else // this can only happen for new stereo points inserted by the Tracking + { + mlpRecentAddedMapPoints.push_back(pMP); + } + } + } + } + + // Update links in the Covisibility Graph + mpCurrentKeyFrame->UpdateConnections(); + + // Insert Keyframe in Map + mpMap->AddKeyFrame(mpCurrentKeyFrame); +} + +void LocalMapping::MapPointCulling() +{ + // Check Recent Added MapPoints + list::iterator lit = mlpRecentAddedMapPoints.begin(); + const unsigned long int nCurrentKFid = mpCurrentKeyFrame->mnId; + + int nThObs; + if(mbMonocular) + nThObs = 2; + else + nThObs = 3; + const int cnThObs = nThObs; + + while(lit!=mlpRecentAddedMapPoints.end()) + { + MapPoint* pMP = *lit; + if(pMP->isBad()) + { + lit = mlpRecentAddedMapPoints.erase(lit); + } + else if(pMP->GetFoundRatio()<0.25f ) + { + pMP->SetBadFlag(); + lit = mlpRecentAddedMapPoints.erase(lit); + } + else if(((int)nCurrentKFid-(int)pMP->mnFirstKFid)>=2 && pMP->Observations()<=cnThObs) + { + pMP->SetBadFlag(); + lit = mlpRecentAddedMapPoints.erase(lit); + } + else if(((int)nCurrentKFid-(int)pMP->mnFirstKFid)>=3) + lit = mlpRecentAddedMapPoints.erase(lit); + else + lit++; + } +} + +void LocalMapping::CreateNewMapPoints() +{ + // Retrieve neighbor keyframes in covisibility graph + int nn = 10; + if(mbMonocular) + nn=20; + const vector vpNeighKFs = mpCurrentKeyFrame->GetBestCovisibilityKeyFrames(nn); + + ORBmatcher matcher(0.6,false); + + cv::Mat Rcw1 = mpCurrentKeyFrame->GetRotation(); + cv::Mat Rwc1 = Rcw1.t(); + cv::Mat tcw1 = mpCurrentKeyFrame->GetTranslation(); + cv::Mat Tcw1(3,4,CV_32F); + Rcw1.copyTo(Tcw1.colRange(0,3)); + tcw1.copyTo(Tcw1.col(3)); + cv::Mat Ow1 = mpCurrentKeyFrame->GetCameraCenter(); + + const float &fx1 = mpCurrentKeyFrame->fx; + const float &fy1 = mpCurrentKeyFrame->fy; + const float &cx1 = mpCurrentKeyFrame->cx; + const float &cy1 = mpCurrentKeyFrame->cy; + const float &invfx1 = mpCurrentKeyFrame->invfx; + const float &invfy1 = mpCurrentKeyFrame->invfy; + + const float ratioFactor = 1.5f*mpCurrentKeyFrame->mfScaleFactor; + + int nnew=0; + + // Search matches with epipolar restriction and triangulate + for(size_t i=0; i0 && CheckNewKeyFrames()) + return; + + KeyFrame* pKF2 = vpNeighKFs[i]; + + // Check first that baseline is not too short + cv::Mat Ow2 = pKF2->GetCameraCenter(); + cv::Mat vBaseline = Ow2-Ow1; + const float baseline = cv::norm(vBaseline); + + if(!mbMonocular) + { + if(baselinemb) + continue; + } + else + { + const float medianDepthKF2 = pKF2->ComputeSceneMedianDepth(2); + const float ratioBaselineDepth = baseline/medianDepthKF2; + + if(ratioBaselineDepth<0.01) + continue; + } + + // Compute Fundamental Matrix + cv::Mat F12 = ComputeF12(mpCurrentKeyFrame,pKF2); + + // Search matches that fullfil epipolar constraint + vector > vMatchedIndices; + matcher.SearchForTriangulation(mpCurrentKeyFrame,pKF2,F12,vMatchedIndices,false); + + cv::Mat Rcw2 = pKF2->GetRotation(); + cv::Mat Rwc2 = Rcw2.t(); + cv::Mat tcw2 = pKF2->GetTranslation(); + cv::Mat Tcw2(3,4,CV_32F); + Rcw2.copyTo(Tcw2.colRange(0,3)); + tcw2.copyTo(Tcw2.col(3)); + + const float &fx2 = pKF2->fx; + const float &fy2 = pKF2->fy; + const float &cx2 = pKF2->cx; + const float &cy2 = pKF2->cy; + const float &invfx2 = pKF2->invfx; + const float &invfy2 = pKF2->invfy; + + // Triangulate each match + const int nmatches = vMatchedIndices.size(); + for(int ikp=0; ikpmvKeysUn[idx1]; + const float kp1_ur=mpCurrentKeyFrame->mvuRight[idx1]; + bool bStereo1 = kp1_ur>=0; + + const cv::KeyPoint &kp2 = pKF2->mvKeysUn[idx2]; + const float kp2_ur = pKF2->mvuRight[idx2]; + bool bStereo2 = kp2_ur>=0; + + // Check parallax between rays + cv::Mat xn1 = (cv::Mat_(3,1) << (kp1.pt.x-cx1)*invfx1, (kp1.pt.y-cy1)*invfy1, 1.0); + cv::Mat xn2 = (cv::Mat_(3,1) << (kp2.pt.x-cx2)*invfx2, (kp2.pt.y-cy2)*invfy2, 1.0); + + cv::Mat ray1 = Rwc1*xn1; + cv::Mat ray2 = Rwc2*xn2; + const float cosParallaxRays = ray1.dot(ray2)/(cv::norm(ray1)*cv::norm(ray2)); + + float cosParallaxStereo = cosParallaxRays+1; + float cosParallaxStereo1 = cosParallaxStereo; + float cosParallaxStereo2 = cosParallaxStereo; + + if(bStereo1) + cosParallaxStereo1 = cos(2*atan2(mpCurrentKeyFrame->mb/2,mpCurrentKeyFrame->mvDepth[idx1])); + else if(bStereo2) + cosParallaxStereo2 = cos(2*atan2(pKF2->mb/2,pKF2->mvDepth[idx2])); + + cosParallaxStereo = min(cosParallaxStereo1,cosParallaxStereo2); + + cv::Mat x3D; + if(cosParallaxRays0 && (bStereo1 || bStereo2 || cosParallaxRays<0.9998)) + { + // Linear Triangulation Method + cv::Mat A(4,4,CV_32F); + A.row(0) = xn1.at(0)*Tcw1.row(2)-Tcw1.row(0); + A.row(1) = xn1.at(1)*Tcw1.row(2)-Tcw1.row(1); + A.row(2) = xn2.at(0)*Tcw2.row(2)-Tcw2.row(0); + A.row(3) = xn2.at(1)*Tcw2.row(2)-Tcw2.row(1); + + cv::Mat w,u,vt; + cv::SVD::compute(A,w,u,vt,cv::SVD::MODIFY_A| cv::SVD::FULL_UV); + + x3D = vt.row(3).t(); + + if(x3D.at(3)==0) + continue; + + // Euclidean coordinates + x3D = x3D.rowRange(0,3)/x3D.at(3); + + } + else if(bStereo1 && cosParallaxStereo1UnprojectStereo(idx1); + } + else if(bStereo2 && cosParallaxStereo2UnprojectStereo(idx2); + } + else + continue; //No stereo and very low parallax + + cv::Mat x3Dt = x3D.t(); + + //Check triangulation in front of cameras + float z1 = Rcw1.row(2).dot(x3Dt)+tcw1.at(2); + if(z1<=0) + continue; + + float z2 = Rcw2.row(2).dot(x3Dt)+tcw2.at(2); + if(z2<=0) + continue; + + //Check reprojection error in first keyframe + const float &sigmaSquare1 = mpCurrentKeyFrame->mvLevelSigma2[kp1.octave]; + const float x1 = Rcw1.row(0).dot(x3Dt)+tcw1.at(0); + const float y1 = Rcw1.row(1).dot(x3Dt)+tcw1.at(1); + const float invz1 = 1.0/z1; + + if(!bStereo1) + { + float u1 = fx1*x1*invz1+cx1; + float v1 = fy1*y1*invz1+cy1; + float errX1 = u1 - kp1.pt.x; + float errY1 = v1 - kp1.pt.y; + if((errX1*errX1+errY1*errY1)>5.991*sigmaSquare1) + continue; + } + else + { + float u1 = fx1*x1*invz1+cx1; + float u1_r = u1 - mpCurrentKeyFrame->mbf*invz1; + float v1 = fy1*y1*invz1+cy1; + float errX1 = u1 - kp1.pt.x; + float errY1 = v1 - kp1.pt.y; + float errX1_r = u1_r - kp1_ur; + if((errX1*errX1+errY1*errY1+errX1_r*errX1_r)>7.8*sigmaSquare1) + continue; + } + + //Check reprojection error in second keyframe + const float sigmaSquare2 = pKF2->mvLevelSigma2[kp2.octave]; + const float x2 = Rcw2.row(0).dot(x3Dt)+tcw2.at(0); + const float y2 = Rcw2.row(1).dot(x3Dt)+tcw2.at(1); + const float invz2 = 1.0/z2; + if(!bStereo2) + { + float u2 = fx2*x2*invz2+cx2; + float v2 = fy2*y2*invz2+cy2; + float errX2 = u2 - kp2.pt.x; + float errY2 = v2 - kp2.pt.y; + if((errX2*errX2+errY2*errY2)>5.991*sigmaSquare2) + continue; + } + else + { + float u2 = fx2*x2*invz2+cx2; + float u2_r = u2 - mpCurrentKeyFrame->mbf*invz2; + float v2 = fy2*y2*invz2+cy2; + float errX2 = u2 - kp2.pt.x; + float errY2 = v2 - kp2.pt.y; + float errX2_r = u2_r - kp2_ur; + if((errX2*errX2+errY2*errY2+errX2_r*errX2_r)>7.8*sigmaSquare2) + continue; + } + + //Check scale consistency + cv::Mat normal1 = x3D-Ow1; + float dist1 = cv::norm(normal1); + + cv::Mat normal2 = x3D-Ow2; + float dist2 = cv::norm(normal2); + + if(dist1==0 || dist2==0) + continue; + + const float ratioDist = dist2/dist1; + const float ratioOctave = mpCurrentKeyFrame->mvScaleFactors[kp1.octave]/pKF2->mvScaleFactors[kp2.octave]; + + /*if(fabs(ratioDist-ratioOctave)>ratioFactor) + continue;*/ + if(ratioDist*ratioFactorratioOctave*ratioFactor) + continue; + + // Triangulation is succesfull + MapPoint* pMP = new MapPoint(x3D,mpCurrentKeyFrame,mpMap); + + pMP->AddObservation(mpCurrentKeyFrame,idx1); + pMP->AddObservation(pKF2,idx2); + + mpCurrentKeyFrame->AddMapPoint(pMP,idx1); + pKF2->AddMapPoint(pMP,idx2); + + pMP->ComputeDistinctiveDescriptors(); + + pMP->UpdateNormalAndDepth(); + + mpMap->AddMapPoint(pMP); + mlpRecentAddedMapPoints.push_back(pMP); + + nnew++; + } + } +} + +void LocalMapping::SearchInNeighbors() +{ + // Retrieve neighbor keyframes + int nn = 10; + if(mbMonocular) + nn=20; + const vector vpNeighKFs = mpCurrentKeyFrame->GetBestCovisibilityKeyFrames(nn); + vector vpTargetKFs; + for(vector::const_iterator vit=vpNeighKFs.begin(), vend=vpNeighKFs.end(); vit!=vend; vit++) + { + KeyFrame* pKFi = *vit; + if(pKFi->isBad() || pKFi->mnFuseTargetForKF == mpCurrentKeyFrame->mnId) + continue; + vpTargetKFs.push_back(pKFi); + pKFi->mnFuseTargetForKF = mpCurrentKeyFrame->mnId; + + // Extend to some second neighbors + const vector vpSecondNeighKFs = pKFi->GetBestCovisibilityKeyFrames(5); + for(vector::const_iterator vit2=vpSecondNeighKFs.begin(), vend2=vpSecondNeighKFs.end(); vit2!=vend2; vit2++) + { + KeyFrame* pKFi2 = *vit2; + if(pKFi2->isBad() || pKFi2->mnFuseTargetForKF==mpCurrentKeyFrame->mnId || pKFi2->mnId==mpCurrentKeyFrame->mnId) + continue; + vpTargetKFs.push_back(pKFi2); + } + } + + + // Search matches by projection from current KF in target KFs + ORBmatcher matcher; + vector vpMapPointMatches = mpCurrentKeyFrame->GetMapPointMatches(); + for(vector::iterator vit=vpTargetKFs.begin(), vend=vpTargetKFs.end(); vit!=vend; vit++) + { + KeyFrame* pKFi = *vit; + + matcher.Fuse(pKFi,vpMapPointMatches); + } + + // Search matches by projection from target KFs in current KF + vector vpFuseCandidates; + vpFuseCandidates.reserve(vpTargetKFs.size()*vpMapPointMatches.size()); + + for(vector::iterator vitKF=vpTargetKFs.begin(), vendKF=vpTargetKFs.end(); vitKF!=vendKF; vitKF++) + { + KeyFrame* pKFi = *vitKF; + + vector vpMapPointsKFi = pKFi->GetMapPointMatches(); + + for(vector::iterator vitMP=vpMapPointsKFi.begin(), vendMP=vpMapPointsKFi.end(); vitMP!=vendMP; vitMP++) + { + MapPoint* pMP = *vitMP; + if(!pMP) + continue; + if(pMP->isBad() || pMP->mnFuseCandidateForKF == mpCurrentKeyFrame->mnId) + continue; + pMP->mnFuseCandidateForKF = mpCurrentKeyFrame->mnId; + vpFuseCandidates.push_back(pMP); + } + } + + matcher.Fuse(mpCurrentKeyFrame,vpFuseCandidates); + + + // Update points + vpMapPointMatches = mpCurrentKeyFrame->GetMapPointMatches(); + for(size_t i=0, iend=vpMapPointMatches.size(); iisBad()) + { + pMP->ComputeDistinctiveDescriptors(); + pMP->UpdateNormalAndDepth(); + } + } + } + + // Update connections in covisibility graph + mpCurrentKeyFrame->UpdateConnections(); +} + +cv::Mat LocalMapping::ComputeF12(KeyFrame *&pKF1, KeyFrame *&pKF2) +{ + cv::Mat R1w = pKF1->GetRotation(); + cv::Mat t1w = pKF1->GetTranslation(); + cv::Mat R2w = pKF2->GetRotation(); + cv::Mat t2w = pKF2->GetTranslation(); + + cv::Mat R12 = R1w*R2w.t(); + cv::Mat t12 = -R1w*R2w.t()*t2w+t1w; + + cv::Mat t12x = SkewSymmetricMatrix(t12); + + const cv::Mat &K1 = pKF1->mK; + const cv::Mat &K2 = pKF2->mK; + + + return K1.t().inv()*t12x*R12*K2.inv(); +} + +void LocalMapping::RequestStop() +{ + unique_lock lock(mMutexStop); + mbStopRequested = true; + unique_lock lock2(mMutexNewKFs); + mbAbortBA = true; +} + +bool LocalMapping::Stop() +{ + unique_lock lock(mMutexStop); + if(mbStopRequested && !mbNotStop) + { + mbStopped = true; + cout << "Local Mapping STOP" << endl; + return true; + } + + return false; +} + +bool LocalMapping::isStopped() +{ + unique_lock lock(mMutexStop); + return mbStopped; +} + +bool LocalMapping::stopRequested() +{ + unique_lock lock(mMutexStop); + return mbStopRequested; +} + +void LocalMapping::Release() +{ + unique_lock lock(mMutexStop); + unique_lock lock2(mMutexFinish); + if(mbFinished) + return; + mbStopped = false; + mbStopRequested = false; + for(list::iterator lit = mlNewKeyFrames.begin(), lend=mlNewKeyFrames.end(); lit!=lend; lit++) + delete *lit; + mlNewKeyFrames.clear(); + + cout << "Local Mapping RELEASE" << endl; +} + +bool LocalMapping::AcceptKeyFrames() +{ + unique_lock lock(mMutexAccept); + return mbAcceptKeyFrames; +} + +void LocalMapping::SetAcceptKeyFrames(bool flag) +{ + unique_lock lock(mMutexAccept); + mbAcceptKeyFrames=flag; +} + +bool LocalMapping::SetNotStop(bool flag) +{ + unique_lock lock(mMutexStop); + + if(flag && mbStopped) + return false; + + mbNotStop = flag; + + return true; +} + +void LocalMapping::InterruptBA() +{ + mbAbortBA = true; +} + +void LocalMapping::KeyFrameCulling() +{ + // Check redundant keyframes (only local keyframes) + // A keyframe is considered redundant if the 90% of the MapPoints it sees, are seen + // in at least other 3 keyframes (in the same or finer scale) + // We only consider close stereo points + vector vpLocalKeyFrames = mpCurrentKeyFrame->GetVectorCovisibleKeyFrames(); + + for(vector::iterator vit=vpLocalKeyFrames.begin(), vend=vpLocalKeyFrames.end(); vit!=vend; vit++) + { + KeyFrame* pKF = *vit; + if(pKF->mnId==0) + continue; + const vector vpMapPoints = pKF->GetMapPointMatches(); + + int nObs = 3; + const int thObs=nObs; + int nRedundantObservations=0; + int nMPs=0; + for(size_t i=0, iend=vpMapPoints.size(); iisBad()) + { + if(!mbMonocular) + { + if(pKF->mvDepth[i]>pKF->mThDepth || pKF->mvDepth[i]<0) + continue; + } + + nMPs++; + if(pMP->Observations()>thObs) + { + const int &scaleLevel = pKF->mvKeysUn[i].octave; + const map observations = pMP->GetObservations(); + int nObs=0; + for(map::const_iterator mit=observations.begin(), mend=observations.end(); mit!=mend; mit++) + { + KeyFrame* pKFi = mit->first; + if(pKFi==pKF) + continue; + const int &scaleLeveli = pKFi->mvKeysUn[mit->second].octave; + + if(scaleLeveli<=scaleLevel+1) + { + nObs++; + if(nObs>=thObs) + break; + } + } + if(nObs>=thObs) + { + nRedundantObservations++; + } + } + } + } + } + + if(nRedundantObservations>0.9*nMPs) + pKF->SetBadFlag(); + } +} + +cv::Mat LocalMapping::SkewSymmetricMatrix(const cv::Mat &v) +{ + return (cv::Mat_(3,3) << 0, -v.at(2), v.at(1), + v.at(2), 0,-v.at(0), + -v.at(1), v.at(0), 0); +} + +void LocalMapping::RequestReset() +{ + { + unique_lock lock(mMutexReset); + mbResetRequested = true; + } + + while(1) + { + { + unique_lock lock2(mMutexReset); + if(!mbResetRequested) + break; + } + usleep(3000); + } +} + +void LocalMapping::ResetIfRequested() +{ + unique_lock lock(mMutexReset); + if(mbResetRequested) + { + mlNewKeyFrames.clear(); + mlpRecentAddedMapPoints.clear(); + mbResetRequested=false; + } +} + +void LocalMapping::RequestFinish() +{ + unique_lock lock(mMutexFinish); + mbFinishRequested = true; +} + +bool LocalMapping::CheckFinish() +{ + unique_lock lock(mMutexFinish); + return mbFinishRequested; +} + +void LocalMapping::SetFinish() +{ + unique_lock lock(mMutexFinish); + mbFinished = true; + unique_lock lock2(mMutexStop); + mbStopped = true; +} + +bool LocalMapping::isFinished() +{ + unique_lock lock(mMutexFinish); + return mbFinished; +} + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/LoopClosing.cc b/for_orb_slam2/src/LoopClosing.cc new file mode 100644 index 00000000000..5e317dd4202 --- /dev/null +++ b/for_orb_slam2/src/LoopClosing.cc @@ -0,0 +1,776 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#include "LoopClosing.h" + +#include "Sim3Solver.h" + +#include "Converter.h" + +#include "Optimizer.h" + +#include "ORBmatcher.h" + +#include +#include + + +namespace ORB_SLAM2 +{ + +LoopClosing::LoopClosing(Map *pMap, KeyFrameDatabase *pDB, ORBVocabulary *pVoc, const bool bFixScale): + mbResetRequested(false), mbFinishRequested(false), mbFinished(true), mpMap(pMap), + mpKeyFrameDB(pDB), mpORBVocabulary(pVoc), mpMatchedKF(NULL), mLastLoopKFid(0), mbRunningGBA(false), mbFinishedGBA(true), + mbStopGBA(false), mpThreadGBA(NULL), mbFixScale(bFixScale), mnFullBAIdx(0) +{ + mnCovisibilityConsistencyTh = 3; +} + +void LoopClosing::SetTracker(Tracking *pTracker) +{ + mpTracker=pTracker; +} + +void LoopClosing::SetLocalMapper(LocalMapping *pLocalMapper) +{ + mpLocalMapper=pLocalMapper; +} + + +void LoopClosing::Run() +{ + mbFinished =false; + + while(1) + { + // Check if there are keyframes in the queue + if(CheckNewKeyFrames()) + { + // Detect loop candidates and check covisibility consistency + if(DetectLoop()) + { + // Compute similarity transformation [sR|t] + // In the stereo/RGBD case s=1 + if(ComputeSim3()) + { + // Perform loop fusion and pose graph optimization + CorrectLoop(); + } + } + } + + ResetIfRequested(); + + if(CheckFinish()) + break; + + usleep(5000); + } + + SetFinish(); +} + +void LoopClosing::InsertKeyFrame(KeyFrame *pKF) +{ + unique_lock lock(mMutexLoopQueue); + if(pKF->mnId!=0) + mlpLoopKeyFrameQueue.push_back(pKF); +} + +bool LoopClosing::CheckNewKeyFrames() +{ + unique_lock lock(mMutexLoopQueue); + return(!mlpLoopKeyFrameQueue.empty()); +} + +bool LoopClosing::DetectLoop() +{ + { + unique_lock lock(mMutexLoopQueue); + mpCurrentKF = mlpLoopKeyFrameQueue.front(); + mlpLoopKeyFrameQueue.pop_front(); + // Avoid that a keyframe can be erased while it is being process by this thread + mpCurrentKF->SetNotErase(); + } + + //If the map contains less than 10 KF or less than 10 KF have passed from last loop detection + if(mpCurrentKF->mnIdadd(mpCurrentKF); + mpCurrentKF->SetErase(); + return false; + } + + // Compute reference BoW similarity score + // This is the lowest score to a connected keyframe in the covisibility graph + // We will impose loop candidates to have a higher similarity than this + const vector vpConnectedKeyFrames = mpCurrentKF->GetVectorCovisibleKeyFrames(); + const DBoW2::BowVector &CurrentBowVec = mpCurrentKF->mBowVec; + float minScore = 1; + for(size_t i=0; iisBad()) + continue; + const DBoW2::BowVector &BowVec = pKF->mBowVec; + + float score = mpORBVocabulary->score(CurrentBowVec, BowVec); + + if(score vpCandidateKFs = mpKeyFrameDB->DetectLoopCandidates(mpCurrentKF, minScore); + + // If there are no loop candidates, just add new keyframe and return false + if(vpCandidateKFs.empty()) + { + mpKeyFrameDB->add(mpCurrentKF); + mvConsistentGroups.clear(); + mpCurrentKF->SetErase(); + return false; + } + + // For each loop candidate check consistency with previous loop candidates + // Each candidate expands a covisibility group (keyframes connected to the loop candidate in the covisibility graph) + // A group is consistent with a previous group if they share at least a keyframe + // We must detect a consistent loop in several consecutive keyframes to accept it + mvpEnoughConsistentCandidates.clear(); + + vector vCurrentConsistentGroups; + vector vbConsistentGroup(mvConsistentGroups.size(),false); + for(size_t i=0, iend=vpCandidateKFs.size(); i spCandidateGroup = pCandidateKF->GetConnectedKeyFrames(); + spCandidateGroup.insert(pCandidateKF); + + bool bEnoughConsistent = false; + bool bConsistentForSomeGroup = false; + for(size_t iG=0, iendG=mvConsistentGroups.size(); iG sPreviousGroup = mvConsistentGroups[iG].first; + + bool bConsistent = false; + for(set::iterator sit=spCandidateGroup.begin(), send=spCandidateGroup.end(); sit!=send;sit++) + { + if(sPreviousGroup.count(*sit)) + { + bConsistent=true; + bConsistentForSomeGroup=true; + break; + } + } + + if(bConsistent) + { + int nPreviousConsistency = mvConsistentGroups[iG].second; + int nCurrentConsistency = nPreviousConsistency + 1; + if(!vbConsistentGroup[iG]) + { + ConsistentGroup cg = make_pair(spCandidateGroup,nCurrentConsistency); + vCurrentConsistentGroups.push_back(cg); + vbConsistentGroup[iG]=true; //this avoid to include the same group more than once + } + if(nCurrentConsistency>=mnCovisibilityConsistencyTh && !bEnoughConsistent) + { + mvpEnoughConsistentCandidates.push_back(pCandidateKF); + bEnoughConsistent=true; //this avoid to insert the same candidate more than once + } + } + } + + // If the group is not consistent with any previous group insert with consistency counter set to zero + if(!bConsistentForSomeGroup) + { + ConsistentGroup cg = make_pair(spCandidateGroup,0); + vCurrentConsistentGroups.push_back(cg); + } + } + + // Update Covisibility Consistent Groups + mvConsistentGroups = vCurrentConsistentGroups; + + + // Add Current Keyframe to database + mpKeyFrameDB->add(mpCurrentKF); + + if(mvpEnoughConsistentCandidates.empty()) + { + mpCurrentKF->SetErase(); + return false; + } + else + { + return true; + } + + mpCurrentKF->SetErase(); + return false; +} + +bool LoopClosing::ComputeSim3() +{ + // For each consistent loop candidate we try to compute a Sim3 + + const int nInitialCandidates = mvpEnoughConsistentCandidates.size(); + + // We compute first ORB matches for each candidate + // If enough matches are found, we setup a Sim3Solver + ORBmatcher matcher(0.75,true); + + vector vpSim3Solvers; + vpSim3Solvers.resize(nInitialCandidates); + + vector > vvpMapPointMatches; + vvpMapPointMatches.resize(nInitialCandidates); + + vector vbDiscarded; + vbDiscarded.resize(nInitialCandidates); + + int nCandidates=0; //candidates with enough matches + + for(int i=0; iSetNotErase(); + + if(pKF->isBad()) + { + vbDiscarded[i] = true; + continue; + } + + int nmatches = matcher.SearchByBoW(mpCurrentKF,pKF,vvpMapPointMatches[i]); + + if(nmatches<20) + { + vbDiscarded[i] = true; + continue; + } + else + { + Sim3Solver* pSolver = new Sim3Solver(mpCurrentKF,pKF,vvpMapPointMatches[i],mbFixScale); + pSolver->SetRansacParameters(0.99,20,300); + vpSim3Solvers[i] = pSolver; + } + + nCandidates++; + } + + bool bMatch = false; + + // Perform alternatively RANSAC iterations for each candidate + // until one is succesful or all fail + while(nCandidates>0 && !bMatch) + { + for(int i=0; i vbInliers; + int nInliers; + bool bNoMore; + + Sim3Solver* pSolver = vpSim3Solvers[i]; + cv::Mat Scm = pSolver->iterate(5,bNoMore,vbInliers,nInliers); + + // If Ransac reachs max. iterations discard keyframe + if(bNoMore) + { + vbDiscarded[i]=true; + nCandidates--; + } + + // If RANSAC returns a Sim3, perform a guided matching and optimize with all correspondences + if(!Scm.empty()) + { + vector vpMapPointMatches(vvpMapPointMatches[i].size(), static_cast(NULL)); + for(size_t j=0, jend=vbInliers.size(); jGetEstimatedRotation(); + cv::Mat t = pSolver->GetEstimatedTranslation(); + const float s = pSolver->GetEstimatedScale(); + matcher.SearchBySim3(mpCurrentKF,pKF,vpMapPointMatches,s,R,t,7.5); + + g2o::Sim3 gScm(Converter::toMatrix3d(R),Converter::toVector3d(t),s); + const int nInliers = Optimizer::OptimizeSim3(mpCurrentKF, pKF, vpMapPointMatches, gScm, 10, mbFixScale); + + // If optimization is succesful stop ransacs and continue + if(nInliers>=20) + { + bMatch = true; + mpMatchedKF = pKF; + g2o::Sim3 gSmw(Converter::toMatrix3d(pKF->GetRotation()),Converter::toVector3d(pKF->GetTranslation()),1.0); + mg2oScw = gScm*gSmw; + mScw = Converter::toCvMat(mg2oScw); + + mvpCurrentMatchedPoints = vpMapPointMatches; + break; + } + } + } + } + + if(!bMatch) + { + for(int i=0; iSetErase(); + mpCurrentKF->SetErase(); + return false; + } + + // Retrieve MapPoints seen in Loop Keyframe and neighbors + vector vpLoopConnectedKFs = mpMatchedKF->GetVectorCovisibleKeyFrames(); + vpLoopConnectedKFs.push_back(mpMatchedKF); + mvpLoopMapPoints.clear(); + for(vector::iterator vit=vpLoopConnectedKFs.begin(); vit!=vpLoopConnectedKFs.end(); vit++) + { + KeyFrame* pKF = *vit; + vector vpMapPoints = pKF->GetMapPointMatches(); + for(size_t i=0, iend=vpMapPoints.size(); iisBad() && pMP->mnLoopPointForKF!=mpCurrentKF->mnId) + { + mvpLoopMapPoints.push_back(pMP); + pMP->mnLoopPointForKF=mpCurrentKF->mnId; + } + } + } + } + + // Find more matches projecting with the computed Sim3 + matcher.SearchByProjection(mpCurrentKF, mScw, mvpLoopMapPoints, mvpCurrentMatchedPoints,10); + + // If enough matches accept Loop + int nTotalMatches = 0; + for(size_t i=0; i=40) + { + for(int i=0; iSetErase(); + return true; + } + else + { + for(int i=0; iSetErase(); + mpCurrentKF->SetErase(); + return false; + } + +} + +void LoopClosing::CorrectLoop() +{ + cout << "Loop detected!" << endl; + + // Send a stop signal to Local Mapping + // Avoid new keyframes are inserted while correcting the loop + mpLocalMapper->RequestStop(); + + // If a Global Bundle Adjustment is running, abort it + if(isRunningGBA()) + { + unique_lock lock(mMutexGBA); + mbStopGBA = true; + + mnFullBAIdx++; + + if(mpThreadGBA) + { + mpThreadGBA->detach(); + delete mpThreadGBA; + } + } + + // Wait until Local Mapping has effectively stopped + while(!mpLocalMapper->isStopped()) + { + usleep(1000); + } + + // Ensure current keyframe is updated + mpCurrentKF->UpdateConnections(); + + // Retrive keyframes connected to the current keyframe and compute corrected Sim3 pose by propagation + mvpCurrentConnectedKFs = mpCurrentKF->GetVectorCovisibleKeyFrames(); + mvpCurrentConnectedKFs.push_back(mpCurrentKF); + + KeyFrameAndPose CorrectedSim3, NonCorrectedSim3; + CorrectedSim3[mpCurrentKF]=mg2oScw; + cv::Mat Twc = mpCurrentKF->GetPoseInverse(); + + + { + // Get Map Mutex + unique_lock lock(mpMap->mMutexMapUpdate); + + for(vector::iterator vit=mvpCurrentConnectedKFs.begin(), vend=mvpCurrentConnectedKFs.end(); vit!=vend; vit++) + { + KeyFrame* pKFi = *vit; + + cv::Mat Tiw = pKFi->GetPose(); + + if(pKFi!=mpCurrentKF) + { + cv::Mat Tic = Tiw*Twc; + cv::Mat Ric = Tic.rowRange(0,3).colRange(0,3); + cv::Mat tic = Tic.rowRange(0,3).col(3); + g2o::Sim3 g2oSic(Converter::toMatrix3d(Ric),Converter::toVector3d(tic),1.0); + g2o::Sim3 g2oCorrectedSiw = g2oSic*mg2oScw; + //Pose corrected with the Sim3 of the loop closure + CorrectedSim3[pKFi]=g2oCorrectedSiw; + } + + cv::Mat Riw = Tiw.rowRange(0,3).colRange(0,3); + cv::Mat tiw = Tiw.rowRange(0,3).col(3); + g2o::Sim3 g2oSiw(Converter::toMatrix3d(Riw),Converter::toVector3d(tiw),1.0); + //Pose without correction + NonCorrectedSim3[pKFi]=g2oSiw; + } + + // Correct all MapPoints obsrved by current keyframe and neighbors, so that they align with the other side of the loop + for(KeyFrameAndPose::iterator mit=CorrectedSim3.begin(), mend=CorrectedSim3.end(); mit!=mend; mit++) + { + KeyFrame* pKFi = mit->first; + g2o::Sim3 g2oCorrectedSiw = mit->second; + g2o::Sim3 g2oCorrectedSwi = g2oCorrectedSiw.inverse(); + + g2o::Sim3 g2oSiw =NonCorrectedSim3[pKFi]; + + vector vpMPsi = pKFi->GetMapPointMatches(); + for(size_t iMP=0, endMPi = vpMPsi.size(); iMPisBad()) + continue; + if(pMPi->mnCorrectedByKF==mpCurrentKF->mnId) + continue; + + // Project with non-corrected pose and project back with corrected pose + cv::Mat P3Dw = pMPi->GetWorldPos(); + Eigen::Matrix eigP3Dw = Converter::toVector3d(P3Dw); + Eigen::Matrix eigCorrectedP3Dw = g2oCorrectedSwi.map(g2oSiw.map(eigP3Dw)); + + cv::Mat cvCorrectedP3Dw = Converter::toCvMat(eigCorrectedP3Dw); + pMPi->SetWorldPos(cvCorrectedP3Dw); + pMPi->mnCorrectedByKF = mpCurrentKF->mnId; + pMPi->mnCorrectedReference = pKFi->mnId; + pMPi->UpdateNormalAndDepth(); + } + + // Update keyframe pose with corrected Sim3. First transform Sim3 to SE3 (scale translation) + Eigen::Matrix3d eigR = g2oCorrectedSiw.rotation().toRotationMatrix(); + Eigen::Vector3d eigt = g2oCorrectedSiw.translation(); + double s = g2oCorrectedSiw.scale(); + + eigt *=(1./s); //[R t/s;0 1] + + cv::Mat correctedTiw = Converter::toCvSE3(eigR,eigt); + + pKFi->SetPose(correctedTiw); + + // Make sure connections are updated + pKFi->UpdateConnections(); + } + + // Start Loop Fusion + // Update matched map points and replace if duplicated + for(size_t i=0; iGetMapPoint(i); + if(pCurMP) + pCurMP->Replace(pLoopMP); + else + { + mpCurrentKF->AddMapPoint(pLoopMP,i); + pLoopMP->AddObservation(mpCurrentKF,i); + pLoopMP->ComputeDistinctiveDescriptors(); + } + } + } + + } + + // Project MapPoints observed in the neighborhood of the loop keyframe + // into the current keyframe and neighbors using corrected poses. + // Fuse duplications. + SearchAndFuse(CorrectedSim3); + + + // After the MapPoint fusion, new links in the covisibility graph will appear attaching both sides of the loop + map > LoopConnections; + + for(vector::iterator vit=mvpCurrentConnectedKFs.begin(), vend=mvpCurrentConnectedKFs.end(); vit!=vend; vit++) + { + KeyFrame* pKFi = *vit; + vector vpPreviousNeighbors = pKFi->GetVectorCovisibleKeyFrames(); + + // Update connections. Detect new links. + pKFi->UpdateConnections(); + LoopConnections[pKFi]=pKFi->GetConnectedKeyFrames(); + for(vector::iterator vit_prev=vpPreviousNeighbors.begin(), vend_prev=vpPreviousNeighbors.end(); vit_prev!=vend_prev; vit_prev++) + { + LoopConnections[pKFi].erase(*vit_prev); + } + for(vector::iterator vit2=mvpCurrentConnectedKFs.begin(), vend2=mvpCurrentConnectedKFs.end(); vit2!=vend2; vit2++) + { + LoopConnections[pKFi].erase(*vit2); + } + } + + // Optimize graph + Optimizer::OptimizeEssentialGraph(mpMap, mpMatchedKF, mpCurrentKF, NonCorrectedSim3, CorrectedSim3, LoopConnections, mbFixScale); + + mpMap->InformNewBigChange(); + + // Add loop edge + mpMatchedKF->AddLoopEdge(mpCurrentKF); + mpCurrentKF->AddLoopEdge(mpMatchedKF); + + // Launch a new thread to perform Global Bundle Adjustment + mbRunningGBA = true; + mbFinishedGBA = false; + mbStopGBA = false; + mpThreadGBA = new thread(&LoopClosing::RunGlobalBundleAdjustment,this,mpCurrentKF->mnId); + + // Loop closed. Release Local Mapping. + mpLocalMapper->Release(); + + mLastLoopKFid = mpCurrentKF->mnId; +} + +void LoopClosing::SearchAndFuse(const KeyFrameAndPose &CorrectedPosesMap) +{ + ORBmatcher matcher(0.8); + + for(KeyFrameAndPose::const_iterator mit=CorrectedPosesMap.begin(), mend=CorrectedPosesMap.end(); mit!=mend;mit++) + { + KeyFrame* pKF = mit->first; + + g2o::Sim3 g2oScw = mit->second; + cv::Mat cvScw = Converter::toCvMat(g2oScw); + + vector vpReplacePoints(mvpLoopMapPoints.size(),static_cast(NULL)); + matcher.Fuse(pKF,cvScw,mvpLoopMapPoints,4,vpReplacePoints); + + // Get Map Mutex + unique_lock lock(mpMap->mMutexMapUpdate); + const int nLP = mvpLoopMapPoints.size(); + for(int i=0; iReplace(mvpLoopMapPoints[i]); + } + } + } +} + + +void LoopClosing::RequestReset() +{ + { + unique_lock lock(mMutexReset); + mbResetRequested = true; + } + + while(1) + { + { + unique_lock lock2(mMutexReset); + if(!mbResetRequested) + break; + } + usleep(5000); + } +} + +void LoopClosing::ResetIfRequested() +{ + unique_lock lock(mMutexReset); + if(mbResetRequested) + { + mlpLoopKeyFrameQueue.clear(); + mLastLoopKFid=0; + mbResetRequested=false; + } +} + +void LoopClosing::RunGlobalBundleAdjustment(unsigned long nLoopKF) +{ + cout << "Starting Global Bundle Adjustment" << endl; + + int idx = mnFullBAIdx; + Optimizer::GlobalBundleAdjustemnt(mpMap,10,&mbStopGBA,nLoopKF,false); + + // Update all MapPoints and KeyFrames + // Local Mapping was active during BA, that means that there might be new keyframes + // not included in the Global BA and they are not consistent with the updated map. + // We need to propagate the correction through the spanning tree + { + unique_lock lock(mMutexGBA); + if(idx!=mnFullBAIdx) + return; + + if(!mbStopGBA) + { + cout << "Global Bundle Adjustment finished" << endl; + cout << "Updating map ..." << endl; + mpLocalMapper->RequestStop(); + // Wait until Local Mapping has effectively stopped + + while(!mpLocalMapper->isStopped() && !mpLocalMapper->isFinished()) + { + usleep(1000); + } + + // Get Map Mutex + unique_lock lock(mpMap->mMutexMapUpdate); + + // Correct keyframes starting at map first keyframe + list lpKFtoCheck(mpMap->mvpKeyFrameOrigins.begin(),mpMap->mvpKeyFrameOrigins.end()); + + while(!lpKFtoCheck.empty()) + { + KeyFrame* pKF = lpKFtoCheck.front(); + const set sChilds = pKF->GetChilds(); + cv::Mat Twc = pKF->GetPoseInverse(); + for(set::const_iterator sit=sChilds.begin();sit!=sChilds.end();sit++) + { + KeyFrame* pChild = *sit; + if(pChild->mnBAGlobalForKF!=nLoopKF) + { + cv::Mat Tchildc = pChild->GetPose()*Twc; + pChild->mTcwGBA = Tchildc*pKF->mTcwGBA;//*Tcorc*pKF->mTcwGBA; + pChild->mnBAGlobalForKF=nLoopKF; + + } + lpKFtoCheck.push_back(pChild); + } + + pKF->mTcwBefGBA = pKF->GetPose(); + pKF->SetPose(pKF->mTcwGBA); + lpKFtoCheck.pop_front(); + } + + // Correct MapPoints + const vector vpMPs = mpMap->GetAllMapPoints(); + + for(size_t i=0; iisBad()) + continue; + + if(pMP->mnBAGlobalForKF==nLoopKF) + { + // If optimized by Global BA, just update + pMP->SetWorldPos(pMP->mPosGBA); + } + else + { + // Update according to the correction of its reference keyframe + KeyFrame* pRefKF = pMP->GetReferenceKeyFrame(); + + if(pRefKF->mnBAGlobalForKF!=nLoopKF) + continue; + + // Map to non-corrected camera + cv::Mat Rcw = pRefKF->mTcwBefGBA.rowRange(0,3).colRange(0,3); + cv::Mat tcw = pRefKF->mTcwBefGBA.rowRange(0,3).col(3); + cv::Mat Xc = Rcw*pMP->GetWorldPos()+tcw; + + // Backproject using corrected camera + cv::Mat Twc = pRefKF->GetPoseInverse(); + cv::Mat Rwc = Twc.rowRange(0,3).colRange(0,3); + cv::Mat twc = Twc.rowRange(0,3).col(3); + + pMP->SetWorldPos(Rwc*Xc+twc); + } + } + + mpMap->InformNewBigChange(); + + mpLocalMapper->Release(); + + cout << "Map updated!" << endl; + } + + mbFinishedGBA = true; + mbRunningGBA = false; + } +} + +void LoopClosing::RequestFinish() +{ + unique_lock lock(mMutexFinish); + mbFinishRequested = true; +} + +bool LoopClosing::CheckFinish() +{ + unique_lock lock(mMutexFinish); + return mbFinishRequested; +} + +void LoopClosing::SetFinish() +{ + unique_lock lock(mMutexFinish); + mbFinished = true; +} + +bool LoopClosing::isFinished() +{ + unique_lock lock(mMutexFinish); + return mbFinished; +} + + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/Map.cc b/for_orb_slam2/src/Map.cc new file mode 100644 index 00000000000..15fcd869148 --- /dev/null +++ b/for_orb_slam2/src/Map.cc @@ -0,0 +1,133 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#include "Map.h" + +#include + +namespace ORB_SLAM2 +{ + +Map::Map():mnMaxKFid(0),mnBigChangeIdx(0) +{ +} + +void Map::AddKeyFrame(KeyFrame *pKF) +{ + unique_lock lock(mMutexMap); + mspKeyFrames.insert(pKF); + if(pKF->mnId>mnMaxKFid) + mnMaxKFid=pKF->mnId; +} + +void Map::AddMapPoint(MapPoint *pMP) +{ + unique_lock lock(mMutexMap); + mspMapPoints.insert(pMP); +} + +void Map::EraseMapPoint(MapPoint *pMP) +{ + unique_lock lock(mMutexMap); + mspMapPoints.erase(pMP); + + // TODO: This only erase the pointer. + // Delete the MapPoint +} + +void Map::EraseKeyFrame(KeyFrame *pKF) +{ + unique_lock lock(mMutexMap); + mspKeyFrames.erase(pKF); + + // TODO: This only erase the pointer. + // Delete the MapPoint +} + +void Map::SetReferenceMapPoints(const vector &vpMPs) +{ + unique_lock lock(mMutexMap); + mvpReferenceMapPoints = vpMPs; +} + +void Map::InformNewBigChange() +{ + unique_lock lock(mMutexMap); + mnBigChangeIdx++; +} + +int Map::GetLastBigChangeIdx() +{ + unique_lock lock(mMutexMap); + return mnBigChangeIdx; +} + +vector Map::GetAllKeyFrames() +{ + unique_lock lock(mMutexMap); + return vector(mspKeyFrames.begin(),mspKeyFrames.end()); +} + +vector Map::GetAllMapPoints() +{ + unique_lock lock(mMutexMap); + return vector(mspMapPoints.begin(),mspMapPoints.end()); +} + +long unsigned int Map::MapPointsInMap() +{ + unique_lock lock(mMutexMap); + return mspMapPoints.size(); +} + +long unsigned int Map::KeyFramesInMap() +{ + unique_lock lock(mMutexMap); + return mspKeyFrames.size(); +} + +vector Map::GetReferenceMapPoints() +{ + unique_lock lock(mMutexMap); + return mvpReferenceMapPoints; +} + +long unsigned int Map::GetMaxKFid() +{ + unique_lock lock(mMutexMap); + return mnMaxKFid; +} + +void Map::clear() +{ + for(set::iterator sit=mspMapPoints.begin(), send=mspMapPoints.end(); sit!=send; sit++) + delete *sit; + + for(set::iterator sit=mspKeyFrames.begin(), send=mspKeyFrames.end(); sit!=send; sit++) + delete *sit; + + mspMapPoints.clear(); + mspKeyFrames.clear(); + mnMaxKFid = 0; + mvpReferenceMapPoints.clear(); + mvpKeyFrameOrigins.clear(); +} + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/MapDrawer.cc b/for_orb_slam2/src/MapDrawer.cc new file mode 100644 index 00000000000..4d9990bc8a2 --- /dev/null +++ b/for_orb_slam2/src/MapDrawer.cc @@ -0,0 +1,264 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#include "MapDrawer.h" +#include "MapPoint.h" +#include "KeyFrame.h" +#include +#include + +namespace ORB_SLAM2 +{ + + +MapDrawer::MapDrawer(Map* pMap, const string &strSettingPath):mpMap(pMap) +{ + cv::FileStorage fSettings(strSettingPath, cv::FileStorage::READ); + + mKeyFrameSize = fSettings["Viewer.KeyFrameSize"]; + mKeyFrameLineWidth = fSettings["Viewer.KeyFrameLineWidth"]; + mGraphLineWidth = fSettings["Viewer.GraphLineWidth"]; + mPointSize = fSettings["Viewer.PointSize"]; + mCameraSize = fSettings["Viewer.CameraSize"]; + mCameraLineWidth = fSettings["Viewer.CameraLineWidth"]; + +} + +void MapDrawer::DrawMapPoints() +{ + const vector &vpMPs = mpMap->GetAllMapPoints(); + const vector &vpRefMPs = mpMap->GetReferenceMapPoints(); + + set spRefMPs(vpRefMPs.begin(), vpRefMPs.end()); + + if(vpMPs.empty()) + return; + + glPointSize(mPointSize); + glBegin(GL_POINTS); + glColor3f(0.0,0.0,0.0); + + for(size_t i=0, iend=vpMPs.size(); iisBad() || spRefMPs.count(vpMPs[i])) + continue; + cv::Mat pos = vpMPs[i]->GetWorldPos(); + glVertex3f(pos.at(0),pos.at(1),pos.at(2)); + } + glEnd(); + + glPointSize(mPointSize); + glBegin(GL_POINTS); + glColor3f(1.0,0.0,0.0); + + for(set::iterator sit=spRefMPs.begin(), send=spRefMPs.end(); sit!=send; sit++) + { + if((*sit)->isBad()) + continue; + cv::Mat pos = (*sit)->GetWorldPos(); + glVertex3f(pos.at(0),pos.at(1),pos.at(2)); + + } + + glEnd(); +} + +void MapDrawer::DrawKeyFrames(const bool bDrawKF, const bool bDrawGraph) +{ + const float &w = mKeyFrameSize; + const float h = w*0.75; + const float z = w*0.6; + + const vector vpKFs = mpMap->GetAllKeyFrames(); + + if(bDrawKF) + { + for(size_t i=0; iGetPoseInverse().t(); + + glPushMatrix(); + + glMultMatrixf(Twc.ptr(0)); + + glLineWidth(mKeyFrameLineWidth); + glColor3f(0.0f,0.0f,1.0f); + glBegin(GL_LINES); + glVertex3f(0,0,0); + glVertex3f(w,h,z); + glVertex3f(0,0,0); + glVertex3f(w,-h,z); + glVertex3f(0,0,0); + glVertex3f(-w,-h,z); + glVertex3f(0,0,0); + glVertex3f(-w,h,z); + + glVertex3f(w,h,z); + glVertex3f(w,-h,z); + + glVertex3f(-w,h,z); + glVertex3f(-w,-h,z); + + glVertex3f(-w,h,z); + glVertex3f(w,h,z); + + glVertex3f(-w,-h,z); + glVertex3f(w,-h,z); + glEnd(); + + glPopMatrix(); + } + } + + if(bDrawGraph) + { + glLineWidth(mGraphLineWidth); + glColor4f(0.0f,1.0f,0.0f,0.6f); + glBegin(GL_LINES); + + for(size_t i=0; i vCovKFs = vpKFs[i]->GetCovisiblesByWeight(100); + cv::Mat Ow = vpKFs[i]->GetCameraCenter(); + if(!vCovKFs.empty()) + { + for(vector::const_iterator vit=vCovKFs.begin(), vend=vCovKFs.end(); vit!=vend; vit++) + { + if((*vit)->mnIdmnId) + continue; + cv::Mat Ow2 = (*vit)->GetCameraCenter(); + glVertex3f(Ow.at(0),Ow.at(1),Ow.at(2)); + glVertex3f(Ow2.at(0),Ow2.at(1),Ow2.at(2)); + } + } + + // Spanning tree + KeyFrame* pParent = vpKFs[i]->GetParent(); + if(pParent) + { + cv::Mat Owp = pParent->GetCameraCenter(); + glVertex3f(Ow.at(0),Ow.at(1),Ow.at(2)); + glVertex3f(Owp.at(0),Owp.at(1),Owp.at(2)); + } + + // Loops + set sLoopKFs = vpKFs[i]->GetLoopEdges(); + for(set::iterator sit=sLoopKFs.begin(), send=sLoopKFs.end(); sit!=send; sit++) + { + if((*sit)->mnIdmnId) + continue; + cv::Mat Owl = (*sit)->GetCameraCenter(); + glVertex3f(Ow.at(0),Ow.at(1),Ow.at(2)); + glVertex3f(Owl.at(0),Owl.at(1),Owl.at(2)); + } + } + + glEnd(); + } +} + +void MapDrawer::DrawCurrentCamera(pangolin::OpenGlMatrix &Twc) +{ + const float &w = mCameraSize; + const float h = w*0.75; + const float z = w*0.6; + + glPushMatrix(); + +#ifdef HAVE_GLES + glMultMatrixf(Twc.m); +#else + glMultMatrixd(Twc.m); +#endif + + glLineWidth(mCameraLineWidth); + glColor3f(0.0f,1.0f,0.0f); + glBegin(GL_LINES); + glVertex3f(0,0,0); + glVertex3f(w,h,z); + glVertex3f(0,0,0); + glVertex3f(w,-h,z); + glVertex3f(0,0,0); + glVertex3f(-w,-h,z); + glVertex3f(0,0,0); + glVertex3f(-w,h,z); + + glVertex3f(w,h,z); + glVertex3f(w,-h,z); + + glVertex3f(-w,h,z); + glVertex3f(-w,-h,z); + + glVertex3f(-w,h,z); + glVertex3f(w,h,z); + + glVertex3f(-w,-h,z); + glVertex3f(w,-h,z); + glEnd(); + + glPopMatrix(); +} + + +void MapDrawer::SetCurrentCameraPose(const cv::Mat &Tcw) +{ + unique_lock lock(mMutexCamera); + mCameraPose = Tcw.clone(); +} + +void MapDrawer::GetCurrentOpenGLCameraMatrix(pangolin::OpenGlMatrix &M) +{ + if(!mCameraPose.empty()) + { + cv::Mat Rwc(3,3,CV_32F); + cv::Mat twc(3,1,CV_32F); + { + unique_lock lock(mMutexCamera); + Rwc = mCameraPose.rowRange(0,3).colRange(0,3).t(); + twc = -Rwc*mCameraPose.rowRange(0,3).col(3); + } + + M.m[0] = Rwc.at(0,0); + M.m[1] = Rwc.at(1,0); + M.m[2] = Rwc.at(2,0); + M.m[3] = 0.0; + + M.m[4] = Rwc.at(0,1); + M.m[5] = Rwc.at(1,1); + M.m[6] = Rwc.at(2,1); + M.m[7] = 0.0; + + M.m[8] = Rwc.at(0,2); + M.m[9] = Rwc.at(1,2); + M.m[10] = Rwc.at(2,2); + M.m[11] = 0.0; + + M.m[12] = twc.at(0); + M.m[13] = twc.at(1); + M.m[14] = twc.at(2); + M.m[15] = 1.0; + } + else + M.SetIdentity(); +} + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/MapPoint.cc b/for_orb_slam2/src/MapPoint.cc new file mode 100644 index 00000000000..3b2921197b1 --- /dev/null +++ b/for_orb_slam2/src/MapPoint.cc @@ -0,0 +1,421 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#include "MapPoint.h" +#include "ORBmatcher.h" + +#include + +namespace ORB_SLAM2 +{ + +long unsigned int MapPoint::nNextId=0; +mutex MapPoint::mGlobalMutex; + +MapPoint::MapPoint(const cv::Mat &Pos, KeyFrame *pRefKF, Map* pMap): + mnFirstKFid(pRefKF->mnId), mnFirstFrame(pRefKF->mnFrameId), nObs(0), mnTrackReferenceForFrame(0), + mnLastFrameSeen(0), mnBALocalForKF(0), mnFuseCandidateForKF(0), mnLoopPointForKF(0), mnCorrectedByKF(0), + mnCorrectedReference(0), mnBAGlobalForKF(0), mpRefKF(pRefKF), mnVisible(1), mnFound(1), mbBad(false), + mpReplaced(static_cast(NULL)), mfMinDistance(0), mfMaxDistance(0), mpMap(pMap) +{ + Pos.copyTo(mWorldPos); + mNormalVector = cv::Mat::zeros(3,1,CV_32F); + + // MapPoints can be created from Tracking and Local Mapping. This mutex avoid conflicts with id. + unique_lock lock(mpMap->mMutexPointCreation); + mnId=nNextId++; +} + +MapPoint::MapPoint(const cv::Mat &Pos, Map* pMap, Frame* pFrame, const int &idxF): + mnFirstKFid(-1), mnFirstFrame(pFrame->mnId), nObs(0), mnTrackReferenceForFrame(0), mnLastFrameSeen(0), + mnBALocalForKF(0), mnFuseCandidateForKF(0),mnLoopPointForKF(0), mnCorrectedByKF(0), + mnCorrectedReference(0), mnBAGlobalForKF(0), mpRefKF(static_cast(NULL)), mnVisible(1), + mnFound(1), mbBad(false), mpReplaced(NULL), mpMap(pMap) +{ + Pos.copyTo(mWorldPos); + cv::Mat Ow = pFrame->GetCameraCenter(); + mNormalVector = mWorldPos - Ow; + mNormalVector = mNormalVector/cv::norm(mNormalVector); + + cv::Mat PC = Pos - Ow; + const float dist = cv::norm(PC); + const int level = pFrame->mvKeysUn[idxF].octave; + const float levelScaleFactor = pFrame->mvScaleFactors[level]; + const int nLevels = pFrame->mnScaleLevels; + + mfMaxDistance = dist*levelScaleFactor; + mfMinDistance = mfMaxDistance/pFrame->mvScaleFactors[nLevels-1]; + + pFrame->mDescriptors.row(idxF).copyTo(mDescriptor); + + // MapPoints can be created from Tracking and Local Mapping. This mutex avoid conflicts with id. + unique_lock lock(mpMap->mMutexPointCreation); + mnId=nNextId++; +} + +void MapPoint::SetWorldPos(const cv::Mat &Pos) +{ + unique_lock lock2(mGlobalMutex); + unique_lock lock(mMutexPos); + Pos.copyTo(mWorldPos); +} + +cv::Mat MapPoint::GetWorldPos() +{ + unique_lock lock(mMutexPos); + return mWorldPos.clone(); +} + +cv::Mat MapPoint::GetNormal() +{ + unique_lock lock(mMutexPos); + return mNormalVector.clone(); +} + +KeyFrame* MapPoint::GetReferenceKeyFrame() +{ + unique_lock lock(mMutexFeatures); + return mpRefKF; +} + +void MapPoint::AddObservation(KeyFrame* pKF, size_t idx) +{ + unique_lock lock(mMutexFeatures); + if(mObservations.count(pKF)) + return; + mObservations[pKF]=idx; + + if(pKF->mvuRight[idx]>=0) + nObs+=2; + else + nObs++; +} + +void MapPoint::EraseObservation(KeyFrame* pKF) +{ + bool bBad=false; + { + unique_lock lock(mMutexFeatures); + if(mObservations.count(pKF)) + { + int idx = mObservations[pKF]; + if(pKF->mvuRight[idx]>=0) + nObs-=2; + else + nObs--; + + mObservations.erase(pKF); + + if(mpRefKF==pKF) + mpRefKF=mObservations.begin()->first; + + // If only 2 observations or less, discard point + if(nObs<=2) + bBad=true; + } + } + + if(bBad) + SetBadFlag(); +} + +map MapPoint::GetObservations() +{ + unique_lock lock(mMutexFeatures); + return mObservations; +} + +int MapPoint::Observations() +{ + unique_lock lock(mMutexFeatures); + return nObs; +} + +void MapPoint::SetBadFlag() +{ + map obs; + { + unique_lock lock1(mMutexFeatures); + unique_lock lock2(mMutexPos); + mbBad=true; + obs = mObservations; + mObservations.clear(); + } + for(map::iterator mit=obs.begin(), mend=obs.end(); mit!=mend; mit++) + { + KeyFrame* pKF = mit->first; + pKF->EraseMapPointMatch(mit->second); + } + + mpMap->EraseMapPoint(this); +} + +MapPoint* MapPoint::GetReplaced() +{ + unique_lock lock1(mMutexFeatures); + unique_lock lock2(mMutexPos); + return mpReplaced; +} + +void MapPoint::Replace(MapPoint* pMP) +{ + if(pMP->mnId==this->mnId) + return; + + int nvisible, nfound; + map obs; + { + unique_lock lock1(mMutexFeatures); + unique_lock lock2(mMutexPos); + obs=mObservations; + mObservations.clear(); + mbBad=true; + nvisible = mnVisible; + nfound = mnFound; + mpReplaced = pMP; + } + + for(map::iterator mit=obs.begin(), mend=obs.end(); mit!=mend; mit++) + { + // Replace measurement in keyframe + KeyFrame* pKF = mit->first; + + if(!pMP->IsInKeyFrame(pKF)) + { + pKF->ReplaceMapPointMatch(mit->second, pMP); + pMP->AddObservation(pKF,mit->second); + } + else + { + pKF->EraseMapPointMatch(mit->second); + } + } + pMP->IncreaseFound(nfound); + pMP->IncreaseVisible(nvisible); + pMP->ComputeDistinctiveDescriptors(); + + mpMap->EraseMapPoint(this); +} + +bool MapPoint::isBad() +{ + unique_lock lock(mMutexFeatures); + unique_lock lock2(mMutexPos); + return mbBad; +} + +void MapPoint::IncreaseVisible(int n) +{ + unique_lock lock(mMutexFeatures); + mnVisible+=n; +} + +void MapPoint::IncreaseFound(int n) +{ + unique_lock lock(mMutexFeatures); + mnFound+=n; +} + +float MapPoint::GetFoundRatio() +{ + unique_lock lock(mMutexFeatures); + return static_cast(mnFound)/mnVisible; +} + +void MapPoint::ComputeDistinctiveDescriptors() +{ + // Retrieve all observed descriptors + vector vDescriptors; + + map observations; + + { + unique_lock lock1(mMutexFeatures); + if(mbBad) + return; + observations=mObservations; + } + + if(observations.empty()) + return; + + vDescriptors.reserve(observations.size()); + + for(map::iterator mit=observations.begin(), mend=observations.end(); mit!=mend; mit++) + { + KeyFrame* pKF = mit->first; + + if(!pKF->isBad()) + vDescriptors.push_back(pKF->mDescriptors.row(mit->second)); + } + + if(vDescriptors.empty()) + return; + + // Compute distances between them + const size_t N = vDescriptors.size(); + + float Distances[N][N]; + for(size_t i=0;i vDists(Distances[i],Distances[i]+N); + sort(vDists.begin(),vDists.end()); + int median = vDists[0.5*(N-1)]; + + if(median lock(mMutexFeatures); + mDescriptor = vDescriptors[BestIdx].clone(); + } +} + +cv::Mat MapPoint::GetDescriptor() +{ + unique_lock lock(mMutexFeatures); + return mDescriptor.clone(); +} + +int MapPoint::GetIndexInKeyFrame(KeyFrame *pKF) +{ + unique_lock lock(mMutexFeatures); + if(mObservations.count(pKF)) + return mObservations[pKF]; + else + return -1; +} + +bool MapPoint::IsInKeyFrame(KeyFrame *pKF) +{ + unique_lock lock(mMutexFeatures); + return (mObservations.count(pKF)); +} + +void MapPoint::UpdateNormalAndDepth() +{ + map observations; + KeyFrame* pRefKF; + cv::Mat Pos; + { + unique_lock lock1(mMutexFeatures); + unique_lock lock2(mMutexPos); + if(mbBad) + return; + observations=mObservations; + pRefKF=mpRefKF; + Pos = mWorldPos.clone(); + } + + if(observations.empty()) + return; + + cv::Mat normal = cv::Mat::zeros(3,1,CV_32F); + int n=0; + for(map::iterator mit=observations.begin(), mend=observations.end(); mit!=mend; mit++) + { + KeyFrame* pKF = mit->first; + cv::Mat Owi = pKF->GetCameraCenter(); + cv::Mat normali = mWorldPos - Owi; + normal = normal + normali/cv::norm(normali); + n++; + } + + cv::Mat PC = Pos - pRefKF->GetCameraCenter(); + const float dist = cv::norm(PC); + const int level = pRefKF->mvKeysUn[observations[pRefKF]].octave; + const float levelScaleFactor = pRefKF->mvScaleFactors[level]; + const int nLevels = pRefKF->mnScaleLevels; + + { + unique_lock lock3(mMutexPos); + mfMaxDistance = dist*levelScaleFactor; + mfMinDistance = mfMaxDistance/pRefKF->mvScaleFactors[nLevels-1]; + mNormalVector = normal/n; + } +} + +float MapPoint::GetMinDistanceInvariance() +{ + unique_lock lock(mMutexPos); + return 0.8f*mfMinDistance; +} + +float MapPoint::GetMaxDistanceInvariance() +{ + unique_lock lock(mMutexPos); + return 1.2f*mfMaxDistance; +} + +int MapPoint::PredictScale(const float ¤tDist, KeyFrame* pKF) +{ + float ratio; + { + unique_lock lock(mMutexPos); + ratio = mfMaxDistance/currentDist; + } + + int nScale = ceil(log(ratio)/pKF->mfLogScaleFactor); + if(nScale<0) + nScale = 0; + else if(nScale>=pKF->mnScaleLevels) + nScale = pKF->mnScaleLevels-1; + + return nScale; +} + +int MapPoint::PredictScale(const float ¤tDist, Frame* pF) +{ + float ratio; + { + unique_lock lock(mMutexPos); + ratio = mfMaxDistance/currentDist; + } + + int nScale = ceil(log(ratio)/pF->mfLogScaleFactor); + if(nScale<0) + nScale = 0; + else if(nScale>=pF->mnScaleLevels) + nScale = pF->mnScaleLevels-1; + + return nScale; +} + + + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/ORBextractor.cc b/for_orb_slam2/src/ORBextractor.cc new file mode 100644 index 00000000000..14be2efae5a --- /dev/null +++ b/for_orb_slam2/src/ORBextractor.cc @@ -0,0 +1,1134 @@ +/** +* This file is part of ORB-SLAM2. +* This file is based on the file orb.cpp from the OpenCV library (see BSD license below). +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ +/** +* Software License Agreement (BSD License) +* +* Copyright (c) 2009, Willow Garage, Inc. +* All rights reserved. +* +* Redistribution and use in source and binary forms, with or without +* modification, are permitted provided that the following conditions +* are met: +* +* * Redistributions of source code must retain the above copyright +* notice, this list of conditions and the following disclaimer. +* * Redistributions in binary form must reproduce the above +* copyright notice, this list of conditions and the following +* disclaimer in the documentation and/or other materials provided +* with the distribution. +* * Neither the name of the Willow Garage nor the names of its +* contributors may be used to endorse or promote products derived +* from this software without specific prior written permission. +* +* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS +* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE +* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, +* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT +* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN +* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +* POSSIBILITY OF SUCH DAMAGE. +* +*/ + + +#include +#include +#include +#include +#include + +#include "ORBextractor.h" + + +using namespace cv; +using namespace std; + +namespace ORB_SLAM2 +{ + +const int PATCH_SIZE = 31; +const int HALF_PATCH_SIZE = 15; +const int EDGE_THRESHOLD = 19; + + +static float IC_Angle(const Mat& image, Point2f pt, const vector & u_max) +{ + int m_01 = 0, m_10 = 0; + + const uchar* center = &image.at (cvRound(pt.y), cvRound(pt.x)); + + // Treat the center line differently, v=0 + for (int u = -HALF_PATCH_SIZE; u <= HALF_PATCH_SIZE; ++u) + m_10 += u * center[u]; + + // Go line by line in the circuI853lar patch + int step = (int)image.step1(); + for (int v = 1; v <= HALF_PATCH_SIZE; ++v) + { + // Proceed over the two lines + int v_sum = 0; + int d = u_max[v]; + for (int u = -d; u <= d; ++u) + { + int val_plus = center[u + v*step], val_minus = center[u - v*step]; + v_sum += (val_plus - val_minus); + m_10 += u * (val_plus + val_minus); + } + m_01 += v * v_sum; + } + + return fastAtan2((float)m_01, (float)m_10); +} + + +const float factorPI = (float)(CV_PI/180.f); +static void computeOrbDescriptor(const KeyPoint& kpt, + const Mat& img, const Point* pattern, + uchar* desc) +{ + float angle = (float)kpt.angle*factorPI; + float a = (float)cos(angle), b = (float)sin(angle); + + const uchar* center = &img.at(cvRound(kpt.pt.y), cvRound(kpt.pt.x)); + const int step = (int)img.step; + + #define GET_VALUE(idx) \ + center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \ + cvRound(pattern[idx].x*a - pattern[idx].y*b)] + + + for (int i = 0; i < 32; ++i, pattern += 16) + { + int t0, t1, val; + t0 = GET_VALUE(0); t1 = GET_VALUE(1); + val = t0 < t1; + t0 = GET_VALUE(2); t1 = GET_VALUE(3); + val |= (t0 < t1) << 1; + t0 = GET_VALUE(4); t1 = GET_VALUE(5); + val |= (t0 < t1) << 2; + t0 = GET_VALUE(6); t1 = GET_VALUE(7); + val |= (t0 < t1) << 3; + t0 = GET_VALUE(8); t1 = GET_VALUE(9); + val |= (t0 < t1) << 4; + t0 = GET_VALUE(10); t1 = GET_VALUE(11); + val |= (t0 < t1) << 5; + t0 = GET_VALUE(12); t1 = GET_VALUE(13); + val |= (t0 < t1) << 6; + t0 = GET_VALUE(14); t1 = GET_VALUE(15); + val |= (t0 < t1) << 7; + + desc[i] = (uchar)val; + } + + #undef GET_VALUE +} + + +static int bit_pattern_31_[256*4] = +{ + 8,-3, 9,5/*mean (0), correlation (0)*/, + 4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/, + -11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/, + 7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/, + 2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/, + 1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/, + -2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/, + -13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/, + -13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/, + 10,4, 11,9/*mean (0.122065), correlation (0.093285)*/, + -13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/, + -11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/, + 7,7, 12,6/*mean (0.160583), correlation (0.130064)*/, + -4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/, + -13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/, + -9,0, -7,5/*mean (0.198234), correlation (0.143636)*/, + 12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/, + -3,6, -2,12/*mean (0.166847), correlation (0.171682)*/, + -6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/, + 11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/, + 4,7, 5,1/*mean (0.205106), correlation (0.186848)*/, + 5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/, + 3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/, + -8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/, + -2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/, + -13,12, -8,10/*mean (0.14783), correlation (0.206356)*/, + -7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/, + -4,2, -3,7/*mean (0.188237), correlation (0.21384)*/, + -10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/, + 5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/, + 5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/, + 1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/, + 9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/, + 4,7, 4,12/*mean (0.131005), correlation (0.257622)*/, + 2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/, + -4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/, + -8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/, + 4,11, 9,12/*mean (0.226226), correlation (0.258255)*/, + 0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/, + -13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/, + -3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/, + -6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/, + 8,12, 10,7/*mean (0.225337), correlation (0.282851)*/, + 0,9, 1,3/*mean (0.226687), correlation (0.278734)*/, + 7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/, + -13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/, + 10,7, 12,1/*mean (0.125517), correlation (0.31089)*/, + -6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/, + 10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/, + -13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/, + -13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/, + 3,3, 7,8/*mean (0.177755), correlation (0.309394)*/, + 5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/, + -1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/, + 3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/, + 2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/, + -13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/, + -13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/, + -13,3, -11,8/*mean (0.134222), correlation (0.322922)*/, + -7,12, -4,7/*mean (0.153284), correlation (0.337061)*/, + 6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/, + -9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/, + -2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/, + -12,5, -7,5/*mean (0.207805), correlation (0.335631)*/, + 3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/, + -7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/, + -3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/, + 2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/, + -11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/, + -1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/, + 5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/, + -4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/, + -9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/, + -12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/, + 10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/, + 7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/, + -7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/, + -4,9, -3,4/*mean (0.099865), correlation (0.372276)*/, + 7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/, + -7,6, -5,1/*mean (0.126125), correlation (0.369606)*/, + -13,11, -12,5/*mean (0.130364), correlation (0.358502)*/, + -3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/, + 7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/, + -13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/, + 1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/, + 2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/, + -4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/, + -1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/, + 7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/, + 1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/, + 9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/, + -1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/, + -13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/, + 7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/, + 12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/, + 6,3, 7,11/*mean (0.1074), correlation (0.413224)*/, + 5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/, + 2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/, + 3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/, + 2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/, + 9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/, + -8,4, -7,9/*mean (0.183682), correlation (0.402956)*/, + -11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/, + 1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/, + 6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/, + 2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/, + 6,3, 11,0/*mean (0.204588), correlation (0.411762)*/, + 3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/, + 7,8, 9,3/*mean (0.213237), correlation (0.409306)*/, + -11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/, + -10,11, -5,10/*mean (0.247672), correlation (0.413392)*/, + -5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/, + -10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/, + 8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/, + 4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/, + -10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/, + 4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/, + -2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/, + -5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/, + 7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/, + -9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/, + -5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/, + 8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/, + -9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/, + 1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/, + 7,-4, 9,1/*mean (0.132692), correlation (0.454)*/, + -2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/, + 11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/, + -12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/, + 3,7, 7,12/*mean (0.147627), correlation (0.456643)*/, + 5,5, 10,8/*mean (0.152901), correlation (0.455036)*/, + 0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/, + -9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/, + 0,7, 2,12/*mean (0.18312), correlation (0.433855)*/, + -1,2, 1,7/*mean (0.185504), correlation (0.443838)*/, + 5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/, + 3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/, + -13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/, + -5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/, + -4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/, + 6,5, 8,0/*mean (0.1972), correlation (0.450481)*/, + -7,6, -6,12/*mean (0.199438), correlation (0.458156)*/, + -13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/, + 1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/, + 4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/, + -2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/, + 2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/, + -2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/, + 4,1, 9,3/*mean (0.23962), correlation (0.444824)*/, + -6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/, + -3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/, + 7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/, + 4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/, + -13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/, + 7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/, + 7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/, + -7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/, + -8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/, + -13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/, + 2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/, + 10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/, + -6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/, + 8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/, + 2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/, + -11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/, + -12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/, + -11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/, + 5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/, + -2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/, + -1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/, + -13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/, + -10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/, + -3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/, + 2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/, + -9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/, + -4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/, + -4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/, + -6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/, + 6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/, + -13,11, -5,5/*mean (0.162427), correlation (0.501907)*/, + 11,11, 12,6/*mean (0.16652), correlation (0.497632)*/, + 7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/, + -1,12, 0,7/*mean (0.169456), correlation (0.495339)*/, + -4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/, + -7,1, -6,7/*mean (0.175), correlation (0.500024)*/, + -13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/, + -7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/, + -8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/, + -5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/, + -13,7, -8,10/*mean (0.196739), correlation (0.496503)*/, + 1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/, + 1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/, + 9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/, + 5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/, + -1,11, 1,-13/*mean (0.212), correlation (0.499414)*/, + -9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/, + -1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/, + -13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/, + 8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/, + 2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/, + 7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/, + -10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/, + -10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/, + 4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/, + 3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/, + -4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/, + 5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/, + 4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/, + -9,9, -4,3/*mean (0.236977), correlation (0.497739)*/, + 0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/, + -12,1, -6,1/*mean (0.243297), correlation (0.489447)*/, + 3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/, + -10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/, + 8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/, + -8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/, + 2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/, + 10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/, + 6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/, + -7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/, + -3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/, + -1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/, + -3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/, + -8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/, + 4,2, 12,12/*mean (0.01778), correlation (0.546921)*/, + 2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/, + 6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/, + 3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/, + 11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/, + -3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/, + 4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/, + 2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/, + -10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/, + -13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/, + -13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/, + 6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/, + 0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/, + -13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/, + -9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/, + -13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/, + 5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/, + 2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/, + -1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/, + 9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/, + 11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/, + 3,0, 3,5/*mean (0.101147), correlation (0.525576)*/, + -1,4, 0,10/*mean (0.105263), correlation (0.531498)*/, + 3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/, + -13,0, -10,5/*mean (0.112798), correlation (0.536582)*/, + 5,8, 12,11/*mean (0.114181), correlation (0.555793)*/, + 8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/, + 7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/, + -10,4, -10,9/*mean (0.12094), correlation (0.554785)*/, + 7,3, 12,4/*mean (0.122582), correlation (0.555825)*/, + 9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/, + 7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/, + -1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/ +}; + +ORBextractor::ORBextractor(int _nfeatures, float _scaleFactor, int _nlevels, + int _iniThFAST, int _minThFAST): + nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels), + iniThFAST(_iniThFAST), minThFAST(_minThFAST) +{ + mvScaleFactor.resize(nlevels); + mvLevelSigma2.resize(nlevels); + mvScaleFactor[0]=1.0f; + mvLevelSigma2[0]=1.0f; + for(int i=1; i= vmin; --v) + { + while (umax[v0] == umax[v0 + 1]) + ++v0; + umax[v] = v0; + ++v0; + } +} + +static void computeOrientation(const Mat& image, vector& keypoints, const vector& umax) +{ + for (vector::iterator keypoint = keypoints.begin(), + keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint) + { + keypoint->angle = IC_Angle(image, keypoint->pt, umax); + } +} + +void ExtractorNode::DivideNode(ExtractorNode &n1, ExtractorNode &n2, ExtractorNode &n3, ExtractorNode &n4) +{ + const int halfX = ceil(static_cast(UR.x-UL.x)/2); + const int halfY = ceil(static_cast(BR.y-UL.y)/2); + + //Define boundaries of childs + n1.UL = UL; + n1.UR = cv::Point2i(UL.x+halfX,UL.y); + n1.BL = cv::Point2i(UL.x,UL.y+halfY); + n1.BR = cv::Point2i(UL.x+halfX,UL.y+halfY); + n1.vKeys.reserve(vKeys.size()); + + n2.UL = n1.UR; + n2.UR = UR; + n2.BL = n1.BR; + n2.BR = cv::Point2i(UR.x,UL.y+halfY); + n2.vKeys.reserve(vKeys.size()); + + n3.UL = n1.BL; + n3.UR = n1.BR; + n3.BL = BL; + n3.BR = cv::Point2i(n1.BR.x,BL.y); + n3.vKeys.reserve(vKeys.size()); + + n4.UL = n3.UR; + n4.UR = n2.BR; + n4.BL = n3.BR; + n4.BR = BR; + n4.vKeys.reserve(vKeys.size()); + + //Associate points to childs + for(size_t i=0;i ORBextractor::DistributeOctTree(const vector& vToDistributeKeys, const int &minX, + const int &maxX, const int &minY, const int &maxY, const int &N, const int &level) +{ + // Compute how many initial nodes + const int nIni = round(static_cast(maxX-minX)/(maxY-minY)); + + const float hX = static_cast(maxX-minX)/nIni; + + list lNodes; + + vector vpIniNodes; + vpIniNodes.resize(nIni); + + for(int i=0; i(i),0); + ni.UR = cv::Point2i(hX*static_cast(i+1),0); + ni.BL = cv::Point2i(ni.UL.x,maxY-minY); + ni.BR = cv::Point2i(ni.UR.x,maxY-minY); + ni.vKeys.reserve(vToDistributeKeys.size()); + + lNodes.push_back(ni); + vpIniNodes[i] = &lNodes.back(); + } + + //Associate points to childs + for(size_t i=0;ivKeys.push_back(kp); + } + + list::iterator lit = lNodes.begin(); + + while(lit!=lNodes.end()) + { + if(lit->vKeys.size()==1) + { + lit->bNoMore=true; + lit++; + } + else if(lit->vKeys.empty()) + lit = lNodes.erase(lit); + else + lit++; + } + + bool bFinish = false; + + int iteration = 0; + + vector > vSizeAndPointerToNode; + vSizeAndPointerToNode.reserve(lNodes.size()*4); + + while(!bFinish) + { + iteration++; + + int prevSize = lNodes.size(); + + lit = lNodes.begin(); + + int nToExpand = 0; + + vSizeAndPointerToNode.clear(); + + while(lit!=lNodes.end()) + { + if(lit->bNoMore) + { + // If node only contains one point do not subdivide and continue + lit++; + continue; + } + else + { + // If more than one point, subdivide + ExtractorNode n1,n2,n3,n4; + lit->DivideNode(n1,n2,n3,n4); + + // Add childs if they contain points + if(n1.vKeys.size()>0) + { + lNodes.push_front(n1); + if(n1.vKeys.size()>1) + { + nToExpand++; + vSizeAndPointerToNode.push_back(make_pair(n1.vKeys.size(),&lNodes.front())); + lNodes.front().lit = lNodes.begin(); + } + } + if(n2.vKeys.size()>0) + { + lNodes.push_front(n2); + if(n2.vKeys.size()>1) + { + nToExpand++; + vSizeAndPointerToNode.push_back(make_pair(n2.vKeys.size(),&lNodes.front())); + lNodes.front().lit = lNodes.begin(); + } + } + if(n3.vKeys.size()>0) + { + lNodes.push_front(n3); + if(n3.vKeys.size()>1) + { + nToExpand++; + vSizeAndPointerToNode.push_back(make_pair(n3.vKeys.size(),&lNodes.front())); + lNodes.front().lit = lNodes.begin(); + } + } + if(n4.vKeys.size()>0) + { + lNodes.push_front(n4); + if(n4.vKeys.size()>1) + { + nToExpand++; + vSizeAndPointerToNode.push_back(make_pair(n4.vKeys.size(),&lNodes.front())); + lNodes.front().lit = lNodes.begin(); + } + } + + lit=lNodes.erase(lit); + continue; + } + } + + // Finish if there are more nodes than required features + // or all nodes contain just one point + if((int)lNodes.size()>=N || (int)lNodes.size()==prevSize) + { + bFinish = true; + } + else if(((int)lNodes.size()+nToExpand*3)>N) + { + + while(!bFinish) + { + + prevSize = lNodes.size(); + + vector > vPrevSizeAndPointerToNode = vSizeAndPointerToNode; + vSizeAndPointerToNode.clear(); + + sort(vPrevSizeAndPointerToNode.begin(),vPrevSizeAndPointerToNode.end()); + for(int j=vPrevSizeAndPointerToNode.size()-1;j>=0;j--) + { + ExtractorNode n1,n2,n3,n4; + vPrevSizeAndPointerToNode[j].second->DivideNode(n1,n2,n3,n4); + + // Add childs if they contain points + if(n1.vKeys.size()>0) + { + lNodes.push_front(n1); + if(n1.vKeys.size()>1) + { + vSizeAndPointerToNode.push_back(make_pair(n1.vKeys.size(),&lNodes.front())); + lNodes.front().lit = lNodes.begin(); + } + } + if(n2.vKeys.size()>0) + { + lNodes.push_front(n2); + if(n2.vKeys.size()>1) + { + vSizeAndPointerToNode.push_back(make_pair(n2.vKeys.size(),&lNodes.front())); + lNodes.front().lit = lNodes.begin(); + } + } + if(n3.vKeys.size()>0) + { + lNodes.push_front(n3); + if(n3.vKeys.size()>1) + { + vSizeAndPointerToNode.push_back(make_pair(n3.vKeys.size(),&lNodes.front())); + lNodes.front().lit = lNodes.begin(); + } + } + if(n4.vKeys.size()>0) + { + lNodes.push_front(n4); + if(n4.vKeys.size()>1) + { + vSizeAndPointerToNode.push_back(make_pair(n4.vKeys.size(),&lNodes.front())); + lNodes.front().lit = lNodes.begin(); + } + } + + lNodes.erase(vPrevSizeAndPointerToNode[j].second->lit); + + if((int)lNodes.size()>=N) + break; + } + + if((int)lNodes.size()>=N || (int)lNodes.size()==prevSize) + bFinish = true; + + } + } + } + + // Retain the best point in each node + vector vResultKeys; + vResultKeys.reserve(nfeatures); + for(list::iterator lit=lNodes.begin(); lit!=lNodes.end(); lit++) + { + vector &vNodeKeys = lit->vKeys; + cv::KeyPoint* pKP = &vNodeKeys[0]; + float maxResponse = pKP->response; + + for(size_t k=1;kmaxResponse) + { + pKP = &vNodeKeys[k]; + maxResponse = vNodeKeys[k].response; + } + } + + vResultKeys.push_back(*pKP); + } + + return vResultKeys; +} + +void ORBextractor::ComputeKeyPointsOctTree(vector >& allKeypoints) +{ + allKeypoints.resize(nlevels); + + const float W = 30; + + for (int level = 0; level < nlevels; ++level) + { + const int minBorderX = EDGE_THRESHOLD-3; + const int minBorderY = minBorderX; + const int maxBorderX = mvImagePyramid[level].cols-EDGE_THRESHOLD+3; + const int maxBorderY = mvImagePyramid[level].rows-EDGE_THRESHOLD+3; + + vector vToDistributeKeys; + vToDistributeKeys.reserve(nfeatures*10); + + const float width = (maxBorderX-minBorderX); + const float height = (maxBorderY-minBorderY); + + const int nCols = width/W; + const int nRows = height/W; + const int wCell = ceil(width/nCols); + const int hCell = ceil(height/nRows); + + for(int i=0; i=maxBorderY-3) + continue; + if(maxY>maxBorderY) + maxY = maxBorderY; + + for(int j=0; j=maxBorderX-6) + continue; + if(maxX>maxBorderX) + maxX = maxBorderX; + + vector vKeysCell; + FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX), + vKeysCell,iniThFAST,true); + + if(vKeysCell.empty()) + { + FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX), + vKeysCell,minThFAST,true); + } + + if(!vKeysCell.empty()) + { + for(vector::iterator vit=vKeysCell.begin(); vit!=vKeysCell.end();vit++) + { + (*vit).pt.x+=j*wCell; + (*vit).pt.y+=i*hCell; + vToDistributeKeys.push_back(*vit); + } + } + + } + } + + vector & keypoints = allKeypoints[level]; + keypoints.reserve(nfeatures); + + keypoints = DistributeOctTree(vToDistributeKeys, minBorderX, maxBorderX, + minBorderY, maxBorderY,mnFeaturesPerLevel[level], level); + + const int scaledPatchSize = PATCH_SIZE*mvScaleFactor[level]; + + // Add border to coordinates and scale information + const int nkps = keypoints.size(); + for(int i=0; i > &allKeypoints) +{ + allKeypoints.resize(nlevels); + + float imageRatio = (float)mvImagePyramid[0].cols/mvImagePyramid[0].rows; + + for (int level = 0; level < nlevels; ++level) + { + const int nDesiredFeatures = mnFeaturesPerLevel[level]; + + const int levelCols = sqrt((float)nDesiredFeatures/(5*imageRatio)); + const int levelRows = imageRatio*levelCols; + + const int minBorderX = EDGE_THRESHOLD; + const int minBorderY = minBorderX; + const int maxBorderX = mvImagePyramid[level].cols-EDGE_THRESHOLD; + const int maxBorderY = mvImagePyramid[level].rows-EDGE_THRESHOLD; + + const int W = maxBorderX - minBorderX; + const int H = maxBorderY - minBorderY; + const int cellW = ceil((float)W/levelCols); + const int cellH = ceil((float)H/levelRows); + + const int nCells = levelRows*levelCols; + const int nfeaturesCell = ceil((float)nDesiredFeatures/nCells); + + vector > > cellKeyPoints(levelRows, vector >(levelCols)); + + vector > nToRetain(levelRows,vector(levelCols,0)); + vector > nTotal(levelRows,vector(levelCols,0)); + vector > bNoMore(levelRows,vector(levelCols,false)); + vector iniXCol(levelCols); + vector iniYRow(levelRows); + int nNoMore = 0; + int nToDistribute = 0; + + + float hY = cellH + 6; + + for(int i=0; infeaturesCell) + { + nToRetain[i][j] = nfeaturesCell; + bNoMore[i][j] = false; + } + else + { + nToRetain[i][j] = nKeys; + nToDistribute += nfeaturesCell-nKeys; + bNoMore[i][j] = true; + nNoMore++; + } + + } + } + + + // Retain by score + + while(nToDistribute>0 && nNoMorenNewFeaturesCell) + { + nToRetain[i][j] = nNewFeaturesCell; + bNoMore[i][j] = false; + } + else + { + nToRetain[i][j] = nTotal[i][j]; + nToDistribute += nNewFeaturesCell-nTotal[i][j]; + bNoMore[i][j] = true; + nNoMore++; + } + } + } + } + } + + vector & keypoints = allKeypoints[level]; + keypoints.reserve(nDesiredFeatures*2); + + const int scaledPatchSize = PATCH_SIZE*mvScaleFactor[level]; + + // Retain by score and transform coordinates + for(int i=0; i &keysCell = cellKeyPoints[i][j]; + KeyPointsFilter::retainBest(keysCell,nToRetain[i][j]); + if((int)keysCell.size()>nToRetain[i][j]) + keysCell.resize(nToRetain[i][j]); + + + for(size_t k=0, kend=keysCell.size(); knDesiredFeatures) + { + KeyPointsFilter::retainBest(keypoints,nDesiredFeatures); + keypoints.resize(nDesiredFeatures); + } + } + + // and compute orientations + for (int level = 0; level < nlevels; ++level) + computeOrientation(mvImagePyramid[level], allKeypoints[level], umax); +} + +static void computeDescriptors(const Mat& image, vector& keypoints, Mat& descriptors, + const vector& pattern) +{ + descriptors = Mat::zeros((int)keypoints.size(), 32, CV_8UC1); + + for (size_t i = 0; i < keypoints.size(); i++) + computeOrbDescriptor(keypoints[i], image, &pattern[0], descriptors.ptr((int)i)); +} + +void ORBextractor::operator()( InputArray _image, InputArray _mask, vector& _keypoints, + OutputArray _descriptors) +{ + if(_image.empty()) + return; + + Mat image = _image.getMat(); + assert(image.type() == CV_8UC1 ); + + // Pre-compute the scale pyramid + ComputePyramid(image); + + vector < vector > allKeypoints; + ComputeKeyPointsOctTree(allKeypoints); + //ComputeKeyPointsOld(allKeypoints); + + Mat descriptors; + + int nkeypoints = 0; + for (int level = 0; level < nlevels; ++level) + nkeypoints += (int)allKeypoints[level].size(); + if( nkeypoints == 0 ) + _descriptors.release(); + else + { + _descriptors.create(nkeypoints, 32, CV_8U); + descriptors = _descriptors.getMat(); + } + + _keypoints.clear(); + _keypoints.reserve(nkeypoints); + + int offset = 0; + for (int level = 0; level < nlevels; ++level) + { + vector& keypoints = allKeypoints[level]; + int nkeypointsLevel = (int)keypoints.size(); + + if(nkeypointsLevel==0) + continue; + + // preprocess the resized image + Mat workingMat = mvImagePyramid[level].clone(); + GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101); + + // Compute the descriptors + Mat desc = descriptors.rowRange(offset, offset + nkeypointsLevel); + computeDescriptors(workingMat, keypoints, desc, pattern); + + offset += nkeypointsLevel; + + // Scale keypoint coordinates + if (level != 0) + { + float scale = mvScaleFactor[level]; //getScale(level, firstLevel, scaleFactor); + for (vector::iterator keypoint = keypoints.begin(), + keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint) + keypoint->pt *= scale; + } + // And add the keypoints to the output + _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end()); + } +} + +void ORBextractor::ComputePyramid(cv::Mat image) +{ + for (int level = 0; level < nlevels; ++level) + { + float scale = mvInvScaleFactor[level]; + Size sz(cvRound((float)image.cols*scale), cvRound((float)image.rows*scale)); + Size wholeSize(sz.width + EDGE_THRESHOLD*2, sz.height + EDGE_THRESHOLD*2); + Mat temp(wholeSize, image.type()), masktemp; + mvImagePyramid[level] = temp(Rect(EDGE_THRESHOLD, EDGE_THRESHOLD, sz.width, sz.height)); + + // Compute the resized image + if( level != 0 ) + { + resize(mvImagePyramid[level-1], mvImagePyramid[level], sz, 0, 0, INTER_LINEAR); + + copyMakeBorder(mvImagePyramid[level], temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, + BORDER_REFLECT_101+BORDER_ISOLATED); + } + else + { + copyMakeBorder(image, temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, + BORDER_REFLECT_101); + } + } + +} + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/ORBmatcher.cc b/for_orb_slam2/src/ORBmatcher.cc new file mode 100644 index 00000000000..56bf279d0f5 --- /dev/null +++ b/for_orb_slam2/src/ORBmatcher.cc @@ -0,0 +1,1665 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#include "ORBmatcher.h" + +#include + +#include +#include + +#include "Thirdparty/DBoW2/DBoW2/FeatureVector.h" + +#include + +using namespace std; + +namespace ORB_SLAM2 +{ + +const int ORBmatcher::TH_HIGH = 100; +const int ORBmatcher::TH_LOW = 50; +const int ORBmatcher::HISTO_LENGTH = 30; + +ORBmatcher::ORBmatcher(float nnratio, bool checkOri): mfNNratio(nnratio), mbCheckOrientation(checkOri) +{ +} + +int ORBmatcher::SearchByProjection(Frame &F, const vector &vpMapPoints, const float th) +{ + int nmatches=0; + + const bool bFactor = th!=1.0; + + for(size_t iMP=0; iMPmbTrackInView) + continue; + + if(pMP->isBad()) + continue; + + const int &nPredictedLevel = pMP->mnTrackScaleLevel; + + // The size of the window will depend on the viewing direction + float r = RadiusByViewingCos(pMP->mTrackViewCos); + + if(bFactor) + r*=th; + + const vector vIndices = + F.GetFeaturesInArea(pMP->mTrackProjX,pMP->mTrackProjY,r*F.mvScaleFactors[nPredictedLevel],nPredictedLevel-1,nPredictedLevel); + + if(vIndices.empty()) + continue; + + const cv::Mat MPdescriptor = pMP->GetDescriptor(); + + int bestDist=256; + int bestLevel= -1; + int bestDist2=256; + int bestLevel2 = -1; + int bestIdx =-1 ; + + // Get best and second matches with near keypoints + for(vector::const_iterator vit=vIndices.begin(), vend=vIndices.end(); vit!=vend; vit++) + { + const size_t idx = *vit; + + if(F.mvpMapPoints[idx]) + if(F.mvpMapPoints[idx]->Observations()>0) + continue; + + if(F.mvuRight[idx]>0) + { + const float er = fabs(pMP->mTrackProjXR-F.mvuRight[idx]); + if(er>r*F.mvScaleFactors[nPredictedLevel]) + continue; + } + + const cv::Mat &d = F.mDescriptors.row(idx); + + const int dist = DescriptorDistance(MPdescriptor,d); + + if(distmfNNratio*bestDist2) + continue; + + F.mvpMapPoints[bestIdx]=pMP; + nmatches++; + } + } + + return nmatches; +} + +float ORBmatcher::RadiusByViewingCos(const float &viewCos) +{ + if(viewCos>0.998) + return 2.5; + else + return 4.0; +} + + +bool ORBmatcher::CheckDistEpipolarLine(const cv::KeyPoint &kp1,const cv::KeyPoint &kp2,const cv::Mat &F12,const KeyFrame* pKF2) +{ + // Epipolar line in second image l = x1'F12 = [a b c] + const float a = kp1.pt.x*F12.at(0,0)+kp1.pt.y*F12.at(1,0)+F12.at(2,0); + const float b = kp1.pt.x*F12.at(0,1)+kp1.pt.y*F12.at(1,1)+F12.at(2,1); + const float c = kp1.pt.x*F12.at(0,2)+kp1.pt.y*F12.at(1,2)+F12.at(2,2); + + const float num = a*kp2.pt.x+b*kp2.pt.y+c; + + const float den = a*a+b*b; + + if(den==0) + return false; + + const float dsqr = num*num/den; + + return dsqr<3.84*pKF2->mvLevelSigma2[kp2.octave]; +} + +int ORBmatcher::SearchByBoW(KeyFrame* pKF,Frame &F, vector &vpMapPointMatches) +{ + const vector vpMapPointsKF = pKF->GetMapPointMatches(); + + vpMapPointMatches = vector(F.N,static_cast(NULL)); + + const DBoW2::FeatureVector &vFeatVecKF = pKF->mFeatVec; + + int nmatches=0; + + vector rotHist[HISTO_LENGTH]; + for(int i=0;ifirst == Fit->first) + { + const vector vIndicesKF = KFit->second; + const vector vIndicesF = Fit->second; + + for(size_t iKF=0; iKFisBad()) + continue; + + const cv::Mat &dKF= pKF->mDescriptors.row(realIdxKF); + + int bestDist1=256; + int bestIdxF =-1 ; + int bestDist2=256; + + for(size_t iF=0; iF(bestDist1)(bestDist2)) + { + vpMapPointMatches[bestIdxF]=pMP; + + const cv::KeyPoint &kp = pKF->mvKeysUn[realIdxKF]; + + if(mbCheckOrientation) + { + float rot = kp.angle-F.mvKeys[bestIdxF].angle; + if(rot<0.0) + rot+=360.0f; + int bin = round(rot*factor); + if(bin==HISTO_LENGTH) + bin=0; + assert(bin>=0 && binfirst < Fit->first) + { + KFit = vFeatVecKF.lower_bound(Fit->first); + } + else + { + Fit = F.mFeatVec.lower_bound(KFit->first); + } + } + + + if(mbCheckOrientation) + { + int ind1=-1; + int ind2=-1; + int ind3=-1; + + ComputeThreeMaxima(rotHist,HISTO_LENGTH,ind1,ind2,ind3); + + for(int i=0; i(NULL); + nmatches--; + } + } + } + + return nmatches; +} + +int ORBmatcher::SearchByProjection(KeyFrame* pKF, cv::Mat Scw, const vector &vpPoints, vector &vpMatched, int th) +{ + // Get Calibration Parameters for later projection + const float &fx = pKF->fx; + const float &fy = pKF->fy; + const float &cx = pKF->cx; + const float &cy = pKF->cy; + + // Decompose Scw + cv::Mat sRcw = Scw.rowRange(0,3).colRange(0,3); + const float scw = sqrt(sRcw.row(0).dot(sRcw.row(0))); + cv::Mat Rcw = sRcw/scw; + cv::Mat tcw = Scw.rowRange(0,3).col(3)/scw; + cv::Mat Ow = -Rcw.t()*tcw; + + // Set of MapPoints already found in the KeyFrame + set spAlreadyFound(vpMatched.begin(), vpMatched.end()); + spAlreadyFound.erase(static_cast(NULL)); + + int nmatches=0; + + // For each Candidate MapPoint Project and Match + for(int iMP=0, iendMP=vpPoints.size(); iMPisBad() || spAlreadyFound.count(pMP)) + continue; + + // Get 3D Coords. + cv::Mat p3Dw = pMP->GetWorldPos(); + + // Transform into Camera Coords. + cv::Mat p3Dc = Rcw*p3Dw+tcw; + + // Depth must be positive + if(p3Dc.at(2)<0.0) + continue; + + // Project into Image + const float invz = 1/p3Dc.at(2); + const float x = p3Dc.at(0)*invz; + const float y = p3Dc.at(1)*invz; + + const float u = fx*x+cx; + const float v = fy*y+cy; + + // Point must be inside the image + if(!pKF->IsInImage(u,v)) + continue; + + // Depth must be inside the scale invariance region of the point + const float maxDistance = pMP->GetMaxDistanceInvariance(); + const float minDistance = pMP->GetMinDistanceInvariance(); + cv::Mat PO = p3Dw-Ow; + const float dist = cv::norm(PO); + + if(distmaxDistance) + continue; + + // Viewing angle must be less than 60 deg + cv::Mat Pn = pMP->GetNormal(); + + if(PO.dot(Pn)<0.5*dist) + continue; + + int nPredictedLevel = pMP->PredictScale(dist,pKF); + + // Search in a radius + const float radius = th*pKF->mvScaleFactors[nPredictedLevel]; + + const vector vIndices = pKF->GetFeaturesInArea(u,v,radius); + + if(vIndices.empty()) + continue; + + // Match to the most similar keypoint in the radius + const cv::Mat dMP = pMP->GetDescriptor(); + + int bestDist = 256; + int bestIdx = -1; + for(vector::const_iterator vit=vIndices.begin(), vend=vIndices.end(); vit!=vend; vit++) + { + const size_t idx = *vit; + if(vpMatched[idx]) + continue; + + const int &kpLevel= pKF->mvKeysUn[idx].octave; + + if(kpLevelnPredictedLevel) + continue; + + const cv::Mat &dKF = pKF->mDescriptors.row(idx); + + const int dist = DescriptorDistance(dMP,dKF); + + if(dist &vbPrevMatched, vector &vnMatches12, int windowSize) +{ + int nmatches=0; + vnMatches12 = vector(F1.mvKeysUn.size(),-1); + + vector rotHist[HISTO_LENGTH]; + for(int i=0;i vMatchedDistance(F2.mvKeysUn.size(),INT_MAX); + vector vnMatches21(F2.mvKeysUn.size(),-1); + + for(size_t i1=0, iend1=F1.mvKeysUn.size(); i10) + continue; + + vector vIndices2 = F2.GetFeaturesInArea(vbPrevMatched[i1].x,vbPrevMatched[i1].y, windowSize,level1,level1); + + if(vIndices2.empty()) + continue; + + cv::Mat d1 = F1.mDescriptors.row(i1); + + int bestDist = INT_MAX; + int bestDist2 = INT_MAX; + int bestIdx2 = -1; + + for(vector::iterator vit=vIndices2.begin(); vit!=vIndices2.end(); vit++) + { + size_t i2 = *vit; + + cv::Mat d2 = F2.mDescriptors.row(i2); + + int dist = DescriptorDistance(d1,d2); + + if(vMatchedDistance[i2]<=dist) + continue; + + if(dist=0) + { + vnMatches12[vnMatches21[bestIdx2]]=-1; + nmatches--; + } + vnMatches12[i1]=bestIdx2; + vnMatches21[bestIdx2]=i1; + vMatchedDistance[bestIdx2]=bestDist; + nmatches++; + + if(mbCheckOrientation) + { + float rot = F1.mvKeysUn[i1].angle-F2.mvKeysUn[bestIdx2].angle; + if(rot<0.0) + rot+=360.0f; + int bin = round(rot*factor); + if(bin==HISTO_LENGTH) + bin=0; + assert(bin>=0 && bin=0) + { + vnMatches12[idx1]=-1; + nmatches--; + } + } + } + + } + + //Update prev matched + for(size_t i1=0, iend1=vnMatches12.size(); i1=0) + vbPrevMatched[i1]=F2.mvKeysUn[vnMatches12[i1]].pt; + + return nmatches; +} + +int ORBmatcher::SearchByBoW(KeyFrame *pKF1, KeyFrame *pKF2, vector &vpMatches12) +{ + const vector &vKeysUn1 = pKF1->mvKeysUn; + const DBoW2::FeatureVector &vFeatVec1 = pKF1->mFeatVec; + const vector vpMapPoints1 = pKF1->GetMapPointMatches(); + const cv::Mat &Descriptors1 = pKF1->mDescriptors; + + const vector &vKeysUn2 = pKF2->mvKeysUn; + const DBoW2::FeatureVector &vFeatVec2 = pKF2->mFeatVec; + const vector vpMapPoints2 = pKF2->GetMapPointMatches(); + const cv::Mat &Descriptors2 = pKF2->mDescriptors; + + vpMatches12 = vector(vpMapPoints1.size(),static_cast(NULL)); + vector vbMatched2(vpMapPoints2.size(),false); + + vector rotHist[HISTO_LENGTH]; + for(int i=0;ifirst == f2it->first) + { + for(size_t i1=0, iend1=f1it->second.size(); i1second[i1]; + + MapPoint* pMP1 = vpMapPoints1[idx1]; + if(!pMP1) + continue; + if(pMP1->isBad()) + continue; + + const cv::Mat &d1 = Descriptors1.row(idx1); + + int bestDist1=256; + int bestIdx2 =-1 ; + int bestDist2=256; + + for(size_t i2=0, iend2=f2it->second.size(); i2second[i2]; + + MapPoint* pMP2 = vpMapPoints2[idx2]; + + if(vbMatched2[idx2] || !pMP2) + continue; + + if(pMP2->isBad()) + continue; + + const cv::Mat &d2 = Descriptors2.row(idx2); + + int dist = DescriptorDistance(d1,d2); + + if(dist(bestDist1)(bestDist2)) + { + vpMatches12[idx1]=vpMapPoints2[bestIdx2]; + vbMatched2[bestIdx2]=true; + + if(mbCheckOrientation) + { + float rot = vKeysUn1[idx1].angle-vKeysUn2[bestIdx2].angle; + if(rot<0.0) + rot+=360.0f; + int bin = round(rot*factor); + if(bin==HISTO_LENGTH) + bin=0; + assert(bin>=0 && binfirst < f2it->first) + { + f1it = vFeatVec1.lower_bound(f2it->first); + } + else + { + f2it = vFeatVec2.lower_bound(f1it->first); + } + } + + if(mbCheckOrientation) + { + int ind1=-1; + int ind2=-1; + int ind3=-1; + + ComputeThreeMaxima(rotHist,HISTO_LENGTH,ind1,ind2,ind3); + + for(int i=0; i(NULL); + nmatches--; + } + } + } + + return nmatches; +} + +int ORBmatcher::SearchForTriangulation(KeyFrame *pKF1, KeyFrame *pKF2, cv::Mat F12, + vector > &vMatchedPairs, const bool bOnlyStereo) +{ + const DBoW2::FeatureVector &vFeatVec1 = pKF1->mFeatVec; + const DBoW2::FeatureVector &vFeatVec2 = pKF2->mFeatVec; + + //Compute epipole in second image + cv::Mat Cw = pKF1->GetCameraCenter(); + cv::Mat R2w = pKF2->GetRotation(); + cv::Mat t2w = pKF2->GetTranslation(); + cv::Mat C2 = R2w*Cw+t2w; + const float invz = 1.0f/C2.at(2); + const float ex =pKF2->fx*C2.at(0)*invz+pKF2->cx; + const float ey =pKF2->fy*C2.at(1)*invz+pKF2->cy; + + // Find matches between not tracked keypoints + // Matching speed-up by ORB Vocabulary + // Compare only ORB that share the same node + + int nmatches=0; + vector vbMatched2(pKF2->N,false); + vector vMatches12(pKF1->N,-1); + + vector rotHist[HISTO_LENGTH]; + for(int i=0;ifirst == f2it->first) + { + for(size_t i1=0, iend1=f1it->second.size(); i1second[i1]; + + MapPoint* pMP1 = pKF1->GetMapPoint(idx1); + + // If there is already a MapPoint skip + if(pMP1) + continue; + + const bool bStereo1 = pKF1->mvuRight[idx1]>=0; + + if(bOnlyStereo) + if(!bStereo1) + continue; + + const cv::KeyPoint &kp1 = pKF1->mvKeysUn[idx1]; + + const cv::Mat &d1 = pKF1->mDescriptors.row(idx1); + + int bestDist = TH_LOW; + int bestIdx2 = -1; + + for(size_t i2=0, iend2=f2it->second.size(); i2second[i2]; + + MapPoint* pMP2 = pKF2->GetMapPoint(idx2); + + // If we have already matched or there is a MapPoint skip + if(vbMatched2[idx2] || pMP2) + continue; + + const bool bStereo2 = pKF2->mvuRight[idx2]>=0; + + if(bOnlyStereo) + if(!bStereo2) + continue; + + const cv::Mat &d2 = pKF2->mDescriptors.row(idx2); + + const int dist = DescriptorDistance(d1,d2); + + if(dist>TH_LOW || dist>bestDist) + continue; + + const cv::KeyPoint &kp2 = pKF2->mvKeysUn[idx2]; + + if(!bStereo1 && !bStereo2) + { + const float distex = ex-kp2.pt.x; + const float distey = ey-kp2.pt.y; + if(distex*distex+distey*distey<100*pKF2->mvScaleFactors[kp2.octave]) + continue; + } + + if(CheckDistEpipolarLine(kp1,kp2,F12,pKF2)) + { + bestIdx2 = idx2; + bestDist = dist; + } + } + + if(bestIdx2>=0) + { + const cv::KeyPoint &kp2 = pKF2->mvKeysUn[bestIdx2]; + vMatches12[idx1]=bestIdx2; + nmatches++; + + if(mbCheckOrientation) + { + float rot = kp1.angle-kp2.angle; + if(rot<0.0) + rot+=360.0f; + int bin = round(rot*factor); + if(bin==HISTO_LENGTH) + bin=0; + assert(bin>=0 && binfirst < f2it->first) + { + f1it = vFeatVec1.lower_bound(f2it->first); + } + else + { + f2it = vFeatVec2.lower_bound(f1it->first); + } + } + + if(mbCheckOrientation) + { + int ind1=-1; + int ind2=-1; + int ind3=-1; + + ComputeThreeMaxima(rotHist,HISTO_LENGTH,ind1,ind2,ind3); + + for(int i=0; i &vpMapPoints, const float th) +{ + cv::Mat Rcw = pKF->GetRotation(); + cv::Mat tcw = pKF->GetTranslation(); + + const float &fx = pKF->fx; + const float &fy = pKF->fy; + const float &cx = pKF->cx; + const float &cy = pKF->cy; + const float &bf = pKF->mbf; + + cv::Mat Ow = pKF->GetCameraCenter(); + + int nFused=0; + + const int nMPs = vpMapPoints.size(); + + for(int i=0; iisBad() || pMP->IsInKeyFrame(pKF)) + continue; + + cv::Mat p3Dw = pMP->GetWorldPos(); + cv::Mat p3Dc = Rcw*p3Dw + tcw; + + // Depth must be positive + if(p3Dc.at(2)<0.0f) + continue; + + const float invz = 1/p3Dc.at(2); + const float x = p3Dc.at(0)*invz; + const float y = p3Dc.at(1)*invz; + + const float u = fx*x+cx; + const float v = fy*y+cy; + + // Point must be inside the image + if(!pKF->IsInImage(u,v)) + continue; + + const float ur = u-bf*invz; + + const float maxDistance = pMP->GetMaxDistanceInvariance(); + const float minDistance = pMP->GetMinDistanceInvariance(); + cv::Mat PO = p3Dw-Ow; + const float dist3D = cv::norm(PO); + + // Depth must be inside the scale pyramid of the image + if(dist3DmaxDistance ) + continue; + + // Viewing angle must be less than 60 deg + cv::Mat Pn = pMP->GetNormal(); + + if(PO.dot(Pn)<0.5*dist3D) + continue; + + int nPredictedLevel = pMP->PredictScale(dist3D,pKF); + + // Search in a radius + const float radius = th*pKF->mvScaleFactors[nPredictedLevel]; + + const vector vIndices = pKF->GetFeaturesInArea(u,v,radius); + + if(vIndices.empty()) + continue; + + // Match to the most similar keypoint in the radius + + const cv::Mat dMP = pMP->GetDescriptor(); + + int bestDist = 256; + int bestIdx = -1; + for(vector::const_iterator vit=vIndices.begin(), vend=vIndices.end(); vit!=vend; vit++) + { + const size_t idx = *vit; + + const cv::KeyPoint &kp = pKF->mvKeysUn[idx]; + + const int &kpLevel= kp.octave; + + if(kpLevelnPredictedLevel) + continue; + + if(pKF->mvuRight[idx]>=0) + { + // Check reprojection error in stereo + const float &kpx = kp.pt.x; + const float &kpy = kp.pt.y; + const float &kpr = pKF->mvuRight[idx]; + const float ex = u-kpx; + const float ey = v-kpy; + const float er = ur-kpr; + const float e2 = ex*ex+ey*ey+er*er; + + if(e2*pKF->mvInvLevelSigma2[kpLevel]>7.8) + continue; + } + else + { + const float &kpx = kp.pt.x; + const float &kpy = kp.pt.y; + const float ex = u-kpx; + const float ey = v-kpy; + const float e2 = ex*ex+ey*ey; + + if(e2*pKF->mvInvLevelSigma2[kpLevel]>5.99) + continue; + } + + const cv::Mat &dKF = pKF->mDescriptors.row(idx); + + const int dist = DescriptorDistance(dMP,dKF); + + if(distGetMapPoint(bestIdx); + if(pMPinKF) + { + if(!pMPinKF->isBad()) + { + if(pMPinKF->Observations()>pMP->Observations()) + pMP->Replace(pMPinKF); + else + pMPinKF->Replace(pMP); + } + } + else + { + pMP->AddObservation(pKF,bestIdx); + pKF->AddMapPoint(pMP,bestIdx); + } + nFused++; + } + } + + return nFused; +} + +int ORBmatcher::Fuse(KeyFrame *pKF, cv::Mat Scw, const vector &vpPoints, float th, vector &vpReplacePoint) +{ + // Get Calibration Parameters for later projection + const float &fx = pKF->fx; + const float &fy = pKF->fy; + const float &cx = pKF->cx; + const float &cy = pKF->cy; + + // Decompose Scw + cv::Mat sRcw = Scw.rowRange(0,3).colRange(0,3); + const float scw = sqrt(sRcw.row(0).dot(sRcw.row(0))); + cv::Mat Rcw = sRcw/scw; + cv::Mat tcw = Scw.rowRange(0,3).col(3)/scw; + cv::Mat Ow = -Rcw.t()*tcw; + + // Set of MapPoints already found in the KeyFrame + const set spAlreadyFound = pKF->GetMapPoints(); + + int nFused=0; + + const int nPoints = vpPoints.size(); + + // For each candidate MapPoint project and match + for(int iMP=0; iMPisBad() || spAlreadyFound.count(pMP)) + continue; + + // Get 3D Coords. + cv::Mat p3Dw = pMP->GetWorldPos(); + + // Transform into Camera Coords. + cv::Mat p3Dc = Rcw*p3Dw+tcw; + + // Depth must be positive + if(p3Dc.at(2)<0.0f) + continue; + + // Project into Image + const float invz = 1.0/p3Dc.at(2); + const float x = p3Dc.at(0)*invz; + const float y = p3Dc.at(1)*invz; + + const float u = fx*x+cx; + const float v = fy*y+cy; + + // Point must be inside the image + if(!pKF->IsInImage(u,v)) + continue; + + // Depth must be inside the scale pyramid of the image + const float maxDistance = pMP->GetMaxDistanceInvariance(); + const float minDistance = pMP->GetMinDistanceInvariance(); + cv::Mat PO = p3Dw-Ow; + const float dist3D = cv::norm(PO); + + if(dist3DmaxDistance) + continue; + + // Viewing angle must be less than 60 deg + cv::Mat Pn = pMP->GetNormal(); + + if(PO.dot(Pn)<0.5*dist3D) + continue; + + // Compute predicted scale level + const int nPredictedLevel = pMP->PredictScale(dist3D,pKF); + + // Search in a radius + const float radius = th*pKF->mvScaleFactors[nPredictedLevel]; + + const vector vIndices = pKF->GetFeaturesInArea(u,v,radius); + + if(vIndices.empty()) + continue; + + // Match to the most similar keypoint in the radius + + const cv::Mat dMP = pMP->GetDescriptor(); + + int bestDist = INT_MAX; + int bestIdx = -1; + for(vector::const_iterator vit=vIndices.begin(); vit!=vIndices.end(); vit++) + { + const size_t idx = *vit; + const int &kpLevel = pKF->mvKeysUn[idx].octave; + + if(kpLevelnPredictedLevel) + continue; + + const cv::Mat &dKF = pKF->mDescriptors.row(idx); + + int dist = DescriptorDistance(dMP,dKF); + + if(distGetMapPoint(bestIdx); + if(pMPinKF) + { + if(!pMPinKF->isBad()) + vpReplacePoint[iMP] = pMPinKF; + } + else + { + pMP->AddObservation(pKF,bestIdx); + pKF->AddMapPoint(pMP,bestIdx); + } + nFused++; + } + } + + return nFused; +} + +int ORBmatcher::SearchBySim3(KeyFrame *pKF1, KeyFrame *pKF2, vector &vpMatches12, + const float &s12, const cv::Mat &R12, const cv::Mat &t12, const float th) +{ + const float &fx = pKF1->fx; + const float &fy = pKF1->fy; + const float &cx = pKF1->cx; + const float &cy = pKF1->cy; + + // Camera 1 from world + cv::Mat R1w = pKF1->GetRotation(); + cv::Mat t1w = pKF1->GetTranslation(); + + //Camera 2 from world + cv::Mat R2w = pKF2->GetRotation(); + cv::Mat t2w = pKF2->GetTranslation(); + + //Transformation between cameras + cv::Mat sR12 = s12*R12; + cv::Mat sR21 = (1.0/s12)*R12.t(); + cv::Mat t21 = -sR21*t12; + + const vector vpMapPoints1 = pKF1->GetMapPointMatches(); + const int N1 = vpMapPoints1.size(); + + const vector vpMapPoints2 = pKF2->GetMapPointMatches(); + const int N2 = vpMapPoints2.size(); + + vector vbAlreadyMatched1(N1,false); + vector vbAlreadyMatched2(N2,false); + + for(int i=0; iGetIndexInKeyFrame(pKF2); + if(idx2>=0 && idx2 vnMatch1(N1,-1); + vector vnMatch2(N2,-1); + + // Transform from KF1 to KF2 and search + for(int i1=0; i1isBad()) + continue; + + cv::Mat p3Dw = pMP->GetWorldPos(); + cv::Mat p3Dc1 = R1w*p3Dw + t1w; + cv::Mat p3Dc2 = sR21*p3Dc1 + t21; + + // Depth must be positive + if(p3Dc2.at(2)<0.0) + continue; + + const float invz = 1.0/p3Dc2.at(2); + const float x = p3Dc2.at(0)*invz; + const float y = p3Dc2.at(1)*invz; + + const float u = fx*x+cx; + const float v = fy*y+cy; + + // Point must be inside the image + if(!pKF2->IsInImage(u,v)) + continue; + + const float maxDistance = pMP->GetMaxDistanceInvariance(); + const float minDistance = pMP->GetMinDistanceInvariance(); + const float dist3D = cv::norm(p3Dc2); + + // Depth must be inside the scale invariance region + if(dist3DmaxDistance ) + continue; + + // Compute predicted octave + const int nPredictedLevel = pMP->PredictScale(dist3D,pKF2); + + // Search in a radius + const float radius = th*pKF2->mvScaleFactors[nPredictedLevel]; + + const vector vIndices = pKF2->GetFeaturesInArea(u,v,radius); + + if(vIndices.empty()) + continue; + + // Match to the most similar keypoint in the radius + const cv::Mat dMP = pMP->GetDescriptor(); + + int bestDist = INT_MAX; + int bestIdx = -1; + for(vector::const_iterator vit=vIndices.begin(), vend=vIndices.end(); vit!=vend; vit++) + { + const size_t idx = *vit; + + const cv::KeyPoint &kp = pKF2->mvKeysUn[idx]; + + if(kp.octavenPredictedLevel) + continue; + + const cv::Mat &dKF = pKF2->mDescriptors.row(idx); + + const int dist = DescriptorDistance(dMP,dKF); + + if(distisBad()) + continue; + + cv::Mat p3Dw = pMP->GetWorldPos(); + cv::Mat p3Dc2 = R2w*p3Dw + t2w; + cv::Mat p3Dc1 = sR12*p3Dc2 + t12; + + // Depth must be positive + if(p3Dc1.at(2)<0.0) + continue; + + const float invz = 1.0/p3Dc1.at(2); + const float x = p3Dc1.at(0)*invz; + const float y = p3Dc1.at(1)*invz; + + const float u = fx*x+cx; + const float v = fy*y+cy; + + // Point must be inside the image + if(!pKF1->IsInImage(u,v)) + continue; + + const float maxDistance = pMP->GetMaxDistanceInvariance(); + const float minDistance = pMP->GetMinDistanceInvariance(); + const float dist3D = cv::norm(p3Dc1); + + // Depth must be inside the scale pyramid of the image + if(dist3DmaxDistance) + continue; + + // Compute predicted octave + const int nPredictedLevel = pMP->PredictScale(dist3D,pKF1); + + // Search in a radius of 2.5*sigma(ScaleLevel) + const float radius = th*pKF1->mvScaleFactors[nPredictedLevel]; + + const vector vIndices = pKF1->GetFeaturesInArea(u,v,radius); + + if(vIndices.empty()) + continue; + + // Match to the most similar keypoint in the radius + const cv::Mat dMP = pMP->GetDescriptor(); + + int bestDist = INT_MAX; + int bestIdx = -1; + for(vector::const_iterator vit=vIndices.begin(), vend=vIndices.end(); vit!=vend; vit++) + { + const size_t idx = *vit; + + const cv::KeyPoint &kp = pKF1->mvKeysUn[idx]; + + if(kp.octavenPredictedLevel) + continue; + + const cv::Mat &dKF = pKF1->mDescriptors.row(idx); + + const int dist = DescriptorDistance(dMP,dKF); + + if(dist=0) + { + int idx1 = vnMatch2[idx2]; + if(idx1==i1) + { + vpMatches12[i1] = vpMapPoints2[idx2]; + nFound++; + } + } + } + + return nFound; +} + +int ORBmatcher::SearchByProjection(Frame &CurrentFrame, const Frame &LastFrame, const float th, const bool bMono) +{ + int nmatches = 0; + + // Rotation Histogram (to check rotation consistency) + vector rotHist[HISTO_LENGTH]; + for(int i=0;i(2)>CurrentFrame.mb && !bMono; + const bool bBackward = -tlc.at(2)>CurrentFrame.mb && !bMono; + + for(int i=0; iGetWorldPos(); + cv::Mat x3Dc = Rcw*x3Dw+tcw; + + const float xc = x3Dc.at(0); + const float yc = x3Dc.at(1); + const float invzc = 1.0/x3Dc.at(2); + + if(invzc<0) + continue; + + float u = CurrentFrame.fx*xc*invzc+CurrentFrame.cx; + float v = CurrentFrame.fy*yc*invzc+CurrentFrame.cy; + + if(uCurrentFrame.mnMaxX) + continue; + if(vCurrentFrame.mnMaxY) + continue; + + int nLastOctave = LastFrame.mvKeys[i].octave; + + // Search in a window. Size depends on scale + float radius = th*CurrentFrame.mvScaleFactors[nLastOctave]; + + vector vIndices2; + + if(bForward) + vIndices2 = CurrentFrame.GetFeaturesInArea(u,v, radius, nLastOctave); + else if(bBackward) + vIndices2 = CurrentFrame.GetFeaturesInArea(u,v, radius, 0, nLastOctave); + else + vIndices2 = CurrentFrame.GetFeaturesInArea(u,v, radius, nLastOctave-1, nLastOctave+1); + + if(vIndices2.empty()) + continue; + + const cv::Mat dMP = pMP->GetDescriptor(); + + int bestDist = 256; + int bestIdx2 = -1; + + for(vector::const_iterator vit=vIndices2.begin(), vend=vIndices2.end(); vit!=vend; vit++) + { + const size_t i2 = *vit; + if(CurrentFrame.mvpMapPoints[i2]) + if(CurrentFrame.mvpMapPoints[i2]->Observations()>0) + continue; + + if(CurrentFrame.mvuRight[i2]>0) + { + const float ur = u - CurrentFrame.mbf*invzc; + const float er = fabs(ur - CurrentFrame.mvuRight[i2]); + if(er>radius) + continue; + } + + const cv::Mat &d = CurrentFrame.mDescriptors.row(i2); + + const int dist = DescriptorDistance(dMP,d); + + if(dist=0 && bin(NULL); + nmatches--; + } + } + } + } + + return nmatches; +} + +int ORBmatcher::SearchByProjection(Frame &CurrentFrame, KeyFrame *pKF, const set &sAlreadyFound, const float th , const int ORBdist) +{ + int nmatches = 0; + + const cv::Mat Rcw = CurrentFrame.mTcw.rowRange(0,3).colRange(0,3); + const cv::Mat tcw = CurrentFrame.mTcw.rowRange(0,3).col(3); + const cv::Mat Ow = -Rcw.t()*tcw; + + // Rotation Histogram (to check rotation consistency) + vector rotHist[HISTO_LENGTH]; + for(int i=0;i vpMPs = pKF->GetMapPointMatches(); + + for(size_t i=0, iend=vpMPs.size(); iisBad() && !sAlreadyFound.count(pMP)) + { + //Project + cv::Mat x3Dw = pMP->GetWorldPos(); + cv::Mat x3Dc = Rcw*x3Dw+tcw; + + const float xc = x3Dc.at(0); + const float yc = x3Dc.at(1); + const float invzc = 1.0/x3Dc.at(2); + + const float u = CurrentFrame.fx*xc*invzc+CurrentFrame.cx; + const float v = CurrentFrame.fy*yc*invzc+CurrentFrame.cy; + + if(uCurrentFrame.mnMaxX) + continue; + if(vCurrentFrame.mnMaxY) + continue; + + // Compute predicted scale level + cv::Mat PO = x3Dw-Ow; + float dist3D = cv::norm(PO); + + const float maxDistance = pMP->GetMaxDistanceInvariance(); + const float minDistance = pMP->GetMinDistanceInvariance(); + + // Depth must be inside the scale pyramid of the image + if(dist3DmaxDistance) + continue; + + int nPredictedLevel = pMP->PredictScale(dist3D,&CurrentFrame); + + // Search in a window + const float radius = th*CurrentFrame.mvScaleFactors[nPredictedLevel]; + + const vector vIndices2 = CurrentFrame.GetFeaturesInArea(u, v, radius, nPredictedLevel-1, nPredictedLevel+1); + + if(vIndices2.empty()) + continue; + + const cv::Mat dMP = pMP->GetDescriptor(); + + int bestDist = 256; + int bestIdx2 = -1; + + for(vector::const_iterator vit=vIndices2.begin(); vit!=vIndices2.end(); vit++) + { + const size_t i2 = *vit; + if(CurrentFrame.mvpMapPoints[i2]) + continue; + + const cv::Mat &d = CurrentFrame.mDescriptors.row(i2); + + const int dist = DescriptorDistance(dMP,d); + + if(distmvKeysUn[i].angle-CurrentFrame.mvKeysUn[bestIdx2].angle; + if(rot<0.0) + rot+=360.0f; + int bin = round(rot*factor); + if(bin==HISTO_LENGTH) + bin=0; + assert(bin>=0 && bin* histo, const int L, int &ind1, int &ind2, int &ind3) +{ + int max1=0; + int max2=0; + int max3=0; + + for(int i=0; imax1) + { + max3=max2; + max2=max1; + max1=s; + ind3=ind2; + ind2=ind1; + ind1=i; + } + else if(s>max2) + { + max3=max2; + max2=s; + ind3=ind2; + ind2=i; + } + else if(s>max3) + { + max3=s; + ind3=i; + } + } + + if(max2<0.1f*(float)max1) + { + ind2=-1; + ind3=-1; + } + else if(max3<0.1f*(float)max1) + { + ind3=-1; + } +} + + +// Bit set count operation from +// http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetParallel +int ORBmatcher::DescriptorDistance(const cv::Mat &a, const cv::Mat &b) +{ + const int *pa = a.ptr(); + const int *pb = b.ptr(); + + int dist=0; + + for(int i=0; i<8; i++, pa++, pb++) + { + unsigned int v = *pa ^ *pb; + v = v - ((v >> 1) & 0x55555555); + v = (v & 0x33333333) + ((v >> 2) & 0x33333333); + dist += (((v + (v >> 4)) & 0xF0F0F0F) * 0x1010101) >> 24; + } + + return dist; +} + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/Optimizer.cc b/for_orb_slam2/src/Optimizer.cc new file mode 100644 index 00000000000..83d9264546b --- /dev/null +++ b/for_orb_slam2/src/Optimizer.cc @@ -0,0 +1,1244 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#include "Optimizer.h" + +#include "Thirdparty/g2o/g2o/core/block_solver.h" +#include "Thirdparty/g2o/g2o/core/optimization_algorithm_levenberg.h" +#include "Thirdparty/g2o/g2o/solvers/linear_solver_eigen.h" +#include "Thirdparty/g2o/g2o/types/types_six_dof_expmap.h" +#include "Thirdparty/g2o/g2o/core/robust_kernel_impl.h" +#include "Thirdparty/g2o/g2o/solvers/linear_solver_dense.h" +#include "Thirdparty/g2o/g2o/types/types_seven_dof_expmap.h" + +#include + +#include "Converter.h" + +#include + +namespace ORB_SLAM2 +{ + + +void Optimizer::GlobalBundleAdjustemnt(Map* pMap, int nIterations, bool* pbStopFlag, const unsigned long nLoopKF, const bool bRobust) +{ + vector vpKFs = pMap->GetAllKeyFrames(); + vector vpMP = pMap->GetAllMapPoints(); + BundleAdjustment(vpKFs,vpMP,nIterations,pbStopFlag, nLoopKF, bRobust); +} + + +void Optimizer::BundleAdjustment(const vector &vpKFs, const vector &vpMP, + int nIterations, bool* pbStopFlag, const unsigned long nLoopKF, const bool bRobust) +{ + vector vbNotIncludedMP; + vbNotIncludedMP.resize(vpMP.size()); + + g2o::SparseOptimizer optimizer; + g2o::BlockSolver_6_3::LinearSolverType * linearSolver; + + linearSolver = new g2o::LinearSolverEigen(); + + g2o::BlockSolver_6_3 * solver_ptr = new g2o::BlockSolver_6_3(linearSolver); + + g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr); + optimizer.setAlgorithm(solver); + + if(pbStopFlag) + optimizer.setForceStopFlag(pbStopFlag); + + long unsigned int maxKFid = 0; + + // Set KeyFrame vertices + for(size_t i=0; iisBad()) + continue; + g2o::VertexSE3Expmap * vSE3 = new g2o::VertexSE3Expmap(); + vSE3->setEstimate(Converter::toSE3Quat(pKF->GetPose())); + vSE3->setId(pKF->mnId); + vSE3->setFixed(pKF->mnId==0); + optimizer.addVertex(vSE3); + if(pKF->mnId>maxKFid) + maxKFid=pKF->mnId; + } + + const float thHuber2D = sqrt(5.99); + const float thHuber3D = sqrt(7.815); + + // Set MapPoint vertices + for(size_t i=0; iisBad()) + continue; + g2o::VertexSBAPointXYZ* vPoint = new g2o::VertexSBAPointXYZ(); + vPoint->setEstimate(Converter::toVector3d(pMP->GetWorldPos())); + const int id = pMP->mnId+maxKFid+1; + vPoint->setId(id); + vPoint->setMarginalized(true); + optimizer.addVertex(vPoint); + + const map observations = pMP->GetObservations(); + + int nEdges = 0; + //SET EDGES + for(map::const_iterator mit=observations.begin(); mit!=observations.end(); mit++) + { + + KeyFrame* pKF = mit->first; + if(pKF->isBad() || pKF->mnId>maxKFid) + continue; + + nEdges++; + + const cv::KeyPoint &kpUn = pKF->mvKeysUn[mit->second]; + + if(pKF->mvuRight[mit->second]<0) + { + Eigen::Matrix obs; + obs << kpUn.pt.x, kpUn.pt.y; + + g2o::EdgeSE3ProjectXYZ* e = new g2o::EdgeSE3ProjectXYZ(); + + e->setVertex(0, dynamic_cast(optimizer.vertex(id))); + e->setVertex(1, dynamic_cast(optimizer.vertex(pKF->mnId))); + e->setMeasurement(obs); + const float &invSigma2 = pKF->mvInvLevelSigma2[kpUn.octave]; + e->setInformation(Eigen::Matrix2d::Identity()*invSigma2); + + if(bRobust) + { + g2o::RobustKernelHuber* rk = new g2o::RobustKernelHuber; + e->setRobustKernel(rk); + rk->setDelta(thHuber2D); + } + + e->fx = pKF->fx; + e->fy = pKF->fy; + e->cx = pKF->cx; + e->cy = pKF->cy; + + optimizer.addEdge(e); + } + else + { + Eigen::Matrix obs; + const float kp_ur = pKF->mvuRight[mit->second]; + obs << kpUn.pt.x, kpUn.pt.y, kp_ur; + + g2o::EdgeStereoSE3ProjectXYZ* e = new g2o::EdgeStereoSE3ProjectXYZ(); + + e->setVertex(0, dynamic_cast(optimizer.vertex(id))); + e->setVertex(1, dynamic_cast(optimizer.vertex(pKF->mnId))); + e->setMeasurement(obs); + const float &invSigma2 = pKF->mvInvLevelSigma2[kpUn.octave]; + Eigen::Matrix3d Info = Eigen::Matrix3d::Identity()*invSigma2; + e->setInformation(Info); + + if(bRobust) + { + g2o::RobustKernelHuber* rk = new g2o::RobustKernelHuber; + e->setRobustKernel(rk); + rk->setDelta(thHuber3D); + } + + e->fx = pKF->fx; + e->fy = pKF->fy; + e->cx = pKF->cx; + e->cy = pKF->cy; + e->bf = pKF->mbf; + + optimizer.addEdge(e); + } + } + + if(nEdges==0) + { + optimizer.removeVertex(vPoint); + vbNotIncludedMP[i]=true; + } + else + { + vbNotIncludedMP[i]=false; + } + } + + // Optimize! + optimizer.initializeOptimization(); + optimizer.optimize(nIterations); + + // Recover optimized data + + //Keyframes + for(size_t i=0; iisBad()) + continue; + g2o::VertexSE3Expmap* vSE3 = static_cast(optimizer.vertex(pKF->mnId)); + g2o::SE3Quat SE3quat = vSE3->estimate(); + if(nLoopKF==0) + { + pKF->SetPose(Converter::toCvMat(SE3quat)); + } + else + { + pKF->mTcwGBA.create(4,4,CV_32F); + Converter::toCvMat(SE3quat).copyTo(pKF->mTcwGBA); + pKF->mnBAGlobalForKF = nLoopKF; + } + } + + //Points + for(size_t i=0; iisBad()) + continue; + g2o::VertexSBAPointXYZ* vPoint = static_cast(optimizer.vertex(pMP->mnId+maxKFid+1)); + + if(nLoopKF==0) + { + pMP->SetWorldPos(Converter::toCvMat(vPoint->estimate())); + pMP->UpdateNormalAndDepth(); + } + else + { + pMP->mPosGBA.create(3,1,CV_32F); + Converter::toCvMat(vPoint->estimate()).copyTo(pMP->mPosGBA); + pMP->mnBAGlobalForKF = nLoopKF; + } + } + +} + +int Optimizer::PoseOptimization(Frame *pFrame) +{ + g2o::SparseOptimizer optimizer; + g2o::BlockSolver_6_3::LinearSolverType * linearSolver; + + linearSolver = new g2o::LinearSolverDense(); + + g2o::BlockSolver_6_3 * solver_ptr = new g2o::BlockSolver_6_3(linearSolver); + + g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr); + optimizer.setAlgorithm(solver); + + int nInitialCorrespondences=0; + + // Set Frame vertex + g2o::VertexSE3Expmap * vSE3 = new g2o::VertexSE3Expmap(); + vSE3->setEstimate(Converter::toSE3Quat(pFrame->mTcw)); + vSE3->setId(0); + vSE3->setFixed(false); + optimizer.addVertex(vSE3); + + // Set MapPoint vertices + const int N = pFrame->N; + + vector vpEdgesMono; + vector vnIndexEdgeMono; + vpEdgesMono.reserve(N); + vnIndexEdgeMono.reserve(N); + + vector vpEdgesStereo; + vector vnIndexEdgeStereo; + vpEdgesStereo.reserve(N); + vnIndexEdgeStereo.reserve(N); + + const float deltaMono = sqrt(5.991); + const float deltaStereo = sqrt(7.815); + + + { + unique_lock lock(MapPoint::mGlobalMutex); + + for(int i=0; imvpMapPoints[i]; + if(pMP) + { + // Monocular observation + if(pFrame->mvuRight[i]<0) + { + nInitialCorrespondences++; + pFrame->mvbOutlier[i] = false; + + Eigen::Matrix obs; + const cv::KeyPoint &kpUn = pFrame->mvKeysUn[i]; + obs << kpUn.pt.x, kpUn.pt.y; + + g2o::EdgeSE3ProjectXYZOnlyPose* e = new g2o::EdgeSE3ProjectXYZOnlyPose(); + + e->setVertex(0, dynamic_cast(optimizer.vertex(0))); + e->setMeasurement(obs); + const float invSigma2 = pFrame->mvInvLevelSigma2[kpUn.octave]; + e->setInformation(Eigen::Matrix2d::Identity()*invSigma2); + + g2o::RobustKernelHuber* rk = new g2o::RobustKernelHuber; + e->setRobustKernel(rk); + rk->setDelta(deltaMono); + + e->fx = pFrame->fx; + e->fy = pFrame->fy; + e->cx = pFrame->cx; + e->cy = pFrame->cy; + cv::Mat Xw = pMP->GetWorldPos(); + e->Xw[0] = Xw.at(0); + e->Xw[1] = Xw.at(1); + e->Xw[2] = Xw.at(2); + + optimizer.addEdge(e); + + vpEdgesMono.push_back(e); + vnIndexEdgeMono.push_back(i); + } + else // Stereo observation + { + nInitialCorrespondences++; + pFrame->mvbOutlier[i] = false; + + //SET EDGE + Eigen::Matrix obs; + const cv::KeyPoint &kpUn = pFrame->mvKeysUn[i]; + const float &kp_ur = pFrame->mvuRight[i]; + obs << kpUn.pt.x, kpUn.pt.y, kp_ur; + + g2o::EdgeStereoSE3ProjectXYZOnlyPose* e = new g2o::EdgeStereoSE3ProjectXYZOnlyPose(); + + e->setVertex(0, dynamic_cast(optimizer.vertex(0))); + e->setMeasurement(obs); + const float invSigma2 = pFrame->mvInvLevelSigma2[kpUn.octave]; + Eigen::Matrix3d Info = Eigen::Matrix3d::Identity()*invSigma2; + e->setInformation(Info); + + g2o::RobustKernelHuber* rk = new g2o::RobustKernelHuber; + e->setRobustKernel(rk); + rk->setDelta(deltaStereo); + + e->fx = pFrame->fx; + e->fy = pFrame->fy; + e->cx = pFrame->cx; + e->cy = pFrame->cy; + e->bf = pFrame->mbf; + cv::Mat Xw = pMP->GetWorldPos(); + e->Xw[0] = Xw.at(0); + e->Xw[1] = Xw.at(1); + e->Xw[2] = Xw.at(2); + + optimizer.addEdge(e); + + vpEdgesStereo.push_back(e); + vnIndexEdgeStereo.push_back(i); + } + } + + } + } + + + if(nInitialCorrespondences<3) + return 0; + + // We perform 4 optimizations, after each optimization we classify observation as inlier/outlier + // At the next optimization, outliers are not included, but at the end they can be classified as inliers again. + const float chi2Mono[4]={5.991,5.991,5.991,5.991}; + const float chi2Stereo[4]={7.815,7.815,7.815, 7.815}; + const int its[4]={10,10,10,10}; + + int nBad=0; + for(size_t it=0; it<4; it++) + { + + vSE3->setEstimate(Converter::toSE3Quat(pFrame->mTcw)); + optimizer.initializeOptimization(0); + optimizer.optimize(its[it]); + + nBad=0; + for(size_t i=0, iend=vpEdgesMono.size(); imvbOutlier[idx]) + { + e->computeError(); + } + + const float chi2 = e->chi2(); + + if(chi2>chi2Mono[it]) + { + pFrame->mvbOutlier[idx]=true; + e->setLevel(1); + nBad++; + } + else + { + pFrame->mvbOutlier[idx]=false; + e->setLevel(0); + } + + if(it==2) + e->setRobustKernel(0); + } + + for(size_t i=0, iend=vpEdgesStereo.size(); imvbOutlier[idx]) + { + e->computeError(); + } + + const float chi2 = e->chi2(); + + if(chi2>chi2Stereo[it]) + { + pFrame->mvbOutlier[idx]=true; + e->setLevel(1); + nBad++; + } + else + { + e->setLevel(0); + pFrame->mvbOutlier[idx]=false; + } + + if(it==2) + e->setRobustKernel(0); + } + + if(optimizer.edges().size()<10) + break; + } + + // Recover optimized pose and return number of inliers + g2o::VertexSE3Expmap* vSE3_recov = static_cast(optimizer.vertex(0)); + g2o::SE3Quat SE3quat_recov = vSE3_recov->estimate(); + cv::Mat pose = Converter::toCvMat(SE3quat_recov); + pFrame->SetPose(pose); + + return nInitialCorrespondences-nBad; +} + +void Optimizer::LocalBundleAdjustment(KeyFrame *pKF, bool* pbStopFlag, Map* pMap) +{ + // Local KeyFrames: First Breath Search from Current Keyframe + list lLocalKeyFrames; + + lLocalKeyFrames.push_back(pKF); + pKF->mnBALocalForKF = pKF->mnId; + + const vector vNeighKFs = pKF->GetVectorCovisibleKeyFrames(); + for(int i=0, iend=vNeighKFs.size(); imnBALocalForKF = pKF->mnId; + if(!pKFi->isBad()) + lLocalKeyFrames.push_back(pKFi); + } + + // Local MapPoints seen in Local KeyFrames + list lLocalMapPoints; + for(list::iterator lit=lLocalKeyFrames.begin() , lend=lLocalKeyFrames.end(); lit!=lend; lit++) + { + vector vpMPs = (*lit)->GetMapPointMatches(); + for(vector::iterator vit=vpMPs.begin(), vend=vpMPs.end(); vit!=vend; vit++) + { + MapPoint* pMP = *vit; + if(pMP) + if(!pMP->isBad()) + if(pMP->mnBALocalForKF!=pKF->mnId) + { + lLocalMapPoints.push_back(pMP); + pMP->mnBALocalForKF=pKF->mnId; + } + } + } + + // Fixed Keyframes. Keyframes that see Local MapPoints but that are not Local Keyframes + list lFixedCameras; + for(list::iterator lit=lLocalMapPoints.begin(), lend=lLocalMapPoints.end(); lit!=lend; lit++) + { + map observations = (*lit)->GetObservations(); + for(map::iterator mit=observations.begin(), mend=observations.end(); mit!=mend; mit++) + { + KeyFrame* pKFi = mit->first; + + if(pKFi->mnBALocalForKF!=pKF->mnId && pKFi->mnBAFixedForKF!=pKF->mnId) + { + pKFi->mnBAFixedForKF=pKF->mnId; + if(!pKFi->isBad()) + lFixedCameras.push_back(pKFi); + } + } + } + + // Setup optimizer + g2o::SparseOptimizer optimizer; + g2o::BlockSolver_6_3::LinearSolverType * linearSolver; + + linearSolver = new g2o::LinearSolverEigen(); + + g2o::BlockSolver_6_3 * solver_ptr = new g2o::BlockSolver_6_3(linearSolver); + + g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr); + optimizer.setAlgorithm(solver); + + if(pbStopFlag) + optimizer.setForceStopFlag(pbStopFlag); + + unsigned long maxKFid = 0; + + // Set Local KeyFrame vertices + for(list::iterator lit=lLocalKeyFrames.begin(), lend=lLocalKeyFrames.end(); lit!=lend; lit++) + { + KeyFrame* pKFi = *lit; + g2o::VertexSE3Expmap * vSE3 = new g2o::VertexSE3Expmap(); + vSE3->setEstimate(Converter::toSE3Quat(pKFi->GetPose())); + vSE3->setId(pKFi->mnId); + vSE3->setFixed(pKFi->mnId==0); + optimizer.addVertex(vSE3); + if(pKFi->mnId>maxKFid) + maxKFid=pKFi->mnId; + } + + // Set Fixed KeyFrame vertices + for(list::iterator lit=lFixedCameras.begin(), lend=lFixedCameras.end(); lit!=lend; lit++) + { + KeyFrame* pKFi = *lit; + g2o::VertexSE3Expmap * vSE3 = new g2o::VertexSE3Expmap(); + vSE3->setEstimate(Converter::toSE3Quat(pKFi->GetPose())); + vSE3->setId(pKFi->mnId); + vSE3->setFixed(true); + optimizer.addVertex(vSE3); + if(pKFi->mnId>maxKFid) + maxKFid=pKFi->mnId; + } + + // Set MapPoint vertices + const int nExpectedSize = (lLocalKeyFrames.size()+lFixedCameras.size())*lLocalMapPoints.size(); + + vector vpEdgesMono; + vpEdgesMono.reserve(nExpectedSize); + + vector vpEdgeKFMono; + vpEdgeKFMono.reserve(nExpectedSize); + + vector vpMapPointEdgeMono; + vpMapPointEdgeMono.reserve(nExpectedSize); + + vector vpEdgesStereo; + vpEdgesStereo.reserve(nExpectedSize); + + vector vpEdgeKFStereo; + vpEdgeKFStereo.reserve(nExpectedSize); + + vector vpMapPointEdgeStereo; + vpMapPointEdgeStereo.reserve(nExpectedSize); + + const float thHuberMono = sqrt(5.991); + const float thHuberStereo = sqrt(7.815); + + for(list::iterator lit=lLocalMapPoints.begin(), lend=lLocalMapPoints.end(); lit!=lend; lit++) + { + MapPoint* pMP = *lit; + g2o::VertexSBAPointXYZ* vPoint = new g2o::VertexSBAPointXYZ(); + vPoint->setEstimate(Converter::toVector3d(pMP->GetWorldPos())); + int id = pMP->mnId+maxKFid+1; + vPoint->setId(id); + vPoint->setMarginalized(true); + optimizer.addVertex(vPoint); + + const map observations = pMP->GetObservations(); + + //Set edges + for(map::const_iterator mit=observations.begin(), mend=observations.end(); mit!=mend; mit++) + { + KeyFrame* pKFi = mit->first; + + if(!pKFi->isBad()) + { + const cv::KeyPoint &kpUn = pKFi->mvKeysUn[mit->second]; + + // Monocular observation + if(pKFi->mvuRight[mit->second]<0) + { + Eigen::Matrix obs; + obs << kpUn.pt.x, kpUn.pt.y; + + g2o::EdgeSE3ProjectXYZ* e = new g2o::EdgeSE3ProjectXYZ(); + + e->setVertex(0, dynamic_cast(optimizer.vertex(id))); + e->setVertex(1, dynamic_cast(optimizer.vertex(pKFi->mnId))); + e->setMeasurement(obs); + const float &invSigma2 = pKFi->mvInvLevelSigma2[kpUn.octave]; + e->setInformation(Eigen::Matrix2d::Identity()*invSigma2); + + g2o::RobustKernelHuber* rk = new g2o::RobustKernelHuber; + e->setRobustKernel(rk); + rk->setDelta(thHuberMono); + + e->fx = pKFi->fx; + e->fy = pKFi->fy; + e->cx = pKFi->cx; + e->cy = pKFi->cy; + + optimizer.addEdge(e); + vpEdgesMono.push_back(e); + vpEdgeKFMono.push_back(pKFi); + vpMapPointEdgeMono.push_back(pMP); + } + else // Stereo observation + { + Eigen::Matrix obs; + const float kp_ur = pKFi->mvuRight[mit->second]; + obs << kpUn.pt.x, kpUn.pt.y, kp_ur; + + g2o::EdgeStereoSE3ProjectXYZ* e = new g2o::EdgeStereoSE3ProjectXYZ(); + + e->setVertex(0, dynamic_cast(optimizer.vertex(id))); + e->setVertex(1, dynamic_cast(optimizer.vertex(pKFi->mnId))); + e->setMeasurement(obs); + const float &invSigma2 = pKFi->mvInvLevelSigma2[kpUn.octave]; + Eigen::Matrix3d Info = Eigen::Matrix3d::Identity()*invSigma2; + e->setInformation(Info); + + g2o::RobustKernelHuber* rk = new g2o::RobustKernelHuber; + e->setRobustKernel(rk); + rk->setDelta(thHuberStereo); + + e->fx = pKFi->fx; + e->fy = pKFi->fy; + e->cx = pKFi->cx; + e->cy = pKFi->cy; + e->bf = pKFi->mbf; + + optimizer.addEdge(e); + vpEdgesStereo.push_back(e); + vpEdgeKFStereo.push_back(pKFi); + vpMapPointEdgeStereo.push_back(pMP); + } + } + } + } + + if(pbStopFlag) + if(*pbStopFlag) + return; + + optimizer.initializeOptimization(); + optimizer.optimize(5); + + bool bDoMore= true; + + if(pbStopFlag) + if(*pbStopFlag) + bDoMore = false; + + if(bDoMore) + { + + // Check inlier observations + for(size_t i=0, iend=vpEdgesMono.size(); iisBad()) + continue; + + if(e->chi2()>5.991 || !e->isDepthPositive()) + { + e->setLevel(1); + } + + e->setRobustKernel(0); + } + + for(size_t i=0, iend=vpEdgesStereo.size(); iisBad()) + continue; + + if(e->chi2()>7.815 || !e->isDepthPositive()) + { + e->setLevel(1); + } + + e->setRobustKernel(0); + } + + // Optimize again without the outliers + + optimizer.initializeOptimization(0); + optimizer.optimize(10); + + } + + vector > vToErase; + vToErase.reserve(vpEdgesMono.size()+vpEdgesStereo.size()); + + // Check inlier observations + for(size_t i=0, iend=vpEdgesMono.size(); iisBad()) + continue; + + if(e->chi2()>5.991 || !e->isDepthPositive()) + { + KeyFrame* pKFi = vpEdgeKFMono[i]; + vToErase.push_back(make_pair(pKFi,pMP)); + } + } + + for(size_t i=0, iend=vpEdgesStereo.size(); iisBad()) + continue; + + if(e->chi2()>7.815 || !e->isDepthPositive()) + { + KeyFrame* pKFi = vpEdgeKFStereo[i]; + vToErase.push_back(make_pair(pKFi,pMP)); + } + } + + // Get Map Mutex + unique_lock lock(pMap->mMutexMapUpdate); + + if(!vToErase.empty()) + { + for(size_t i=0;iEraseMapPointMatch(pMPi); + pMPi->EraseObservation(pKFi); + } + } + + // Recover optimized data + + //Keyframes + for(list::iterator lit=lLocalKeyFrames.begin(), lend=lLocalKeyFrames.end(); lit!=lend; lit++) + { + KeyFrame* pKF = *lit; + g2o::VertexSE3Expmap* vSE3 = static_cast(optimizer.vertex(pKF->mnId)); + g2o::SE3Quat SE3quat = vSE3->estimate(); + pKF->SetPose(Converter::toCvMat(SE3quat)); + } + + //Points + for(list::iterator lit=lLocalMapPoints.begin(), lend=lLocalMapPoints.end(); lit!=lend; lit++) + { + MapPoint* pMP = *lit; + g2o::VertexSBAPointXYZ* vPoint = static_cast(optimizer.vertex(pMP->mnId+maxKFid+1)); + pMP->SetWorldPos(Converter::toCvMat(vPoint->estimate())); + pMP->UpdateNormalAndDepth(); + } +} + + +void Optimizer::OptimizeEssentialGraph(Map* pMap, KeyFrame* pLoopKF, KeyFrame* pCurKF, + const LoopClosing::KeyFrameAndPose &NonCorrectedSim3, + const LoopClosing::KeyFrameAndPose &CorrectedSim3, + const map > &LoopConnections, const bool &bFixScale) +{ + // Setup optimizer + g2o::SparseOptimizer optimizer; + optimizer.setVerbose(false); + g2o::BlockSolver_7_3::LinearSolverType * linearSolver = + new g2o::LinearSolverEigen(); + g2o::BlockSolver_7_3 * solver_ptr= new g2o::BlockSolver_7_3(linearSolver); + g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr); + + solver->setUserLambdaInit(1e-16); + optimizer.setAlgorithm(solver); + + const vector vpKFs = pMap->GetAllKeyFrames(); + const vector vpMPs = pMap->GetAllMapPoints(); + + const unsigned int nMaxKFid = pMap->GetMaxKFid(); + + vector > vScw(nMaxKFid+1); + vector > vCorrectedSwc(nMaxKFid+1); + vector vpVertices(nMaxKFid+1); + + const int minFeat = 100; + + // Set KeyFrame vertices + for(size_t i=0, iend=vpKFs.size(); iisBad()) + continue; + g2o::VertexSim3Expmap* VSim3 = new g2o::VertexSim3Expmap(); + + const int nIDi = pKF->mnId; + + LoopClosing::KeyFrameAndPose::const_iterator it = CorrectedSim3.find(pKF); + + if(it!=CorrectedSim3.end()) + { + vScw[nIDi] = it->second; + VSim3->setEstimate(it->second); + } + else + { + Eigen::Matrix Rcw = Converter::toMatrix3d(pKF->GetRotation()); + Eigen::Matrix tcw = Converter::toVector3d(pKF->GetTranslation()); + g2o::Sim3 Siw(Rcw,tcw,1.0); + vScw[nIDi] = Siw; + VSim3->setEstimate(Siw); + } + + if(pKF==pLoopKF) + VSim3->setFixed(true); + + VSim3->setId(nIDi); + VSim3->setMarginalized(false); + VSim3->_fix_scale = bFixScale; + + optimizer.addVertex(VSim3); + + vpVertices[nIDi]=VSim3; + } + + + set > sInsertedEdges; + + const Eigen::Matrix matLambda = Eigen::Matrix::Identity(); + + // Set Loop edges + for(map >::const_iterator mit = LoopConnections.begin(), mend=LoopConnections.end(); mit!=mend; mit++) + { + KeyFrame* pKF = mit->first; + const long unsigned int nIDi = pKF->mnId; + const set &spConnections = mit->second; + const g2o::Sim3 Siw = vScw[nIDi]; + const g2o::Sim3 Swi = Siw.inverse(); + + for(set::const_iterator sit=spConnections.begin(), send=spConnections.end(); sit!=send; sit++) + { + const long unsigned int nIDj = (*sit)->mnId; + if((nIDi!=pCurKF->mnId || nIDj!=pLoopKF->mnId) && pKF->GetWeight(*sit)setVertex(1, dynamic_cast(optimizer.vertex(nIDj))); + e->setVertex(0, dynamic_cast(optimizer.vertex(nIDi))); + e->setMeasurement(Sji); + + e->information() = matLambda; + + optimizer.addEdge(e); + + sInsertedEdges.insert(make_pair(min(nIDi,nIDj),max(nIDi,nIDj))); + } + } + + // Set normal edges + for(size_t i=0, iend=vpKFs.size(); imnId; + + g2o::Sim3 Swi; + + LoopClosing::KeyFrameAndPose::const_iterator iti = NonCorrectedSim3.find(pKF); + + if(iti!=NonCorrectedSim3.end()) + Swi = (iti->second).inverse(); + else + Swi = vScw[nIDi].inverse(); + + KeyFrame* pParentKF = pKF->GetParent(); + + // Spanning tree edge + if(pParentKF) + { + int nIDj = pParentKF->mnId; + + g2o::Sim3 Sjw; + + LoopClosing::KeyFrameAndPose::const_iterator itj = NonCorrectedSim3.find(pParentKF); + + if(itj!=NonCorrectedSim3.end()) + Sjw = itj->second; + else + Sjw = vScw[nIDj]; + + g2o::Sim3 Sji = Sjw * Swi; + + g2o::EdgeSim3* e = new g2o::EdgeSim3(); + e->setVertex(1, dynamic_cast(optimizer.vertex(nIDj))); + e->setVertex(0, dynamic_cast(optimizer.vertex(nIDi))); + e->setMeasurement(Sji); + + e->information() = matLambda; + optimizer.addEdge(e); + } + + // Loop edges + const set sLoopEdges = pKF->GetLoopEdges(); + for(set::const_iterator sit=sLoopEdges.begin(), send=sLoopEdges.end(); sit!=send; sit++) + { + KeyFrame* pLKF = *sit; + if(pLKF->mnIdmnId) + { + g2o::Sim3 Slw; + + LoopClosing::KeyFrameAndPose::const_iterator itl = NonCorrectedSim3.find(pLKF); + + if(itl!=NonCorrectedSim3.end()) + Slw = itl->second; + else + Slw = vScw[pLKF->mnId]; + + g2o::Sim3 Sli = Slw * Swi; + g2o::EdgeSim3* el = new g2o::EdgeSim3(); + el->setVertex(1, dynamic_cast(optimizer.vertex(pLKF->mnId))); + el->setVertex(0, dynamic_cast(optimizer.vertex(nIDi))); + el->setMeasurement(Sli); + el->information() = matLambda; + optimizer.addEdge(el); + } + } + + // Covisibility graph edges + const vector vpConnectedKFs = pKF->GetCovisiblesByWeight(minFeat); + for(vector::const_iterator vit=vpConnectedKFs.begin(); vit!=vpConnectedKFs.end(); vit++) + { + KeyFrame* pKFn = *vit; + if(pKFn && pKFn!=pParentKF && !pKF->hasChild(pKFn) && !sLoopEdges.count(pKFn)) + { + if(!pKFn->isBad() && pKFn->mnIdmnId) + { + if(sInsertedEdges.count(make_pair(min(pKF->mnId,pKFn->mnId),max(pKF->mnId,pKFn->mnId)))) + continue; + + g2o::Sim3 Snw; + + LoopClosing::KeyFrameAndPose::const_iterator itn = NonCorrectedSim3.find(pKFn); + + if(itn!=NonCorrectedSim3.end()) + Snw = itn->second; + else + Snw = vScw[pKFn->mnId]; + + g2o::Sim3 Sni = Snw * Swi; + + g2o::EdgeSim3* en = new g2o::EdgeSim3(); + en->setVertex(1, dynamic_cast(optimizer.vertex(pKFn->mnId))); + en->setVertex(0, dynamic_cast(optimizer.vertex(nIDi))); + en->setMeasurement(Sni); + en->information() = matLambda; + optimizer.addEdge(en); + } + } + } + } + + // Optimize! + optimizer.initializeOptimization(); + optimizer.optimize(20); + + unique_lock lock(pMap->mMutexMapUpdate); + + // SE3 Pose Recovering. Sim3:[sR t;0 1] -> SE3:[R t/s;0 1] + for(size_t i=0;imnId; + + g2o::VertexSim3Expmap* VSim3 = static_cast(optimizer.vertex(nIDi)); + g2o::Sim3 CorrectedSiw = VSim3->estimate(); + vCorrectedSwc[nIDi]=CorrectedSiw.inverse(); + Eigen::Matrix3d eigR = CorrectedSiw.rotation().toRotationMatrix(); + Eigen::Vector3d eigt = CorrectedSiw.translation(); + double s = CorrectedSiw.scale(); + + eigt *=(1./s); //[R t/s;0 1] + + cv::Mat Tiw = Converter::toCvSE3(eigR,eigt); + + pKFi->SetPose(Tiw); + } + + // Correct points. Transform to "non-optimized" reference keyframe pose and transform back with optimized pose + for(size_t i=0, iend=vpMPs.size(); iisBad()) + continue; + + int nIDr; + if(pMP->mnCorrectedByKF==pCurKF->mnId) + { + nIDr = pMP->mnCorrectedReference; + } + else + { + KeyFrame* pRefKF = pMP->GetReferenceKeyFrame(); + nIDr = pRefKF->mnId; + } + + + g2o::Sim3 Srw = vScw[nIDr]; + g2o::Sim3 correctedSwr = vCorrectedSwc[nIDr]; + + cv::Mat P3Dw = pMP->GetWorldPos(); + Eigen::Matrix eigP3Dw = Converter::toVector3d(P3Dw); + Eigen::Matrix eigCorrectedP3Dw = correctedSwr.map(Srw.map(eigP3Dw)); + + cv::Mat cvCorrectedP3Dw = Converter::toCvMat(eigCorrectedP3Dw); + pMP->SetWorldPos(cvCorrectedP3Dw); + + pMP->UpdateNormalAndDepth(); + } +} + +int Optimizer::OptimizeSim3(KeyFrame *pKF1, KeyFrame *pKF2, vector &vpMatches1, g2o::Sim3 &g2oS12, const float th2, const bool bFixScale) +{ + g2o::SparseOptimizer optimizer; + g2o::BlockSolverX::LinearSolverType * linearSolver; + + linearSolver = new g2o::LinearSolverDense(); + + g2o::BlockSolverX * solver_ptr = new g2o::BlockSolverX(linearSolver); + + g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr); + optimizer.setAlgorithm(solver); + + // Calibration + const cv::Mat &K1 = pKF1->mK; + const cv::Mat &K2 = pKF2->mK; + + // Camera poses + const cv::Mat R1w = pKF1->GetRotation(); + const cv::Mat t1w = pKF1->GetTranslation(); + const cv::Mat R2w = pKF2->GetRotation(); + const cv::Mat t2w = pKF2->GetTranslation(); + + // Set Sim3 vertex + g2o::VertexSim3Expmap * vSim3 = new g2o::VertexSim3Expmap(); + vSim3->_fix_scale=bFixScale; + vSim3->setEstimate(g2oS12); + vSim3->setId(0); + vSim3->setFixed(false); + vSim3->_principle_point1[0] = K1.at(0,2); + vSim3->_principle_point1[1] = K1.at(1,2); + vSim3->_focal_length1[0] = K1.at(0,0); + vSim3->_focal_length1[1] = K1.at(1,1); + vSim3->_principle_point2[0] = K2.at(0,2); + vSim3->_principle_point2[1] = K2.at(1,2); + vSim3->_focal_length2[0] = K2.at(0,0); + vSim3->_focal_length2[1] = K2.at(1,1); + optimizer.addVertex(vSim3); + + // Set MapPoint vertices + const int N = vpMatches1.size(); + const vector vpMapPoints1 = pKF1->GetMapPointMatches(); + vector vpEdges12; + vector vpEdges21; + vector vnIndexEdge; + + vnIndexEdge.reserve(2*N); + vpEdges12.reserve(2*N); + vpEdges21.reserve(2*N); + + const float deltaHuber = sqrt(th2); + + int nCorrespondences = 0; + + for(int i=0; iGetIndexInKeyFrame(pKF2); + + if(pMP1 && pMP2) + { + if(!pMP1->isBad() && !pMP2->isBad() && i2>=0) + { + g2o::VertexSBAPointXYZ* vPoint1 = new g2o::VertexSBAPointXYZ(); + cv::Mat P3D1w = pMP1->GetWorldPos(); + cv::Mat P3D1c = R1w*P3D1w + t1w; + vPoint1->setEstimate(Converter::toVector3d(P3D1c)); + vPoint1->setId(id1); + vPoint1->setFixed(true); + optimizer.addVertex(vPoint1); + + g2o::VertexSBAPointXYZ* vPoint2 = new g2o::VertexSBAPointXYZ(); + cv::Mat P3D2w = pMP2->GetWorldPos(); + cv::Mat P3D2c = R2w*P3D2w + t2w; + vPoint2->setEstimate(Converter::toVector3d(P3D2c)); + vPoint2->setId(id2); + vPoint2->setFixed(true); + optimizer.addVertex(vPoint2); + } + else + continue; + } + else + continue; + + nCorrespondences++; + + // Set edge x1 = S12*X2 + Eigen::Matrix obs1; + const cv::KeyPoint &kpUn1 = pKF1->mvKeysUn[i]; + obs1 << kpUn1.pt.x, kpUn1.pt.y; + + g2o::EdgeSim3ProjectXYZ* e12 = new g2o::EdgeSim3ProjectXYZ(); + e12->setVertex(0, dynamic_cast(optimizer.vertex(id2))); + e12->setVertex(1, dynamic_cast(optimizer.vertex(0))); + e12->setMeasurement(obs1); + const float &invSigmaSquare1 = pKF1->mvInvLevelSigma2[kpUn1.octave]; + e12->setInformation(Eigen::Matrix2d::Identity()*invSigmaSquare1); + + g2o::RobustKernelHuber* rk1 = new g2o::RobustKernelHuber; + e12->setRobustKernel(rk1); + rk1->setDelta(deltaHuber); + optimizer.addEdge(e12); + + // Set edge x2 = S21*X1 + Eigen::Matrix obs2; + const cv::KeyPoint &kpUn2 = pKF2->mvKeysUn[i2]; + obs2 << kpUn2.pt.x, kpUn2.pt.y; + + g2o::EdgeInverseSim3ProjectXYZ* e21 = new g2o::EdgeInverseSim3ProjectXYZ(); + + e21->setVertex(0, dynamic_cast(optimizer.vertex(id1))); + e21->setVertex(1, dynamic_cast(optimizer.vertex(0))); + e21->setMeasurement(obs2); + float invSigmaSquare2 = pKF2->mvInvLevelSigma2[kpUn2.octave]; + e21->setInformation(Eigen::Matrix2d::Identity()*invSigmaSquare2); + + g2o::RobustKernelHuber* rk2 = new g2o::RobustKernelHuber; + e21->setRobustKernel(rk2); + rk2->setDelta(deltaHuber); + optimizer.addEdge(e21); + + vpEdges12.push_back(e12); + vpEdges21.push_back(e21); + vnIndexEdge.push_back(i); + } + + // Optimize! + optimizer.initializeOptimization(); + optimizer.optimize(5); + + // Check inliers + int nBad=0; + for(size_t i=0; ichi2()>th2 || e21->chi2()>th2) + { + size_t idx = vnIndexEdge[i]; + vpMatches1[idx]=static_cast(NULL); + optimizer.removeEdge(e12); + optimizer.removeEdge(e21); + vpEdges12[i]=static_cast(NULL); + vpEdges21[i]=static_cast(NULL); + nBad++; + } + } + + int nMoreIterations; + if(nBad>0) + nMoreIterations=10; + else + nMoreIterations=5; + + if(nCorrespondences-nBad<10) + return 0; + + // Optimize again only with inliers + + optimizer.initializeOptimization(); + optimizer.optimize(nMoreIterations); + + int nIn = 0; + for(size_t i=0; ichi2()>th2 || e21->chi2()>th2) + { + size_t idx = vnIndexEdge[i]; + vpMatches1[idx]=static_cast(NULL); + } + else + nIn++; + } + + // Recover optimized Sim3 + g2o::VertexSim3Expmap* vSim3_recov = static_cast(optimizer.vertex(0)); + g2oS12= vSim3_recov->estimate(); + + return nIn; +} + + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/PnPsolver.cc b/for_orb_slam2/src/PnPsolver.cc new file mode 100644 index 00000000000..af3511b1656 --- /dev/null +++ b/for_orb_slam2/src/PnPsolver.cc @@ -0,0 +1,1022 @@ +/** +* This file is part of ORB-SLAM2. +* This file is a modified version of EPnP , see FreeBSD license below. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +/** +* Copyright (c) 2009, V. Lepetit, EPFL +* All rights reserved. +* +* Redistribution and use in source and binary forms, with or without +* modification, are permitted provided that the following conditions are met: +* +* 1. Redistributions of source code must retain the above copyright notice, this +* list of conditions and the following disclaimer. +* 2. Redistributions in binary form must reproduce the above copyright notice, +* this list of conditions and the following disclaimer in the documentation +* and/or other materials provided with the distribution. +* +* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR +* ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +* +* The views and conclusions contained in the software and documentation are those +* of the authors and should not be interpreted as representing official policies, +* either expressed or implied, of the FreeBSD Project +*/ + +#include + +#include "PnPsolver.h" + +#include +#include +#include +#include "Thirdparty/DBoW2/DUtils/Random.h" +#include + +using namespace std; + +namespace ORB_SLAM2 +{ + + +PnPsolver::PnPsolver(const Frame &F, const vector &vpMapPointMatches): + pws(0), us(0), alphas(0), pcs(0), maximum_number_of_correspondences(0), number_of_correspondences(0), mnInliersi(0), + mnIterations(0), mnBestInliers(0), N(0) +{ + mvpMapPointMatches = vpMapPointMatches; + mvP2D.reserve(F.mvpMapPoints.size()); + mvSigma2.reserve(F.mvpMapPoints.size()); + mvP3Dw.reserve(F.mvpMapPoints.size()); + mvKeyPointIndices.reserve(F.mvpMapPoints.size()); + mvAllIndices.reserve(F.mvpMapPoints.size()); + + int idx=0; + for(size_t i=0, iend=vpMapPointMatches.size(); iisBad()) + { + const cv::KeyPoint &kp = F.mvKeysUn[i]; + + mvP2D.push_back(kp.pt); + mvSigma2.push_back(F.mvLevelSigma2[kp.octave]); + + cv::Mat Pos = pMP->GetWorldPos(); + mvP3Dw.push_back(cv::Point3f(Pos.at(0),Pos.at(1), Pos.at(2))); + + mvKeyPointIndices.push_back(i); + mvAllIndices.push_back(idx); + + idx++; + } + } + } + + // Set camera calibration parameters + fu = F.fx; + fv = F.fy; + uc = F.cx; + vc = F.cy; + + SetRansacParameters(); +} + +PnPsolver::~PnPsolver() +{ + delete [] pws; + delete [] us; + delete [] alphas; + delete [] pcs; +} + + +void PnPsolver::SetRansacParameters(double probability, int minInliers, int maxIterations, int minSet, float epsilon, float th2) +{ + mRansacProb = probability; + mRansacMinInliers = minInliers; + mRansacMaxIts = maxIterations; + mRansacEpsilon = epsilon; + mRansacMinSet = minSet; + + N = mvP2D.size(); // number of correspondences + + mvbInliersi.resize(N); + + // Adjust Parameters according to number of correspondences + int nMinInliers = N*mRansacEpsilon; + if(nMinInliers &vbInliers, int &nInliers) +{ + bool bFlag; + return iterate(mRansacMaxIts,bFlag,vbInliers,nInliers); +} + +cv::Mat PnPsolver::iterate(int nIterations, bool &bNoMore, vector &vbInliers, int &nInliers) +{ + bNoMore = false; + vbInliers.clear(); + nInliers=0; + + set_maximum_number_of_correspondences(mRansacMinSet); + + if(N vAvailableIndices; + + int nCurrentIterations = 0; + while(mnIterations=mRansacMinInliers) + { + // If it is the best solution so far, save it + if(mnInliersi>mnBestInliers) + { + mvbBestInliers = mvbInliersi; + mnBestInliers = mnInliersi; + + cv::Mat Rcw(3,3,CV_64F,mRi); + cv::Mat tcw(3,1,CV_64F,mti); + Rcw.convertTo(Rcw,CV_32F); + tcw.convertTo(tcw,CV_32F); + mBestTcw = cv::Mat::eye(4,4,CV_32F); + Rcw.copyTo(mBestTcw.rowRange(0,3).colRange(0,3)); + tcw.copyTo(mBestTcw.rowRange(0,3).col(3)); + } + + if(Refine()) + { + nInliers = mnRefinedInliers; + vbInliers = vector(mvpMapPointMatches.size(),false); + for(int i=0; i=mRansacMaxIts) + { + bNoMore=true; + if(mnBestInliers>=mRansacMinInliers) + { + nInliers=mnBestInliers; + vbInliers = vector(mvpMapPointMatches.size(),false); + for(int i=0; i vIndices; + vIndices.reserve(mvbBestInliers.size()); + + for(size_t i=0; imRansacMinInliers) + { + cv::Mat Rcw(3,3,CV_64F,mRi); + cv::Mat tcw(3,1,CV_64F,mti); + Rcw.convertTo(Rcw,CV_32F); + tcw.convertTo(tcw,CV_32F); + mRefinedTcw = cv::Mat::eye(4,4,CV_32F); + Rcw.copyTo(mRefinedTcw.rowRange(0,3).colRange(0,3)); + tcw.copyTo(mRefinedTcw.rowRange(0,3).col(3)); + return true; + } + + return false; +} + + +void PnPsolver::CheckInliers() +{ + mnInliersi=0; + + for(int i=0; idata.db[3 * i + j] = pws[3 * i + j] - cws[0][j]; + + cvMulTransposed(PW0, &PW0tPW0, 1); + cvSVD(&PW0tPW0, &DC, &UCt, 0, CV_SVD_MODIFY_A | CV_SVD_U_T); + + cvReleaseMat(&PW0); + + for(int i = 1; i < 4; i++) { + double k = sqrt(dc[i - 1] / number_of_correspondences); + for(int j = 0; j < 3; j++) + cws[i][j] = cws[0][j] + k * uct[3 * (i - 1) + j]; + } +} + +void PnPsolver::compute_barycentric_coordinates(void) +{ + double cc[3 * 3], cc_inv[3 * 3]; + CvMat CC = cvMat(3, 3, CV_64F, cc); + CvMat CC_inv = cvMat(3, 3, CV_64F, cc_inv); + + for(int i = 0; i < 3; i++) + for(int j = 1; j < 4; j++) + cc[3 * i + j - 1] = cws[j][i] - cws[0][i]; + + cvInvert(&CC, &CC_inv, CV_SVD); + double * ci = cc_inv; + for(int i = 0; i < number_of_correspondences; i++) { + double * pi = pws + 3 * i; + double * a = alphas + 4 * i; + + for(int j = 0; j < 3; j++) + a[1 + j] = + ci[3 * j ] * (pi[0] - cws[0][0]) + + ci[3 * j + 1] * (pi[1] - cws[0][1]) + + ci[3 * j + 2] * (pi[2] - cws[0][2]); + a[0] = 1.0f - a[1] - a[2] - a[3]; + } +} + +void PnPsolver::fill_M(CvMat * M, + const int row, const double * as, const double u, const double v) +{ + double * M1 = M->data.db + row * 12; + double * M2 = M1 + 12; + + for(int i = 0; i < 4; i++) { + M1[3 * i ] = as[i] * fu; + M1[3 * i + 1] = 0.0; + M1[3 * i + 2] = as[i] * (uc - u); + + M2[3 * i ] = 0.0; + M2[3 * i + 1] = as[i] * fv; + M2[3 * i + 2] = as[i] * (vc - v); + } +} + +void PnPsolver::compute_ccs(const double * betas, const double * ut) +{ + for(int i = 0; i < 4; i++) + ccs[i][0] = ccs[i][1] = ccs[i][2] = 0.0f; + + for(int i = 0; i < 4; i++) { + const double * v = ut + 12 * (11 - i); + for(int j = 0; j < 4; j++) + for(int k = 0; k < 3; k++) + ccs[j][k] += betas[i] * v[3 * j + k]; + } +} + +void PnPsolver::compute_pcs(void) +{ + for(int i = 0; i < number_of_correspondences; i++) { + double * a = alphas + 4 * i; + double * pc = pcs + 3 * i; + + for(int j = 0; j < 3; j++) + pc[j] = a[0] * ccs[0][j] + a[1] * ccs[1][j] + a[2] * ccs[2][j] + a[3] * ccs[3][j]; + } +} + +double PnPsolver::compute_pose(double R[3][3], double t[3]) +{ + choose_control_points(); + compute_barycentric_coordinates(); + + CvMat * M = cvCreateMat(2 * number_of_correspondences, 12, CV_64F); + + for(int i = 0; i < number_of_correspondences; i++) + fill_M(M, 2 * i, alphas + 4 * i, us[2 * i], us[2 * i + 1]); + + double mtm[12 * 12], d[12], ut[12 * 12]; + CvMat MtM = cvMat(12, 12, CV_64F, mtm); + CvMat D = cvMat(12, 1, CV_64F, d); + CvMat Ut = cvMat(12, 12, CV_64F, ut); + + cvMulTransposed(M, &MtM, 1); + cvSVD(&MtM, &D, &Ut, 0, CV_SVD_MODIFY_A | CV_SVD_U_T); + cvReleaseMat(&M); + + double l_6x10[6 * 10], rho[6]; + CvMat L_6x10 = cvMat(6, 10, CV_64F, l_6x10); + CvMat Rho = cvMat(6, 1, CV_64F, rho); + + compute_L_6x10(ut, l_6x10); + compute_rho(rho); + + double Betas[4][4], rep_errors[4]; + double Rs[4][3][3], ts[4][3]; + + find_betas_approx_1(&L_6x10, &Rho, Betas[1]); + gauss_newton(&L_6x10, &Rho, Betas[1]); + rep_errors[1] = compute_R_and_t(ut, Betas[1], Rs[1], ts[1]); + + find_betas_approx_2(&L_6x10, &Rho, Betas[2]); + gauss_newton(&L_6x10, &Rho, Betas[2]); + rep_errors[2] = compute_R_and_t(ut, Betas[2], Rs[2], ts[2]); + + find_betas_approx_3(&L_6x10, &Rho, Betas[3]); + gauss_newton(&L_6x10, &Rho, Betas[3]); + rep_errors[3] = compute_R_and_t(ut, Betas[3], Rs[3], ts[3]); + + int N = 1; + if (rep_errors[2] < rep_errors[1]) N = 2; + if (rep_errors[3] < rep_errors[N]) N = 3; + + copy_R_and_t(Rs[N], ts[N], R, t); + + return rep_errors[N]; +} + +void PnPsolver::copy_R_and_t(const double R_src[3][3], const double t_src[3], + double R_dst[3][3], double t_dst[3]) +{ + for(int i = 0; i < 3; i++) { + for(int j = 0; j < 3; j++) + R_dst[i][j] = R_src[i][j]; + t_dst[i] = t_src[i]; + } +} + +double PnPsolver::dist2(const double * p1, const double * p2) +{ + return + (p1[0] - p2[0]) * (p1[0] - p2[0]) + + (p1[1] - p2[1]) * (p1[1] - p2[1]) + + (p1[2] - p2[2]) * (p1[2] - p2[2]); +} + +double PnPsolver::dot(const double * v1, const double * v2) +{ + return v1[0] * v2[0] + v1[1] * v2[1] + v1[2] * v2[2]; +} + +double PnPsolver::reprojection_error(const double R[3][3], const double t[3]) +{ + double sum2 = 0.0; + + for(int i = 0; i < number_of_correspondences; i++) { + double * pw = pws + 3 * i; + double Xc = dot(R[0], pw) + t[0]; + double Yc = dot(R[1], pw) + t[1]; + double inv_Zc = 1.0 / (dot(R[2], pw) + t[2]); + double ue = uc + fu * Xc * inv_Zc; + double ve = vc + fv * Yc * inv_Zc; + double u = us[2 * i], v = us[2 * i + 1]; + + sum2 += sqrt( (u - ue) * (u - ue) + (v - ve) * (v - ve) ); + } + + return sum2 / number_of_correspondences; +} + +void PnPsolver::estimate_R_and_t(double R[3][3], double t[3]) +{ + double pc0[3], pw0[3]; + + pc0[0] = pc0[1] = pc0[2] = 0.0; + pw0[0] = pw0[1] = pw0[2] = 0.0; + + for(int i = 0; i < number_of_correspondences; i++) { + const double * pc = pcs + 3 * i; + const double * pw = pws + 3 * i; + + for(int j = 0; j < 3; j++) { + pc0[j] += pc[j]; + pw0[j] += pw[j]; + } + } + for(int j = 0; j < 3; j++) { + pc0[j] /= number_of_correspondences; + pw0[j] /= number_of_correspondences; + } + + double abt[3 * 3], abt_d[3], abt_u[3 * 3], abt_v[3 * 3]; + CvMat ABt = cvMat(3, 3, CV_64F, abt); + CvMat ABt_D = cvMat(3, 1, CV_64F, abt_d); + CvMat ABt_U = cvMat(3, 3, CV_64F, abt_u); + CvMat ABt_V = cvMat(3, 3, CV_64F, abt_v); + + cvSetZero(&ABt); + for(int i = 0; i < number_of_correspondences; i++) { + double * pc = pcs + 3 * i; + double * pw = pws + 3 * i; + + for(int j = 0; j < 3; j++) { + abt[3 * j ] += (pc[j] - pc0[j]) * (pw[0] - pw0[0]); + abt[3 * j + 1] += (pc[j] - pc0[j]) * (pw[1] - pw0[1]); + abt[3 * j + 2] += (pc[j] - pc0[j]) * (pw[2] - pw0[2]); + } + } + + cvSVD(&ABt, &ABt_D, &ABt_U, &ABt_V, CV_SVD_MODIFY_A); + + for(int i = 0; i < 3; i++) + for(int j = 0; j < 3; j++) + R[i][j] = dot(abt_u + 3 * i, abt_v + 3 * j); + + const double det = + R[0][0] * R[1][1] * R[2][2] + R[0][1] * R[1][2] * R[2][0] + R[0][2] * R[1][0] * R[2][1] - + R[0][2] * R[1][1] * R[2][0] - R[0][1] * R[1][0] * R[2][2] - R[0][0] * R[1][2] * R[2][1]; + + if (det < 0) { + R[2][0] = -R[2][0]; + R[2][1] = -R[2][1]; + R[2][2] = -R[2][2]; + } + + t[0] = pc0[0] - dot(R[0], pw0); + t[1] = pc0[1] - dot(R[1], pw0); + t[2] = pc0[2] - dot(R[2], pw0); +} + +void PnPsolver::print_pose(const double R[3][3], const double t[3]) +{ + cout << R[0][0] << " " << R[0][1] << " " << R[0][2] << " " << t[0] << endl; + cout << R[1][0] << " " << R[1][1] << " " << R[1][2] << " " << t[1] << endl; + cout << R[2][0] << " " << R[2][1] << " " << R[2][2] << " " << t[2] << endl; +} + +void PnPsolver::solve_for_sign(void) +{ + if (pcs[2] < 0.0) { + for(int i = 0; i < 4; i++) + for(int j = 0; j < 3; j++) + ccs[i][j] = -ccs[i][j]; + + for(int i = 0; i < number_of_correspondences; i++) { + pcs[3 * i ] = -pcs[3 * i]; + pcs[3 * i + 1] = -pcs[3 * i + 1]; + pcs[3 * i + 2] = -pcs[3 * i + 2]; + } + } +} + +double PnPsolver::compute_R_and_t(const double * ut, const double * betas, + double R[3][3], double t[3]) +{ + compute_ccs(betas, ut); + compute_pcs(); + + solve_for_sign(); + + estimate_R_and_t(R, t); + + return reprojection_error(R, t); +} + +// betas10 = [B11 B12 B22 B13 B23 B33 B14 B24 B34 B44] +// betas_approx_1 = [B11 B12 B13 B14] + +void PnPsolver::find_betas_approx_1(const CvMat * L_6x10, const CvMat * Rho, + double * betas) +{ + double l_6x4[6 * 4], b4[4]; + CvMat L_6x4 = cvMat(6, 4, CV_64F, l_6x4); + CvMat B4 = cvMat(4, 1, CV_64F, b4); + + for(int i = 0; i < 6; i++) { + cvmSet(&L_6x4, i, 0, cvmGet(L_6x10, i, 0)); + cvmSet(&L_6x4, i, 1, cvmGet(L_6x10, i, 1)); + cvmSet(&L_6x4, i, 2, cvmGet(L_6x10, i, 3)); + cvmSet(&L_6x4, i, 3, cvmGet(L_6x10, i, 6)); + } + + cvSolve(&L_6x4, Rho, &B4, CV_SVD); + + if (b4[0] < 0) { + betas[0] = sqrt(-b4[0]); + betas[1] = -b4[1] / betas[0]; + betas[2] = -b4[2] / betas[0]; + betas[3] = -b4[3] / betas[0]; + } else { + betas[0] = sqrt(b4[0]); + betas[1] = b4[1] / betas[0]; + betas[2] = b4[2] / betas[0]; + betas[3] = b4[3] / betas[0]; + } +} + +// betas10 = [B11 B12 B22 B13 B23 B33 B14 B24 B34 B44] +// betas_approx_2 = [B11 B12 B22 ] + +void PnPsolver::find_betas_approx_2(const CvMat * L_6x10, const CvMat * Rho, + double * betas) +{ + double l_6x3[6 * 3], b3[3]; + CvMat L_6x3 = cvMat(6, 3, CV_64F, l_6x3); + CvMat B3 = cvMat(3, 1, CV_64F, b3); + + for(int i = 0; i < 6; i++) { + cvmSet(&L_6x3, i, 0, cvmGet(L_6x10, i, 0)); + cvmSet(&L_6x3, i, 1, cvmGet(L_6x10, i, 1)); + cvmSet(&L_6x3, i, 2, cvmGet(L_6x10, i, 2)); + } + + cvSolve(&L_6x3, Rho, &B3, CV_SVD); + + if (b3[0] < 0) { + betas[0] = sqrt(-b3[0]); + betas[1] = (b3[2] < 0) ? sqrt(-b3[2]) : 0.0; + } else { + betas[0] = sqrt(b3[0]); + betas[1] = (b3[2] > 0) ? sqrt(b3[2]) : 0.0; + } + + if (b3[1] < 0) betas[0] = -betas[0]; + + betas[2] = 0.0; + betas[3] = 0.0; +} + +// betas10 = [B11 B12 B22 B13 B23 B33 B14 B24 B34 B44] +// betas_approx_3 = [B11 B12 B22 B13 B23 ] + +void PnPsolver::find_betas_approx_3(const CvMat * L_6x10, const CvMat * Rho, + double * betas) +{ + double l_6x5[6 * 5], b5[5]; + CvMat L_6x5 = cvMat(6, 5, CV_64F, l_6x5); + CvMat B5 = cvMat(5, 1, CV_64F, b5); + + for(int i = 0; i < 6; i++) { + cvmSet(&L_6x5, i, 0, cvmGet(L_6x10, i, 0)); + cvmSet(&L_6x5, i, 1, cvmGet(L_6x10, i, 1)); + cvmSet(&L_6x5, i, 2, cvmGet(L_6x10, i, 2)); + cvmSet(&L_6x5, i, 3, cvmGet(L_6x10, i, 3)); + cvmSet(&L_6x5, i, 4, cvmGet(L_6x10, i, 4)); + } + + cvSolve(&L_6x5, Rho, &B5, CV_SVD); + + if (b5[0] < 0) { + betas[0] = sqrt(-b5[0]); + betas[1] = (b5[2] < 0) ? sqrt(-b5[2]) : 0.0; + } else { + betas[0] = sqrt(b5[0]); + betas[1] = (b5[2] > 0) ? sqrt(b5[2]) : 0.0; + } + if (b5[1] < 0) betas[0] = -betas[0]; + betas[2] = b5[3] / betas[0]; + betas[3] = 0.0; +} + +void PnPsolver::compute_L_6x10(const double * ut, double * l_6x10) +{ + const double * v[4]; + + v[0] = ut + 12 * 11; + v[1] = ut + 12 * 10; + v[2] = ut + 12 * 9; + v[3] = ut + 12 * 8; + + double dv[4][6][3]; + + for(int i = 0; i < 4; i++) { + int a = 0, b = 1; + for(int j = 0; j < 6; j++) { + dv[i][j][0] = v[i][3 * a ] - v[i][3 * b]; + dv[i][j][1] = v[i][3 * a + 1] - v[i][3 * b + 1]; + dv[i][j][2] = v[i][3 * a + 2] - v[i][3 * b + 2]; + + b++; + if (b > 3) { + a++; + b = a + 1; + } + } + } + + for(int i = 0; i < 6; i++) { + double * row = l_6x10 + 10 * i; + + row[0] = dot(dv[0][i], dv[0][i]); + row[1] = 2.0f * dot(dv[0][i], dv[1][i]); + row[2] = dot(dv[1][i], dv[1][i]); + row[3] = 2.0f * dot(dv[0][i], dv[2][i]); + row[4] = 2.0f * dot(dv[1][i], dv[2][i]); + row[5] = dot(dv[2][i], dv[2][i]); + row[6] = 2.0f * dot(dv[0][i], dv[3][i]); + row[7] = 2.0f * dot(dv[1][i], dv[3][i]); + row[8] = 2.0f * dot(dv[2][i], dv[3][i]); + row[9] = dot(dv[3][i], dv[3][i]); + } +} + +void PnPsolver::compute_rho(double * rho) +{ + rho[0] = dist2(cws[0], cws[1]); + rho[1] = dist2(cws[0], cws[2]); + rho[2] = dist2(cws[0], cws[3]); + rho[3] = dist2(cws[1], cws[2]); + rho[4] = dist2(cws[1], cws[3]); + rho[5] = dist2(cws[2], cws[3]); +} + +void PnPsolver::compute_A_and_b_gauss_newton(const double * l_6x10, const double * rho, + double betas[4], CvMat * A, CvMat * b) +{ + for(int i = 0; i < 6; i++) { + const double * rowL = l_6x10 + i * 10; + double * rowA = A->data.db + i * 4; + + rowA[0] = 2 * rowL[0] * betas[0] + rowL[1] * betas[1] + rowL[3] * betas[2] + rowL[6] * betas[3]; + rowA[1] = rowL[1] * betas[0] + 2 * rowL[2] * betas[1] + rowL[4] * betas[2] + rowL[7] * betas[3]; + rowA[2] = rowL[3] * betas[0] + rowL[4] * betas[1] + 2 * rowL[5] * betas[2] + rowL[8] * betas[3]; + rowA[3] = rowL[6] * betas[0] + rowL[7] * betas[1] + rowL[8] * betas[2] + 2 * rowL[9] * betas[3]; + + cvmSet(b, i, 0, rho[i] - + ( + rowL[0] * betas[0] * betas[0] + + rowL[1] * betas[0] * betas[1] + + rowL[2] * betas[1] * betas[1] + + rowL[3] * betas[0] * betas[2] + + rowL[4] * betas[1] * betas[2] + + rowL[5] * betas[2] * betas[2] + + rowL[6] * betas[0] * betas[3] + + rowL[7] * betas[1] * betas[3] + + rowL[8] * betas[2] * betas[3] + + rowL[9] * betas[3] * betas[3] + )); + } +} + +void PnPsolver::gauss_newton(const CvMat * L_6x10, const CvMat * Rho, + double betas[4]) +{ + const int iterations_number = 5; + + double a[6*4], b[6], x[4]; + CvMat A = cvMat(6, 4, CV_64F, a); + CvMat B = cvMat(6, 1, CV_64F, b); + CvMat X = cvMat(4, 1, CV_64F, x); + + for(int k = 0; k < iterations_number; k++) { + compute_A_and_b_gauss_newton(L_6x10->data.db, Rho->data.db, + betas, &A, &B); + qr_solve(&A, &B, &X); + + for(int i = 0; i < 4; i++) + betas[i] += x[i]; + } +} + +void PnPsolver::qr_solve(CvMat * A, CvMat * b, CvMat * X) +{ + static int max_nr = 0; + static double * A1, * A2; + + const int nr = A->rows; + const int nc = A->cols; + + if (max_nr != 0 && max_nr < nr) { + delete [] A1; + delete [] A2; + } + if (max_nr < nr) { + max_nr = nr; + A1 = new double[nr]; + A2 = new double[nr]; + } + + double * pA = A->data.db, * ppAkk = pA; + for(int k = 0; k < nc; k++) { + double * ppAik = ppAkk, eta = fabs(*ppAik); + for(int i = k + 1; i < nr; i++) { + double elt = fabs(*ppAik); + if (eta < elt) eta = elt; + ppAik += nc; + } + + if (eta == 0) { + A1[k] = A2[k] = 0.0; + cerr << "God damnit, A is singular, this shouldn't happen." << endl; + return; + } else { + double * ppAik = ppAkk, sum = 0.0, inv_eta = 1. / eta; + for(int i = k; i < nr; i++) { + *ppAik *= inv_eta; + sum += *ppAik * *ppAik; + ppAik += nc; + } + double sigma = sqrt(sum); + if (*ppAkk < 0) + sigma = -sigma; + *ppAkk += sigma; + A1[k] = sigma * *ppAkk; + A2[k] = -eta * sigma; + for(int j = k + 1; j < nc; j++) { + double * ppAik = ppAkk, sum = 0; + for(int i = k; i < nr; i++) { + sum += *ppAik * ppAik[j - k]; + ppAik += nc; + } + double tau = sum / A1[k]; + ppAik = ppAkk; + for(int i = k; i < nr; i++) { + ppAik[j - k] -= tau * *ppAik; + ppAik += nc; + } + } + } + ppAkk += nc + 1; + } + + // b <- Qt b + double * ppAjj = pA, * pb = b->data.db; + for(int j = 0; j < nc; j++) { + double * ppAij = ppAjj, tau = 0; + for(int i = j; i < nr; i++) { + tau += *ppAij * pb[i]; + ppAij += nc; + } + tau /= A1[j]; + ppAij = ppAjj; + for(int i = j; i < nr; i++) { + pb[i] -= tau * *ppAij; + ppAij += nc; + } + ppAjj += nc + 1; + } + + // X = R-1 b + double * pX = X->data.db; + pX[nc - 1] = pb[nc - 1] / A2[nc - 1]; + for(int i = nc - 2; i >= 0; i--) { + double * ppAij = pA + i * nc + (i + 1), sum = 0; + + for(int j = i + 1; j < nc; j++) { + sum += *ppAij * pX[j]; + ppAij++; + } + pX[i] = (pb[i] - sum) / A2[i]; + } +} + + + +void PnPsolver::relative_error(double & rot_err, double & transl_err, + const double Rtrue[3][3], const double ttrue[3], + const double Rest[3][3], const double test[3]) +{ + double qtrue[4], qest[4]; + + mat_to_quat(Rtrue, qtrue); + mat_to_quat(Rest, qest); + + double rot_err1 = sqrt((qtrue[0] - qest[0]) * (qtrue[0] - qest[0]) + + (qtrue[1] - qest[1]) * (qtrue[1] - qest[1]) + + (qtrue[2] - qest[2]) * (qtrue[2] - qest[2]) + + (qtrue[3] - qest[3]) * (qtrue[3] - qest[3]) ) / + sqrt(qtrue[0] * qtrue[0] + qtrue[1] * qtrue[1] + qtrue[2] * qtrue[2] + qtrue[3] * qtrue[3]); + + double rot_err2 = sqrt((qtrue[0] + qest[0]) * (qtrue[0] + qest[0]) + + (qtrue[1] + qest[1]) * (qtrue[1] + qest[1]) + + (qtrue[2] + qest[2]) * (qtrue[2] + qest[2]) + + (qtrue[3] + qest[3]) * (qtrue[3] + qest[3]) ) / + sqrt(qtrue[0] * qtrue[0] + qtrue[1] * qtrue[1] + qtrue[2] * qtrue[2] + qtrue[3] * qtrue[3]); + + rot_err = min(rot_err1, rot_err2); + + transl_err = + sqrt((ttrue[0] - test[0]) * (ttrue[0] - test[0]) + + (ttrue[1] - test[1]) * (ttrue[1] - test[1]) + + (ttrue[2] - test[2]) * (ttrue[2] - test[2])) / + sqrt(ttrue[0] * ttrue[0] + ttrue[1] * ttrue[1] + ttrue[2] * ttrue[2]); +} + +void PnPsolver::mat_to_quat(const double R[3][3], double q[4]) +{ + double tr = R[0][0] + R[1][1] + R[2][2]; + double n4; + + if (tr > 0.0f) { + q[0] = R[1][2] - R[2][1]; + q[1] = R[2][0] - R[0][2]; + q[2] = R[0][1] - R[1][0]; + q[3] = tr + 1.0f; + n4 = q[3]; + } else if ( (R[0][0] > R[1][1]) && (R[0][0] > R[2][2]) ) { + q[0] = 1.0f + R[0][0] - R[1][1] - R[2][2]; + q[1] = R[1][0] + R[0][1]; + q[2] = R[2][0] + R[0][2]; + q[3] = R[1][2] - R[2][1]; + n4 = q[0]; + } else if (R[1][1] > R[2][2]) { + q[0] = R[1][0] + R[0][1]; + q[1] = 1.0f + R[1][1] - R[0][0] - R[2][2]; + q[2] = R[2][1] + R[1][2]; + q[3] = R[2][0] - R[0][2]; + n4 = q[1]; + } else { + q[0] = R[2][0] + R[0][2]; + q[1] = R[2][1] + R[1][2]; + q[2] = 1.0f + R[2][2] - R[0][0] - R[1][1]; + q[3] = R[0][1] - R[1][0]; + n4 = q[2]; + } + double scale = 0.5f / double(sqrt(n4)); + + q[0] *= scale; + q[1] *= scale; + q[2] *= scale; + q[3] *= scale; +} + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/Sim3Solver.cc b/for_orb_slam2/src/Sim3Solver.cc new file mode 100644 index 00000000000..4ad44ff0de9 --- /dev/null +++ b/for_orb_slam2/src/Sim3Solver.cc @@ -0,0 +1,425 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + + +#include "Sim3Solver.h" + +#include +#include +#include + +#include "KeyFrame.h" +#include "ORBmatcher.h" + +#include "Thirdparty/DBoW2/DUtils/Random.h" + +namespace ORB_SLAM2 +{ + + +Sim3Solver::Sim3Solver(KeyFrame *pKF1, KeyFrame *pKF2, const vector &vpMatched12, const bool bFixScale): + mnIterations(0), mnBestInliers(0), mbFixScale(bFixScale) +{ + mpKF1 = pKF1; + mpKF2 = pKF2; + + vector vpKeyFrameMP1 = pKF1->GetMapPointMatches(); + + mN1 = vpMatched12.size(); + + mvpMapPoints1.reserve(mN1); + mvpMapPoints2.reserve(mN1); + mvpMatches12 = vpMatched12; + mvnIndices1.reserve(mN1); + mvX3Dc1.reserve(mN1); + mvX3Dc2.reserve(mN1); + + cv::Mat Rcw1 = pKF1->GetRotation(); + cv::Mat tcw1 = pKF1->GetTranslation(); + cv::Mat Rcw2 = pKF2->GetRotation(); + cv::Mat tcw2 = pKF2->GetTranslation(); + + mvAllIndices.reserve(mN1); + + size_t idx=0; + for(int i1=0; i1isBad() || pMP2->isBad()) + continue; + + int indexKF1 = pMP1->GetIndexInKeyFrame(pKF1); + int indexKF2 = pMP2->GetIndexInKeyFrame(pKF2); + + if(indexKF1<0 || indexKF2<0) + continue; + + const cv::KeyPoint &kp1 = pKF1->mvKeysUn[indexKF1]; + const cv::KeyPoint &kp2 = pKF2->mvKeysUn[indexKF2]; + + const float sigmaSquare1 = pKF1->mvLevelSigma2[kp1.octave]; + const float sigmaSquare2 = pKF2->mvLevelSigma2[kp2.octave]; + + mvnMaxError1.push_back(9.210*sigmaSquare1); + mvnMaxError2.push_back(9.210*sigmaSquare2); + + mvpMapPoints1.push_back(pMP1); + mvpMapPoints2.push_back(pMP2); + mvnIndices1.push_back(i1); + + cv::Mat X3D1w = pMP1->GetWorldPos(); + mvX3Dc1.push_back(Rcw1*X3D1w+tcw1); + + cv::Mat X3D2w = pMP2->GetWorldPos(); + mvX3Dc2.push_back(Rcw2*X3D2w+tcw2); + + mvAllIndices.push_back(idx); + idx++; + } + } + + mK1 = pKF1->mK; + mK2 = pKF2->mK; + + FromCameraToImage(mvX3Dc1,mvP1im1,mK1); + FromCameraToImage(mvX3Dc2,mvP2im2,mK2); + + SetRansacParameters(); +} + +void Sim3Solver::SetRansacParameters(double probability, int minInliers, int maxIterations) +{ + mRansacProb = probability; + mRansacMinInliers = minInliers; + mRansacMaxIts = maxIterations; + + N = mvpMapPoints1.size(); // number of correspondences + + mvbInliersi.resize(N); + + // Adjust Parameters according to number of correspondences + float epsilon = (float)mRansacMinInliers/N; + + // Set RANSAC iterations according to probability, epsilon, and max iterations + int nIterations; + + if(mRansacMinInliers==N) + nIterations=1; + else + nIterations = ceil(log(1-mRansacProb)/log(1-pow(epsilon,3))); + + mRansacMaxIts = max(1,min(nIterations,mRansacMaxIts)); + + mnIterations = 0; +} + +cv::Mat Sim3Solver::iterate(int nIterations, bool &bNoMore, vector &vbInliers, int &nInliers) +{ + bNoMore = false; + vbInliers = vector(mN1,false); + nInliers=0; + + if(N vAvailableIndices; + + cv::Mat P3Dc1i(3,3,CV_32F); + cv::Mat P3Dc2i(3,3,CV_32F); + + int nCurrentIterations = 0; + while(mnIterations=mnBestInliers) + { + mvbBestInliers = mvbInliersi; + mnBestInliers = mnInliersi; + mBestT12 = mT12i.clone(); + mBestRotation = mR12i.clone(); + mBestTranslation = mt12i.clone(); + mBestScale = ms12i; + + if(mnInliersi>mRansacMinInliers) + { + nInliers = mnInliersi; + for(int i=0; i=mRansacMaxIts) + bNoMore=true; + + return cv::Mat(); +} + +cv::Mat Sim3Solver::find(vector &vbInliers12, int &nInliers) +{ + bool bFlag; + return iterate(mRansacMaxIts,bFlag,vbInliers12,nInliers); +} + +void Sim3Solver::ComputeCentroid(cv::Mat &P, cv::Mat &Pr, cv::Mat &C) +{ + cv::reduce(P,C,1,CV_REDUCE_SUM); + C = C/P.cols; + + for(int i=0; i(0,0)+M.at(1,1)+M.at(2,2); + N12 = M.at(1,2)-M.at(2,1); + N13 = M.at(2,0)-M.at(0,2); + N14 = M.at(0,1)-M.at(1,0); + N22 = M.at(0,0)-M.at(1,1)-M.at(2,2); + N23 = M.at(0,1)+M.at(1,0); + N24 = M.at(2,0)+M.at(0,2); + N33 = -M.at(0,0)+M.at(1,1)-M.at(2,2); + N34 = M.at(1,2)+M.at(2,1); + N44 = -M.at(0,0)-M.at(1,1)+M.at(2,2); + + N = (cv::Mat_(4,4) << N11, N12, N13, N14, + N12, N22, N23, N24, + N13, N23, N33, N34, + N14, N24, N34, N44); + + + // Step 4: Eigenvector of the highest eigenvalue + + cv::Mat eval, evec; + + cv::eigen(N,eval,evec); //evec[0] is the quaternion of the desired rotation + + cv::Mat vec(1,3,evec.type()); + (evec.row(0).colRange(1,4)).copyTo(vec); //extract imaginary part of the quaternion (sin*axis) + + // Rotation angle. sin is the norm of the imaginary part, cos is the real part + double ang=atan2(norm(vec),evec.at(0,0)); + + vec = 2*ang*vec/norm(vec); //Angle-axis representation. quaternion angle is the half + + mR12i.create(3,3,P1.type()); + + cv::Rodrigues(vec,mR12i); // computes the rotation matrix from angle-axis + + // Step 5: Rotate set 2 + + cv::Mat P3 = mR12i*Pr2; + + // Step 6: Scale + + if(!mbFixScale) + { + double nom = Pr1.dot(P3); + cv::Mat aux_P3(P3.size(),P3.type()); + aux_P3=P3; + cv::pow(P3,2,aux_P3); + double den = 0; + + for(int i=0; i(i,j); + } + } + + ms12i = nom/den; + } + else + ms12i = 1.0f; + + // Step 7: Translation + + mt12i.create(1,3,P1.type()); + mt12i = O1 - ms12i*mR12i*O2; + + // Step 8: Transformation + + // Step 8.1 T12 + mT12i = cv::Mat::eye(4,4,P1.type()); + + cv::Mat sR = ms12i*mR12i; + + sR.copyTo(mT12i.rowRange(0,3).colRange(0,3)); + mt12i.copyTo(mT12i.rowRange(0,3).col(3)); + + // Step 8.2 T21 + + mT21i = cv::Mat::eye(4,4,P1.type()); + + cv::Mat sRinv = (1.0/ms12i)*mR12i.t(); + + sRinv.copyTo(mT21i.rowRange(0,3).colRange(0,3)); + cv::Mat tinv = -sRinv*mt12i; + tinv.copyTo(mT21i.rowRange(0,3).col(3)); +} + + +void Sim3Solver::CheckInliers() +{ + vector vP1im2, vP2im1; + Project(mvX3Dc2,vP2im1,mT12i,mK1); + Project(mvX3Dc1,vP1im2,mT21i,mK2); + + mnInliersi=0; + + for(size_t i=0; i &vP3Dw, vector &vP2D, cv::Mat Tcw, cv::Mat K) +{ + cv::Mat Rcw = Tcw.rowRange(0,3).colRange(0,3); + cv::Mat tcw = Tcw.rowRange(0,3).col(3); + const float &fx = K.at(0,0); + const float &fy = K.at(1,1); + const float &cx = K.at(0,2); + const float &cy = K.at(1,2); + + vP2D.clear(); + vP2D.reserve(vP3Dw.size()); + + for(size_t i=0, iend=vP3Dw.size(); i(2)); + const float x = P3Dc.at(0)*invz; + const float y = P3Dc.at(1)*invz; + + vP2D.push_back((cv::Mat_(2,1) << fx*x+cx, fy*y+cy)); + } +} + +void Sim3Solver::FromCameraToImage(const vector &vP3Dc, vector &vP2D, cv::Mat K) +{ + const float &fx = K.at(0,0); + const float &fy = K.at(1,1); + const float &cx = K.at(0,2); + const float &cy = K.at(1,2); + + vP2D.clear(); + vP2D.reserve(vP3Dc.size()); + + for(size_t i=0, iend=vP3Dc.size(); i(2)); + const float x = vP3Dc[i].at(0)*invz; + const float y = vP3Dc[i].at(1)*invz; + + vP2D.push_back((cv::Mat_(2,1) << fx*x+cx, fy*y+cy)); + } +} + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/System.cc b/for_orb_slam2/src/System.cc new file mode 100644 index 00000000000..8df41570954 --- /dev/null +++ b/for_orb_slam2/src/System.cc @@ -0,0 +1,492 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + + + +#include "System.h" +#include "Converter.h" +#include +#include +#include + +namespace ORB_SLAM2 +{ + +System::System(const string &strVocFile, const string &strSettingsFile, const eSensor sensor, + const bool bUseViewer):mSensor(sensor), mpViewer(static_cast(NULL)), mbReset(false),mbActivateLocalizationMode(false), + mbDeactivateLocalizationMode(false) +{ + // Output welcome message + cout << endl << + "ORB-SLAM2 Copyright (C) 2014-2016 Raul Mur-Artal, University of Zaragoza." << endl << + "This program comes with ABSOLUTELY NO WARRANTY;" << endl << + "This is free software, and you are welcome to redistribute it" << endl << + "under certain conditions. See LICENSE.txt." << endl << endl; + + cout << "Input sensor was set to: "; + + if(mSensor==MONOCULAR) + cout << "Monocular" << endl; + else if(mSensor==STEREO) + cout << "Stereo" << endl; + else if(mSensor==RGBD) + cout << "RGB-D" << endl; + + //Check settings file + cv::FileStorage fsSettings(strSettingsFile.c_str(), cv::FileStorage::READ); + if(!fsSettings.isOpened()) + { + cerr << "Failed to open settings file at: " << strSettingsFile << endl; + exit(-1); + } + + + //Load ORB Vocabulary + cout << endl << "Loading ORB Vocabulary. This could take a while..." << endl; + + mpVocabulary = new ORBVocabulary(); + bool bVocLoad = mpVocabulary->loadFromTextFile(strVocFile); + if(!bVocLoad) + { + cerr << "Wrong path to vocabulary. " << endl; + cerr << "Falied to open at: " << strVocFile << endl; + exit(-1); + } + cout << "Vocabulary loaded!" << endl << endl; + + //Create KeyFrame Database + mpKeyFrameDatabase = new KeyFrameDatabase(*mpVocabulary); + + //Create the Map + mpMap = new Map(); + + //Create Drawers. These are used by the Viewer + mpFrameDrawer = new FrameDrawer(mpMap); + mpMapDrawer = new MapDrawer(mpMap, strSettingsFile); + + //Initialize the Tracking thread + //(it will live in the main thread of execution, the one that called this constructor) + mpTracker = new Tracking(this, mpVocabulary, mpFrameDrawer, mpMapDrawer, + mpMap, mpKeyFrameDatabase, strSettingsFile, mSensor); + + //Initialize the Local Mapping thread and launch + mpLocalMapper = new LocalMapping(mpMap, mSensor==MONOCULAR); + mptLocalMapping = new thread(&ORB_SLAM2::LocalMapping::Run,mpLocalMapper); + + //Initialize the Loop Closing thread and launch + mpLoopCloser = new LoopClosing(mpMap, mpKeyFrameDatabase, mpVocabulary, mSensor!=MONOCULAR); + mptLoopClosing = new thread(&ORB_SLAM2::LoopClosing::Run, mpLoopCloser); + + //Initialize the Viewer thread and launch + if(bUseViewer) + { + mpViewer = new Viewer(this, mpFrameDrawer,mpMapDrawer,mpTracker,strSettingsFile); + mptViewer = new thread(&Viewer::Run, mpViewer); + mpTracker->SetViewer(mpViewer); + } + + //Set pointers between threads + mpTracker->SetLocalMapper(mpLocalMapper); + mpTracker->SetLoopClosing(mpLoopCloser); + + mpLocalMapper->SetTracker(mpTracker); + mpLocalMapper->SetLoopCloser(mpLoopCloser); + + mpLoopCloser->SetTracker(mpTracker); + mpLoopCloser->SetLocalMapper(mpLocalMapper); +} + +cv::Mat System::TrackStereo(const cv::Mat &imLeft, const cv::Mat &imRight, const double ×tamp) +{ + if(mSensor!=STEREO) + { + cerr << "ERROR: you called TrackStereo but input sensor was not set to STEREO." << endl; + exit(-1); + } + + // Check mode change + { + unique_lock lock(mMutexMode); + if(mbActivateLocalizationMode) + { + mpLocalMapper->RequestStop(); + + // Wait until Local Mapping has effectively stopped + while(!mpLocalMapper->isStopped()) + { + usleep(1000); + } + + mpTracker->InformOnlyTracking(true); + mbActivateLocalizationMode = false; + } + if(mbDeactivateLocalizationMode) + { + mpTracker->InformOnlyTracking(false); + mpLocalMapper->Release(); + mbDeactivateLocalizationMode = false; + } + } + + // Check reset + { + unique_lock lock(mMutexReset); + if(mbReset) + { + mpTracker->Reset(); + mbReset = false; + } + } + + cv::Mat Tcw = mpTracker->GrabImageStereo(imLeft,imRight,timestamp); + + unique_lock lock2(mMutexState); + mTrackingState = mpTracker->mState; + mTrackedMapPoints = mpTracker->mCurrentFrame.mvpMapPoints; + mTrackedKeyPointsUn = mpTracker->mCurrentFrame.mvKeysUn; + return Tcw; +} + +cv::Mat System::TrackRGBD(const cv::Mat &im, const cv::Mat &depthmap, const double ×tamp) +{ + if(mSensor!=RGBD) + { + cerr << "ERROR: you called TrackRGBD but input sensor was not set to RGBD." << endl; + exit(-1); + } + + // Check mode change + { + unique_lock lock(mMutexMode); + if(mbActivateLocalizationMode) + { + mpLocalMapper->RequestStop(); + + // Wait until Local Mapping has effectively stopped + while(!mpLocalMapper->isStopped()) + { + usleep(1000); + } + + mpTracker->InformOnlyTracking(true); + mbActivateLocalizationMode = false; + } + if(mbDeactivateLocalizationMode) + { + mpTracker->InformOnlyTracking(false); + mpLocalMapper->Release(); + mbDeactivateLocalizationMode = false; + } + } + + // Check reset + { + unique_lock lock(mMutexReset); + if(mbReset) + { + mpTracker->Reset(); + mbReset = false; + } + } + + cv::Mat Tcw = mpTracker->GrabImageRGBD(im,depthmap,timestamp); + + unique_lock lock2(mMutexState); + mTrackingState = mpTracker->mState; + mTrackedMapPoints = mpTracker->mCurrentFrame.mvpMapPoints; + mTrackedKeyPointsUn = mpTracker->mCurrentFrame.mvKeysUn; + return Tcw; +} + +cv::Mat System::TrackMonocular(const cv::Mat &im, const double ×tamp) +{ + if(mSensor!=MONOCULAR) + { + cerr << "ERROR: you called TrackMonocular but input sensor was not set to Monocular." << endl; + exit(-1); + } + + // Check mode change + { + unique_lock lock(mMutexMode); + if(mbActivateLocalizationMode) + { + mpLocalMapper->RequestStop(); + + // Wait until Local Mapping has effectively stopped + while(!mpLocalMapper->isStopped()) + { + usleep(1000); + } + + mpTracker->InformOnlyTracking(true); + mbActivateLocalizationMode = false; + } + if(mbDeactivateLocalizationMode) + { + mpTracker->InformOnlyTracking(false); + mpLocalMapper->Release(); + mbDeactivateLocalizationMode = false; + } + } + + // Check reset + { + unique_lock lock(mMutexReset); + if(mbReset) + { + mpTracker->Reset(); + mbReset = false; + } + } + + cv::Mat Tcw = mpTracker->GrabImageMonocular(im,timestamp); + + unique_lock lock2(mMutexState); + mTrackingState = mpTracker->mState; + mTrackedMapPoints = mpTracker->mCurrentFrame.mvpMapPoints; + mTrackedKeyPointsUn = mpTracker->mCurrentFrame.mvKeysUn; + + return Tcw; +} + +void System::ActivateLocalizationMode() +{ + unique_lock lock(mMutexMode); + mbActivateLocalizationMode = true; +} + +void System::DeactivateLocalizationMode() +{ + unique_lock lock(mMutexMode); + mbDeactivateLocalizationMode = true; +} + +bool System::MapChanged() +{ + static int n=0; + int curn = mpMap->GetLastBigChangeIdx(); + if(n lock(mMutexReset); + mbReset = true; +} + +void System::Shutdown() +{ + mpLocalMapper->RequestFinish(); + mpLoopCloser->RequestFinish(); + if(mpViewer) + { + mpViewer->RequestFinish(); + while(!mpViewer->isFinished()) + usleep(5000); + } + + // Wait until all thread have effectively stopped + while(!mpLocalMapper->isFinished() || !mpLoopCloser->isFinished() || mpLoopCloser->isRunningGBA()) + { + usleep(5000); + } + + if(mpViewer) + pangolin::BindToContext("ORB-SLAM2: Map Viewer"); +} + +void System::SaveTrajectoryTUM(const string &filename) +{ + cout << endl << "Saving camera trajectory to " << filename << " ..." << endl; + if(mSensor==MONOCULAR) + { + cerr << "ERROR: SaveTrajectoryTUM cannot be used for monocular." << endl; + return; + } + + vector vpKFs = mpMap->GetAllKeyFrames(); + sort(vpKFs.begin(),vpKFs.end(),KeyFrame::lId); + + // Transform all keyframes so that the first keyframe is at the origin. + // After a loop closure the first keyframe might not be at the origin. + cv::Mat Two = vpKFs[0]->GetPoseInverse(); + + ofstream f; + f.open(filename.c_str()); + f << fixed; + + // Frame pose is stored relative to its reference keyframe (which is optimized by BA and pose graph). + // We need to get first the keyframe pose and then concatenate the relative transformation. + // Frames not localized (tracking failure) are not saved. + + // For each frame we have a reference keyframe (lRit), the timestamp (lT) and a flag + // which is true when tracking failed (lbL). + list::iterator lRit = mpTracker->mlpReferences.begin(); + list::iterator lT = mpTracker->mlFrameTimes.begin(); + list::iterator lbL = mpTracker->mlbLost.begin(); + for(list::iterator lit=mpTracker->mlRelativeFramePoses.begin(), + lend=mpTracker->mlRelativeFramePoses.end();lit!=lend;lit++, lRit++, lT++, lbL++) + { + if(*lbL) + continue; + + KeyFrame* pKF = *lRit; + + cv::Mat Trw = cv::Mat::eye(4,4,CV_32F); + + // If the reference keyframe was culled, traverse the spanning tree to get a suitable keyframe. + while(pKF->isBad()) + { + Trw = Trw*pKF->mTcp; + pKF = pKF->GetParent(); + } + + Trw = Trw*pKF->GetPose()*Two; + + cv::Mat Tcw = (*lit)*Trw; + cv::Mat Rwc = Tcw.rowRange(0,3).colRange(0,3).t(); + cv::Mat twc = -Rwc*Tcw.rowRange(0,3).col(3); + + vector q = Converter::toQuaternion(Rwc); + + f << setprecision(6) << *lT << " " << setprecision(9) << twc.at(0) << " " << twc.at(1) << " " << twc.at(2) << " " << q[0] << " " << q[1] << " " << q[2] << " " << q[3] << endl; + } + f.close(); + cout << endl << "trajectory saved!" << endl; +} + + +void System::SaveKeyFrameTrajectoryTUM(const string &filename) +{ + cout << endl << "Saving keyframe trajectory to " << filename << " ..." << endl; + + vector vpKFs = mpMap->GetAllKeyFrames(); + sort(vpKFs.begin(),vpKFs.end(),KeyFrame::lId); + + // Transform all keyframes so that the first keyframe is at the origin. + // After a loop closure the first keyframe might not be at the origin. + //cv::Mat Two = vpKFs[0]->GetPoseInverse(); + + ofstream f; + f.open(filename.c_str()); + f << fixed; + + for(size_t i=0; iSetPose(pKF->GetPose()*Two); + + if(pKF->isBad()) + continue; + + cv::Mat R = pKF->GetRotation().t(); + vector q = Converter::toQuaternion(R); + cv::Mat t = pKF->GetCameraCenter(); + f << setprecision(6) << pKF->mTimeStamp << setprecision(7) << " " << t.at(0) << " " << t.at(1) << " " << t.at(2) + << " " << q[0] << " " << q[1] << " " << q[2] << " " << q[3] << endl; + + } + + f.close(); + cout << endl << "trajectory saved!" << endl; +} + +void System::SaveTrajectoryKITTI(const string &filename) +{ + cout << endl << "Saving camera trajectory to " << filename << " ..." << endl; + if(mSensor==MONOCULAR) + { + cerr << "ERROR: SaveTrajectoryKITTI cannot be used for monocular." << endl; + return; + } + + vector vpKFs = mpMap->GetAllKeyFrames(); + sort(vpKFs.begin(),vpKFs.end(),KeyFrame::lId); + + // Transform all keyframes so that the first keyframe is at the origin. + // After a loop closure the first keyframe might not be at the origin. + cv::Mat Two = vpKFs[0]->GetPoseInverse(); + + ofstream f; + f.open(filename.c_str()); + f << fixed; + + // Frame pose is stored relative to its reference keyframe (which is optimized by BA and pose graph). + // We need to get first the keyframe pose and then concatenate the relative transformation. + // Frames not localized (tracking failure) are not saved. + + // For each frame we have a reference keyframe (lRit), the timestamp (lT) and a flag + // which is true when tracking failed (lbL). + list::iterator lRit = mpTracker->mlpReferences.begin(); + list::iterator lT = mpTracker->mlFrameTimes.begin(); + for(list::iterator lit=mpTracker->mlRelativeFramePoses.begin(), lend=mpTracker->mlRelativeFramePoses.end();lit!=lend;lit++, lRit++, lT++) + { + ORB_SLAM2::KeyFrame* pKF = *lRit; + + cv::Mat Trw = cv::Mat::eye(4,4,CV_32F); + + while(pKF->isBad()) + { + // cout << "bad parent" << endl; + Trw = Trw*pKF->mTcp; + pKF = pKF->GetParent(); + } + + Trw = Trw*pKF->GetPose()*Two; + + cv::Mat Tcw = (*lit)*Trw; + cv::Mat Rwc = Tcw.rowRange(0,3).colRange(0,3).t(); + cv::Mat twc = -Rwc*Tcw.rowRange(0,3).col(3); + + f << setprecision(9) << Rwc.at(0,0) << " " << Rwc.at(0,1) << " " << Rwc.at(0,2) << " " << twc.at(0) << " " << + Rwc.at(1,0) << " " << Rwc.at(1,1) << " " << Rwc.at(1,2) << " " << twc.at(1) << " " << + Rwc.at(2,0) << " " << Rwc.at(2,1) << " " << Rwc.at(2,2) << " " << twc.at(2) << endl; + } + f.close(); + cout << endl << "trajectory saved!" << endl; +} + +int System::GetTrackingState() +{ + unique_lock lock(mMutexState); + return mTrackingState; +} + +vector System::GetTrackedMapPoints() +{ + unique_lock lock(mMutexState); + return mTrackedMapPoints; +} + +vector System::GetTrackedKeyPointsUn() +{ + unique_lock lock(mMutexState); + return mTrackedKeyPointsUn; +} + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/Tracking.cc b/for_orb_slam2/src/Tracking.cc new file mode 100644 index 00000000000..2273b2ce48a --- /dev/null +++ b/for_orb_slam2/src/Tracking.cc @@ -0,0 +1,1592 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + + +#include "Tracking.h" + +#include +#include + +#include"ORBmatcher.h" +#include"FrameDrawer.h" +#include"Converter.h" +#include"Map.h" +#include"Initializer.h" + +#include"Optimizer.h" +#include"PnPsolver.h" + +#include + +#include + + +using namespace std; + +namespace ORB_SLAM2 +{ + +Tracking::Tracking(System *pSys, ORBVocabulary* pVoc, FrameDrawer *pFrameDrawer, MapDrawer *pMapDrawer, Map *pMap, KeyFrameDatabase* pKFDB, const string &strSettingPath, const int sensor): + mState(NO_IMAGES_YET), mSensor(sensor), mbOnlyTracking(false), mbVO(false), mpORBVocabulary(pVoc), + mpKeyFrameDB(pKFDB), mpInitializer(static_cast(NULL)), mpSystem(pSys), mpViewer(NULL), + mpFrameDrawer(pFrameDrawer), mpMapDrawer(pMapDrawer), mpMap(pMap), mnLastRelocFrameId(0) +{ + // Load camera parameters from settings file + + cv::FileStorage fSettings(strSettingPath, cv::FileStorage::READ); + float fx = fSettings["Camera.fx"]; + float fy = fSettings["Camera.fy"]; + float cx = fSettings["Camera.cx"]; + float cy = fSettings["Camera.cy"]; + + cv::Mat K = cv::Mat::eye(3,3,CV_32F); + K.at(0,0) = fx; + K.at(1,1) = fy; + K.at(0,2) = cx; + K.at(1,2) = cy; + K.copyTo(mK); + + cv::Mat DistCoef(4,1,CV_32F); + DistCoef.at(0) = fSettings["Camera.k1"]; + DistCoef.at(1) = fSettings["Camera.k2"]; + DistCoef.at(2) = fSettings["Camera.p1"]; + DistCoef.at(3) = fSettings["Camera.p2"]; + const float k3 = fSettings["Camera.k3"]; + if(k3!=0) + { + DistCoef.resize(5); + DistCoef.at(4) = k3; + } + DistCoef.copyTo(mDistCoef); + + mbf = fSettings["Camera.bf"]; + + float fps = fSettings["Camera.fps"]; + if(fps==0) + fps=30; + + // Max/Min Frames to insert keyframes and to check relocalisation + mMinFrames = 0; + mMaxFrames = fps; + + cout << endl << "Camera Parameters: " << endl; + cout << "- fx: " << fx << endl; + cout << "- fy: " << fy << endl; + cout << "- cx: " << cx << endl; + cout << "- cy: " << cy << endl; + cout << "- k1: " << DistCoef.at(0) << endl; + cout << "- k2: " << DistCoef.at(1) << endl; + if(DistCoef.rows==5) + cout << "- k3: " << DistCoef.at(4) << endl; + cout << "- p1: " << DistCoef.at(2) << endl; + cout << "- p2: " << DistCoef.at(3) << endl; + cout << "- fps: " << fps << endl; + + + int nRGB = fSettings["Camera.RGB"]; + mbRGB = nRGB; + + if(mbRGB) + cout << "- color order: RGB (ignored if grayscale)" << endl; + else + cout << "- color order: BGR (ignored if grayscale)" << endl; + + // Load ORB parameters + + int nFeatures = fSettings["ORBextractor.nFeatures"]; + float fScaleFactor = fSettings["ORBextractor.scaleFactor"]; + int nLevels = fSettings["ORBextractor.nLevels"]; + int fIniThFAST = fSettings["ORBextractor.iniThFAST"]; + int fMinThFAST = fSettings["ORBextractor.minThFAST"]; + + mpORBextractorLeft = new ORBextractor(nFeatures,fScaleFactor,nLevels,fIniThFAST,fMinThFAST); + + if(sensor==System::STEREO) + mpORBextractorRight = new ORBextractor(nFeatures,fScaleFactor,nLevels,fIniThFAST,fMinThFAST); + + if(sensor==System::MONOCULAR) + mpIniORBextractor = new ORBextractor(2*nFeatures,fScaleFactor,nLevels,fIniThFAST,fMinThFAST); + + cout << endl << "ORB Extractor Parameters: " << endl; + cout << "- Number of Features: " << nFeatures << endl; + cout << "- Scale Levels: " << nLevels << endl; + cout << "- Scale Factor: " << fScaleFactor << endl; + cout << "- Initial Fast Threshold: " << fIniThFAST << endl; + cout << "- Minimum Fast Threshold: " << fMinThFAST << endl; + + if(sensor==System::STEREO || sensor==System::RGBD) + { + mThDepth = mbf*(float)fSettings["ThDepth"]/fx; + cout << endl << "Depth Threshold (Close/Far Points): " << mThDepth << endl; + } + + if(sensor==System::RGBD) + { + mDepthMapFactor = fSettings["DepthMapFactor"]; + if(fabs(mDepthMapFactor)<1e-5) + mDepthMapFactor=1; + else + mDepthMapFactor = 1.0f/mDepthMapFactor; + } + +} + +void Tracking::SetLocalMapper(LocalMapping *pLocalMapper) +{ + mpLocalMapper=pLocalMapper; +} + +void Tracking::SetLoopClosing(LoopClosing *pLoopClosing) +{ + mpLoopClosing=pLoopClosing; +} + +void Tracking::SetViewer(Viewer *pViewer) +{ + mpViewer=pViewer; +} + + +cv::Mat Tracking::GrabImageStereo(const cv::Mat &imRectLeft, const cv::Mat &imRectRight, const double ×tamp) +{ + mImGray = imRectLeft; + cv::Mat imGrayRight = imRectRight; + + if(mImGray.channels()==3) + { + if(mbRGB) + { + cvtColor(mImGray,mImGray,CV_RGB2GRAY); + cvtColor(imGrayRight,imGrayRight,CV_RGB2GRAY); + } + else + { + cvtColor(mImGray,mImGray,CV_BGR2GRAY); + cvtColor(imGrayRight,imGrayRight,CV_BGR2GRAY); + } + } + else if(mImGray.channels()==4) + { + if(mbRGB) + { + cvtColor(mImGray,mImGray,CV_RGBA2GRAY); + cvtColor(imGrayRight,imGrayRight,CV_RGBA2GRAY); + } + else + { + cvtColor(mImGray,mImGray,CV_BGRA2GRAY); + cvtColor(imGrayRight,imGrayRight,CV_BGRA2GRAY); + } + } + + mCurrentFrame = Frame(mImGray,imGrayRight,timestamp,mpORBextractorLeft,mpORBextractorRight,mpORBVocabulary,mK,mDistCoef,mbf,mThDepth); + + Track(); + + return mCurrentFrame.mTcw.clone(); +} + + +cv::Mat Tracking::GrabImageRGBD(const cv::Mat &imRGB,const cv::Mat &imD, const double ×tamp) +{ + mImGray = imRGB; + cv::Mat imDepth = imD; + + if(mImGray.channels()==3) + { + if(mbRGB) + cvtColor(mImGray,mImGray,CV_RGB2GRAY); + else + cvtColor(mImGray,mImGray,CV_BGR2GRAY); + } + else if(mImGray.channels()==4) + { + if(mbRGB) + cvtColor(mImGray,mImGray,CV_RGBA2GRAY); + else + cvtColor(mImGray,mImGray,CV_BGRA2GRAY); + } + + if((fabs(mDepthMapFactor-1.0f)>1e-5) || imDepth.type()!=CV_32F) + imDepth.convertTo(imDepth,CV_32F,mDepthMapFactor); + + mCurrentFrame = Frame(mImGray,imDepth,timestamp,mpORBextractorLeft,mpORBVocabulary,mK,mDistCoef,mbf,mThDepth); + + Track(); + + return mCurrentFrame.mTcw.clone(); +} + + +cv::Mat Tracking::GrabImageMonocular(const cv::Mat &im, const double ×tamp) +{ + mImGray = im; + + if(mImGray.channels()==3) + { + if(mbRGB) + cvtColor(mImGray,mImGray,CV_RGB2GRAY); + else + cvtColor(mImGray,mImGray,CV_BGR2GRAY); + } + else if(mImGray.channels()==4) + { + if(mbRGB) + cvtColor(mImGray,mImGray,CV_RGBA2GRAY); + else + cvtColor(mImGray,mImGray,CV_BGRA2GRAY); + } + + if(mState==NOT_INITIALIZED || mState==NO_IMAGES_YET) + mCurrentFrame = Frame(mImGray,timestamp,mpIniORBextractor,mpORBVocabulary,mK,mDistCoef,mbf,mThDepth); + else + mCurrentFrame = Frame(mImGray,timestamp,mpORBextractorLeft,mpORBVocabulary,mK,mDistCoef,mbf,mThDepth); + + Track(); + + return mCurrentFrame.mTcw.clone(); +} + +void Tracking::Track() +{ + if(mState==NO_IMAGES_YET) + { + mState = NOT_INITIALIZED; + } + + mLastProcessedState=mState; + + // Get Map Mutex -> Map cannot be changed + unique_lock lock(mpMap->mMutexMapUpdate); + + if(mState==NOT_INITIALIZED) + { + if(mSensor==System::STEREO || mSensor==System::RGBD) + StereoInitialization(); + else + MonocularInitialization(); + + mpFrameDrawer->Update(this); + + if(mState!=OK) + return; + } + else + { + // System is initialized. Track Frame. + bool bOK; + + // Initial camera pose estimation using motion model or relocalization (if tracking is lost) + if(!mbOnlyTracking) + { + // Local Mapping is activated. This is the normal behaviour, unless + // you explicitly activate the "only tracking" mode. + + if(mState==OK) + { + // Local Mapping might have changed some MapPoints tracked in last frame + CheckReplacedInLastFrame(); + + if(mVelocity.empty() || mCurrentFrame.mnId vpMPsMM; + vector vbOutMM; + cv::Mat TcwMM; + if(!mVelocity.empty()) + { + bOKMM = TrackWithMotionModel(); + vpMPsMM = mCurrentFrame.mvpMapPoints; + vbOutMM = mCurrentFrame.mvbOutlier; + TcwMM = mCurrentFrame.mTcw.clone(); + } + bOKReloc = Relocalization(); + + if(bOKMM && !bOKReloc) + { + mCurrentFrame.SetPose(TcwMM); + mCurrentFrame.mvpMapPoints = vpMPsMM; + mCurrentFrame.mvbOutlier = vbOutMM; + + if(mbVO) + { + for(int i =0; iIncreaseFound(); + } + } + } + } + else if(bOKReloc) + { + mbVO = false; + } + + bOK = bOKReloc || bOKMM; + } + } + } + + mCurrentFrame.mpReferenceKF = mpReferenceKF; + + // If we have an initial estimation of the camera pose and matching. Track the local map. + if(!mbOnlyTracking) + { + if(bOK) + bOK = TrackLocalMap(); + } + else + { + // mbVO true means that there are few matches to MapPoints in the map. We cannot retrieve + // a local map and therefore we do not perform TrackLocalMap(). Once the system relocalizes + // the camera we will use the local map again. + if(bOK && !mbVO) + bOK = TrackLocalMap(); + } + + if(bOK) + mState = OK; + else + mState=LOST; + + // Update drawer + mpFrameDrawer->Update(this); + + // If tracking were good, check if we insert a keyframe + if(bOK) + { + // Update motion model + if(!mLastFrame.mTcw.empty()) + { + cv::Mat LastTwc = cv::Mat::eye(4,4,CV_32F); + mLastFrame.GetRotationInverse().copyTo(LastTwc.rowRange(0,3).colRange(0,3)); + mLastFrame.GetCameraCenter().copyTo(LastTwc.rowRange(0,3).col(3)); + mVelocity = mCurrentFrame.mTcw*LastTwc; + } + else + mVelocity = cv::Mat(); + + mpMapDrawer->SetCurrentCameraPose(mCurrentFrame.mTcw); + + // Clean VO matches + for(int i=0; iObservations()<1) + { + mCurrentFrame.mvbOutlier[i] = false; + mCurrentFrame.mvpMapPoints[i]=static_cast(NULL); + } + } + + // Delete temporal MapPoints + for(list::iterator lit = mlpTemporalPoints.begin(), lend = mlpTemporalPoints.end(); lit!=lend; lit++) + { + MapPoint* pMP = *lit; + delete pMP; + } + mlpTemporalPoints.clear(); + + // Check if we need to insert a new keyframe + if(NeedNewKeyFrame()) + CreateNewKeyFrame(); + + // We allow points with high innovation (considererd outliers by the Huber Function) + // pass to the new keyframe, so that bundle adjustment will finally decide + // if they are outliers or not. We don't want next frame to estimate its position + // with those points so we discard them in the frame. + for(int i=0; i(NULL); + } + } + + // Reset if the camera get lost soon after initialization + if(mState==LOST) + { + if(mpMap->KeyFramesInMap()<=5) + { + cout << "Track lost soon after initialisation, reseting..." << endl; + mpSystem->Reset(); + return; + } + } + + if(!mCurrentFrame.mpReferenceKF) + mCurrentFrame.mpReferenceKF = mpReferenceKF; + + mLastFrame = Frame(mCurrentFrame); + } + + // Store frame pose information to retrieve the complete camera trajectory afterwards. + if(!mCurrentFrame.mTcw.empty()) + { + cv::Mat Tcr = mCurrentFrame.mTcw*mCurrentFrame.mpReferenceKF->GetPoseInverse(); + mlRelativeFramePoses.push_back(Tcr); + mlpReferences.push_back(mpReferenceKF); + mlFrameTimes.push_back(mCurrentFrame.mTimeStamp); + mlbLost.push_back(mState==LOST); + } + else + { + // This can happen if tracking is lost + mlRelativeFramePoses.push_back(mlRelativeFramePoses.back()); + mlpReferences.push_back(mlpReferences.back()); + mlFrameTimes.push_back(mlFrameTimes.back()); + mlbLost.push_back(mState==LOST); + } + +} + + +void Tracking::StereoInitialization() +{ + if(mCurrentFrame.N>500) + { + // Set Frame pose to the origin + mCurrentFrame.SetPose(cv::Mat::eye(4,4,CV_32F)); + + // Create KeyFrame + KeyFrame* pKFini = new KeyFrame(mCurrentFrame,mpMap,mpKeyFrameDB); + + // Insert KeyFrame in the map + mpMap->AddKeyFrame(pKFini); + + // Create MapPoints and asscoiate to KeyFrame + for(int i=0; i0) + { + cv::Mat x3D = mCurrentFrame.UnprojectStereo(i); + MapPoint* pNewMP = new MapPoint(x3D,pKFini,mpMap); + pNewMP->AddObservation(pKFini,i); + pKFini->AddMapPoint(pNewMP,i); + pNewMP->ComputeDistinctiveDescriptors(); + pNewMP->UpdateNormalAndDepth(); + mpMap->AddMapPoint(pNewMP); + + mCurrentFrame.mvpMapPoints[i]=pNewMP; + } + } + + cout << "New map created with " << mpMap->MapPointsInMap() << " points" << endl; + + mpLocalMapper->InsertKeyFrame(pKFini); + + mLastFrame = Frame(mCurrentFrame); + mnLastKeyFrameId=mCurrentFrame.mnId; + mpLastKeyFrame = pKFini; + + mvpLocalKeyFrames.push_back(pKFini); + mvpLocalMapPoints=mpMap->GetAllMapPoints(); + mpReferenceKF = pKFini; + mCurrentFrame.mpReferenceKF = pKFini; + + mpMap->SetReferenceMapPoints(mvpLocalMapPoints); + + mpMap->mvpKeyFrameOrigins.push_back(pKFini); + + mpMapDrawer->SetCurrentCameraPose(mCurrentFrame.mTcw); + + mState=OK; + } +} + +void Tracking::MonocularInitialization() +{ + + if(!mpInitializer) + { + // Set Reference Frame + if(mCurrentFrame.mvKeys.size()>100) + { + mInitialFrame = Frame(mCurrentFrame); + mLastFrame = Frame(mCurrentFrame); + mvbPrevMatched.resize(mCurrentFrame.mvKeysUn.size()); + for(size_t i=0; i(NULL); + fill(mvIniMatches.begin(),mvIniMatches.end(),-1); + return; + } + + // Find correspondences + ORBmatcher matcher(0.9,true); + int nmatches = matcher.SearchForInitialization(mInitialFrame,mCurrentFrame,mvbPrevMatched,mvIniMatches,100); + + // Check if there are enough correspondences + if(nmatches<100) + { + delete mpInitializer; + mpInitializer = static_cast(NULL); + return; + } + + cv::Mat Rcw; // Current Camera Rotation + cv::Mat tcw; // Current Camera Translation + vector vbTriangulated; // Triangulated Correspondences (mvIniMatches) + + if(mpInitializer->Initialize(mCurrentFrame, mvIniMatches, Rcw, tcw, mvIniP3D, vbTriangulated)) + { + for(size_t i=0, iend=mvIniMatches.size(); i=0 && !vbTriangulated[i]) + { + mvIniMatches[i]=-1; + nmatches--; + } + } + + // Set Frame Poses + mInitialFrame.SetPose(cv::Mat::eye(4,4,CV_32F)); + cv::Mat Tcw = cv::Mat::eye(4,4,CV_32F); + Rcw.copyTo(Tcw.rowRange(0,3).colRange(0,3)); + tcw.copyTo(Tcw.rowRange(0,3).col(3)); + mCurrentFrame.SetPose(Tcw); + + CreateInitialMapMonocular(); + } + } +} + +void Tracking::CreateInitialMapMonocular() +{ + // Create KeyFrames + KeyFrame* pKFini = new KeyFrame(mInitialFrame,mpMap,mpKeyFrameDB); + KeyFrame* pKFcur = new KeyFrame(mCurrentFrame,mpMap,mpKeyFrameDB); + + + pKFini->ComputeBoW(); + pKFcur->ComputeBoW(); + + // Insert KFs in the map + mpMap->AddKeyFrame(pKFini); + mpMap->AddKeyFrame(pKFcur); + + // Create MapPoints and asscoiate to keyframes + for(size_t i=0; iAddMapPoint(pMP,i); + pKFcur->AddMapPoint(pMP,mvIniMatches[i]); + + pMP->AddObservation(pKFini,i); + pMP->AddObservation(pKFcur,mvIniMatches[i]); + + pMP->ComputeDistinctiveDescriptors(); + pMP->UpdateNormalAndDepth(); + + //Fill Current Frame structure + mCurrentFrame.mvpMapPoints[mvIniMatches[i]] = pMP; + mCurrentFrame.mvbOutlier[mvIniMatches[i]] = false; + + //Add to Map + mpMap->AddMapPoint(pMP); + } + + // Update Connections + pKFini->UpdateConnections(); + pKFcur->UpdateConnections(); + + // Bundle Adjustment + cout << "New Map created with " << mpMap->MapPointsInMap() << " points" << endl; + + Optimizer::GlobalBundleAdjustemnt(mpMap,20); + + // Set median depth to 1 + float medianDepth = pKFini->ComputeSceneMedianDepth(2); + float invMedianDepth = 1.0f/medianDepth; + + if(medianDepth<0 || pKFcur->TrackedMapPoints(1)<100) + { + cout << "Wrong initialization, reseting..." << endl; + Reset(); + return; + } + + // Scale initial baseline + cv::Mat Tc2w = pKFcur->GetPose(); + Tc2w.col(3).rowRange(0,3) = Tc2w.col(3).rowRange(0,3)*invMedianDepth; + pKFcur->SetPose(Tc2w); + + // Scale points + vector vpAllMapPoints = pKFini->GetMapPointMatches(); + for(size_t iMP=0; iMPSetWorldPos(pMP->GetWorldPos()*invMedianDepth); + } + } + + mpLocalMapper->InsertKeyFrame(pKFini); + mpLocalMapper->InsertKeyFrame(pKFcur); + + mCurrentFrame.SetPose(pKFcur->GetPose()); + mnLastKeyFrameId=mCurrentFrame.mnId; + mpLastKeyFrame = pKFcur; + + mvpLocalKeyFrames.push_back(pKFcur); + mvpLocalKeyFrames.push_back(pKFini); + mvpLocalMapPoints=mpMap->GetAllMapPoints(); + mpReferenceKF = pKFcur; + mCurrentFrame.mpReferenceKF = pKFcur; + + mLastFrame = Frame(mCurrentFrame); + + mpMap->SetReferenceMapPoints(mvpLocalMapPoints); + + mpMapDrawer->SetCurrentCameraPose(pKFcur->GetPose()); + + mpMap->mvpKeyFrameOrigins.push_back(pKFini); + + mState=OK; +} + +void Tracking::CheckReplacedInLastFrame() +{ + for(int i =0; iGetReplaced(); + if(pRep) + { + mLastFrame.mvpMapPoints[i] = pRep; + } + } + } +} + + +bool Tracking::TrackReferenceKeyFrame() +{ + // Compute Bag of Words vector + mCurrentFrame.ComputeBoW(); + + // We perform first an ORB matching with the reference keyframe + // If enough matches are found we setup a PnP solver + ORBmatcher matcher(0.7,true); + vector vpMapPointMatches; + + int nmatches = matcher.SearchByBoW(mpReferenceKF,mCurrentFrame,vpMapPointMatches); + + if(nmatches<15) + return false; + + mCurrentFrame.mvpMapPoints = vpMapPointMatches; + mCurrentFrame.SetPose(mLastFrame.mTcw); + + Optimizer::PoseOptimization(&mCurrentFrame); + + // Discard outliers + int nmatchesMap = 0; + for(int i =0; i(NULL); + mCurrentFrame.mvbOutlier[i]=false; + pMP->mbTrackInView = false; + pMP->mnLastFrameSeen = mCurrentFrame.mnId; + nmatches--; + } + else if(mCurrentFrame.mvpMapPoints[i]->Observations()>0) + nmatchesMap++; + } + } + + return nmatchesMap>=10; +} + +void Tracking::UpdateLastFrame() +{ + // Update pose according to reference keyframe + KeyFrame* pRef = mLastFrame.mpReferenceKF; + cv::Mat Tlr = mlRelativeFramePoses.back(); + + mLastFrame.SetPose(Tlr*pRef->GetPose()); + + if(mnLastKeyFrameId==mLastFrame.mnId || mSensor==System::MONOCULAR || !mbOnlyTracking) + return; + + // Create "visual odometry" MapPoints + // We sort points according to their measured depth by the stereo/RGB-D sensor + vector > vDepthIdx; + vDepthIdx.reserve(mLastFrame.N); + for(int i=0; i0) + { + vDepthIdx.push_back(make_pair(z,i)); + } + } + + if(vDepthIdx.empty()) + return; + + sort(vDepthIdx.begin(),vDepthIdx.end()); + + // We insert all close points (depthObservations()<1) + { + bCreateNew = true; + } + + if(bCreateNew) + { + cv::Mat x3D = mLastFrame.UnprojectStereo(i); + MapPoint* pNewMP = new MapPoint(x3D,mpMap,&mLastFrame,i); + + mLastFrame.mvpMapPoints[i]=pNewMP; + + mlpTemporalPoints.push_back(pNewMP); + nPoints++; + } + else + { + nPoints++; + } + + if(vDepthIdx[j].first>mThDepth && nPoints>100) + break; + } +} + +bool Tracking::TrackWithMotionModel() +{ + ORBmatcher matcher(0.9,true); + + // Update last frame pose according to its reference keyframe + // Create "visual odometry" points if in Localization Mode + UpdateLastFrame(); + + mCurrentFrame.SetPose(mVelocity*mLastFrame.mTcw); + + fill(mCurrentFrame.mvpMapPoints.begin(),mCurrentFrame.mvpMapPoints.end(),static_cast(NULL)); + + // Project points seen in previous frame + int th; + if(mSensor!=System::STEREO) + th=15; + else + th=7; + int nmatches = matcher.SearchByProjection(mCurrentFrame,mLastFrame,th,mSensor==System::MONOCULAR); + + // If few matches, uses a wider window search + if(nmatches<20) + { + fill(mCurrentFrame.mvpMapPoints.begin(),mCurrentFrame.mvpMapPoints.end(),static_cast(NULL)); + nmatches = matcher.SearchByProjection(mCurrentFrame,mLastFrame,2*th,mSensor==System::MONOCULAR); + } + + if(nmatches<20) + return false; + + // Optimize frame pose with all matches + Optimizer::PoseOptimization(&mCurrentFrame); + + // Discard outliers + int nmatchesMap = 0; + for(int i =0; i(NULL); + mCurrentFrame.mvbOutlier[i]=false; + pMP->mbTrackInView = false; + pMP->mnLastFrameSeen = mCurrentFrame.mnId; + nmatches--; + } + else if(mCurrentFrame.mvpMapPoints[i]->Observations()>0) + nmatchesMap++; + } + } + + if(mbOnlyTracking) + { + mbVO = nmatchesMap<10; + return nmatches>20; + } + + return nmatchesMap>=10; +} + +bool Tracking::TrackLocalMap() +{ + // We have an estimation of the camera pose and some map points tracked in the frame. + // We retrieve the local map and try to find matches to points in the local map. + + UpdateLocalMap(); + + SearchLocalPoints(); + + // Optimize Pose + Optimizer::PoseOptimization(&mCurrentFrame); + mnMatchesInliers = 0; + + // Update MapPoints Statistics + for(int i=0; iIncreaseFound(); + if(!mbOnlyTracking) + { + if(mCurrentFrame.mvpMapPoints[i]->Observations()>0) + mnMatchesInliers++; + } + else + mnMatchesInliers++; + } + else if(mSensor==System::STEREO) + mCurrentFrame.mvpMapPoints[i] = static_cast(NULL); + + } + } + + // Decide if the tracking was succesful + // More restrictive if there was a relocalization recently + if(mCurrentFrame.mnIdisStopped() || mpLocalMapper->stopRequested()) + return false; + + const int nKFs = mpMap->KeyFramesInMap(); + + // Do not insert keyframes if not enough frames have passed from last relocalisation + if(mCurrentFrame.mnIdmMaxFrames) + return false; + + // Tracked MapPoints in the reference keyframe + int nMinObs = 3; + if(nKFs<=2) + nMinObs=2; + int nRefMatches = mpReferenceKF->TrackedMapPoints(nMinObs); + + // Local Mapping accept keyframes? + bool bLocalMappingIdle = mpLocalMapper->AcceptKeyFrames(); + + // Check how many "close" points are being tracked and how many could be potentially created. + int nNonTrackedClose = 0; + int nTrackedClose= 0; + if(mSensor!=System::MONOCULAR) + { + for(int i =0; i0 && mCurrentFrame.mvDepth[i]70); + + // Thresholds + float thRefRatio = 0.75f; + if(nKFs<2) + thRefRatio = 0.4f; + + if(mSensor==System::MONOCULAR) + thRefRatio = 0.9f; + + // Condition 1a: More than "MaxFrames" have passed from last keyframe insertion + const bool c1a = mCurrentFrame.mnId>=mnLastKeyFrameId+mMaxFrames; + // Condition 1b: More than "MinFrames" have passed and Local Mapping is idle + const bool c1b = (mCurrentFrame.mnId>=mnLastKeyFrameId+mMinFrames && bLocalMappingIdle); + //Condition 1c: tracking is weak + const bool c1c = mSensor!=System::MONOCULAR && (mnMatchesInliers15); + + if((c1a||c1b||c1c)&&c2) + { + // If the mapping accepts keyframes, insert keyframe. + // Otherwise send a signal to interrupt BA + if(bLocalMappingIdle) + { + return true; + } + else + { + mpLocalMapper->InterruptBA(); + if(mSensor!=System::MONOCULAR) + { + if(mpLocalMapper->KeyframesInQueue()<3) + return true; + else + return false; + } + else + return false; + } + } + else + return false; +} + +void Tracking::CreateNewKeyFrame() +{ + if(!mpLocalMapper->SetNotStop(true)) + return; + + KeyFrame* pKF = new KeyFrame(mCurrentFrame,mpMap,mpKeyFrameDB); + + mpReferenceKF = pKF; + mCurrentFrame.mpReferenceKF = pKF; + + if(mSensor!=System::MONOCULAR) + { + mCurrentFrame.UpdatePoseMatrices(); + + // We sort points by the measured depth by the stereo/RGBD sensor. + // We create all those MapPoints whose depth < mThDepth. + // If there are less than 100 close points we create the 100 closest. + vector > vDepthIdx; + vDepthIdx.reserve(mCurrentFrame.N); + for(int i=0; i0) + { + vDepthIdx.push_back(make_pair(z,i)); + } + } + + if(!vDepthIdx.empty()) + { + sort(vDepthIdx.begin(),vDepthIdx.end()); + + int nPoints = 0; + for(size_t j=0; jObservations()<1) + { + bCreateNew = true; + mCurrentFrame.mvpMapPoints[i] = static_cast(NULL); + } + + if(bCreateNew) + { + cv::Mat x3D = mCurrentFrame.UnprojectStereo(i); + MapPoint* pNewMP = new MapPoint(x3D,pKF,mpMap); + pNewMP->AddObservation(pKF,i); + pKF->AddMapPoint(pNewMP,i); + pNewMP->ComputeDistinctiveDescriptors(); + pNewMP->UpdateNormalAndDepth(); + mpMap->AddMapPoint(pNewMP); + + mCurrentFrame.mvpMapPoints[i]=pNewMP; + nPoints++; + } + else + { + nPoints++; + } + + if(vDepthIdx[j].first>mThDepth && nPoints>100) + break; + } + } + } + + mpLocalMapper->InsertKeyFrame(pKF); + + mpLocalMapper->SetNotStop(false); + + mnLastKeyFrameId = mCurrentFrame.mnId; + mpLastKeyFrame = pKF; +} + +void Tracking::SearchLocalPoints() +{ + // Do not search map points already matched + for(vector::iterator vit=mCurrentFrame.mvpMapPoints.begin(), vend=mCurrentFrame.mvpMapPoints.end(); vit!=vend; vit++) + { + MapPoint* pMP = *vit; + if(pMP) + { + if(pMP->isBad()) + { + *vit = static_cast(NULL); + } + else + { + pMP->IncreaseVisible(); + pMP->mnLastFrameSeen = mCurrentFrame.mnId; + pMP->mbTrackInView = false; + } + } + } + + int nToMatch=0; + + // Project points in frame and check its visibility + for(vector::iterator vit=mvpLocalMapPoints.begin(), vend=mvpLocalMapPoints.end(); vit!=vend; vit++) + { + MapPoint* pMP = *vit; + if(pMP->mnLastFrameSeen == mCurrentFrame.mnId) + continue; + if(pMP->isBad()) + continue; + // Project (this fills MapPoint variables for matching) + if(mCurrentFrame.isInFrustum(pMP,0.5)) + { + pMP->IncreaseVisible(); + nToMatch++; + } + } + + if(nToMatch>0) + { + ORBmatcher matcher(0.8); + int th = 1; + if(mSensor==System::RGBD) + th=3; + // If the camera has been relocalised recently, perform a coarser search + if(mCurrentFrame.mnIdSetReferenceMapPoints(mvpLocalMapPoints); + + // Update + UpdateLocalKeyFrames(); + UpdateLocalPoints(); +} + +void Tracking::UpdateLocalPoints() +{ + mvpLocalMapPoints.clear(); + + for(vector::const_iterator itKF=mvpLocalKeyFrames.begin(), itEndKF=mvpLocalKeyFrames.end(); itKF!=itEndKF; itKF++) + { + KeyFrame* pKF = *itKF; + const vector vpMPs = pKF->GetMapPointMatches(); + + for(vector::const_iterator itMP=vpMPs.begin(), itEndMP=vpMPs.end(); itMP!=itEndMP; itMP++) + { + MapPoint* pMP = *itMP; + if(!pMP) + continue; + if(pMP->mnTrackReferenceForFrame==mCurrentFrame.mnId) + continue; + if(!pMP->isBad()) + { + mvpLocalMapPoints.push_back(pMP); + pMP->mnTrackReferenceForFrame=mCurrentFrame.mnId; + } + } + } +} + + +void Tracking::UpdateLocalKeyFrames() +{ + // Each map point vote for the keyframes in which it has been observed + map keyframeCounter; + for(int i=0; iisBad()) + { + const map observations = pMP->GetObservations(); + for(map::const_iterator it=observations.begin(), itend=observations.end(); it!=itend; it++) + keyframeCounter[it->first]++; + } + else + { + mCurrentFrame.mvpMapPoints[i]=NULL; + } + } + } + + if(keyframeCounter.empty()) + return; + + int max=0; + KeyFrame* pKFmax= static_cast(NULL); + + mvpLocalKeyFrames.clear(); + mvpLocalKeyFrames.reserve(3*keyframeCounter.size()); + + // All keyframes that observe a map point are included in the local map. Also check which keyframe shares most points + for(map::const_iterator it=keyframeCounter.begin(), itEnd=keyframeCounter.end(); it!=itEnd; it++) + { + KeyFrame* pKF = it->first; + + if(pKF->isBad()) + continue; + + if(it->second>max) + { + max=it->second; + pKFmax=pKF; + } + + mvpLocalKeyFrames.push_back(it->first); + pKF->mnTrackReferenceForFrame = mCurrentFrame.mnId; + } + + + // Include also some not-already-included keyframes that are neighbors to already-included keyframes + for(vector::const_iterator itKF=mvpLocalKeyFrames.begin(), itEndKF=mvpLocalKeyFrames.end(); itKF!=itEndKF; itKF++) + { + // Limit the number of keyframes + if(mvpLocalKeyFrames.size()>80) + break; + + KeyFrame* pKF = *itKF; + + const vector vNeighs = pKF->GetBestCovisibilityKeyFrames(10); + + for(vector::const_iterator itNeighKF=vNeighs.begin(), itEndNeighKF=vNeighs.end(); itNeighKF!=itEndNeighKF; itNeighKF++) + { + KeyFrame* pNeighKF = *itNeighKF; + if(!pNeighKF->isBad()) + { + if(pNeighKF->mnTrackReferenceForFrame!=mCurrentFrame.mnId) + { + mvpLocalKeyFrames.push_back(pNeighKF); + pNeighKF->mnTrackReferenceForFrame=mCurrentFrame.mnId; + break; + } + } + } + + const set spChilds = pKF->GetChilds(); + for(set::const_iterator sit=spChilds.begin(), send=spChilds.end(); sit!=send; sit++) + { + KeyFrame* pChildKF = *sit; + if(!pChildKF->isBad()) + { + if(pChildKF->mnTrackReferenceForFrame!=mCurrentFrame.mnId) + { + mvpLocalKeyFrames.push_back(pChildKF); + pChildKF->mnTrackReferenceForFrame=mCurrentFrame.mnId; + break; + } + } + } + + KeyFrame* pParent = pKF->GetParent(); + if(pParent) + { + if(pParent->mnTrackReferenceForFrame!=mCurrentFrame.mnId) + { + mvpLocalKeyFrames.push_back(pParent); + pParent->mnTrackReferenceForFrame=mCurrentFrame.mnId; + break; + } + } + + } + + if(pKFmax) + { + mpReferenceKF = pKFmax; + mCurrentFrame.mpReferenceKF = mpReferenceKF; + } +} + +bool Tracking::Relocalization() +{ + // Compute Bag of Words Vector + mCurrentFrame.ComputeBoW(); + + // Relocalization is performed when tracking is lost + // Track Lost: Query KeyFrame Database for keyframe candidates for relocalisation + vector vpCandidateKFs = mpKeyFrameDB->DetectRelocalizationCandidates(&mCurrentFrame); + + if(vpCandidateKFs.empty()) + return false; + + const int nKFs = vpCandidateKFs.size(); + + // We perform first an ORB matching with each candidate + // If enough matches are found we setup a PnP solver + ORBmatcher matcher(0.75,true); + + vector vpPnPsolvers; + vpPnPsolvers.resize(nKFs); + + vector > vvpMapPointMatches; + vvpMapPointMatches.resize(nKFs); + + vector vbDiscarded; + vbDiscarded.resize(nKFs); + + int nCandidates=0; + + for(int i=0; iisBad()) + vbDiscarded[i] = true; + else + { + int nmatches = matcher.SearchByBoW(pKF,mCurrentFrame,vvpMapPointMatches[i]); + if(nmatches<15) + { + vbDiscarded[i] = true; + continue; + } + else + { + PnPsolver* pSolver = new PnPsolver(mCurrentFrame,vvpMapPointMatches[i]); + pSolver->SetRansacParameters(0.99,10,300,4,0.5,5.991); + vpPnPsolvers[i] = pSolver; + nCandidates++; + } + } + } + + // Alternatively perform some iterations of P4P RANSAC + // Until we found a camera pose supported by enough inliers + bool bMatch = false; + ORBmatcher matcher2(0.9,true); + + while(nCandidates>0 && !bMatch) + { + for(int i=0; i vbInliers; + int nInliers; + bool bNoMore; + + PnPsolver* pSolver = vpPnPsolvers[i]; + cv::Mat Tcw = pSolver->iterate(5,bNoMore,vbInliers,nInliers); + + // If Ransac reachs max. iterations discard keyframe + if(bNoMore) + { + vbDiscarded[i]=true; + nCandidates--; + } + + // If a Camera Pose is computed, optimize + if(!Tcw.empty()) + { + Tcw.copyTo(mCurrentFrame.mTcw); + + set sFound; + + const int np = vbInliers.size(); + + for(int j=0; j(NULL); + + // If few inliers, search by projection in a coarse window and optimize again + if(nGood<50) + { + int nadditional =matcher2.SearchByProjection(mCurrentFrame,vpCandidateKFs[i],sFound,10,100); + + if(nadditional+nGood>=50) + { + nGood = Optimizer::PoseOptimization(&mCurrentFrame); + + // If many inliers but still not enough, search by projection again in a narrower window + // the camera has been already optimized with many points + if(nGood>30 && nGood<50) + { + sFound.clear(); + for(int ip =0; ip=50) + { + nGood = Optimizer::PoseOptimization(&mCurrentFrame); + + for(int io =0; io=50) + { + bMatch = true; + break; + } + } + } + } + + if(!bMatch) + { + return false; + } + else + { + mnLastRelocFrameId = mCurrentFrame.mnId; + return true; + } + +} + +void Tracking::Reset() +{ + + cout << "System Reseting" << endl; + if(mpViewer) + { + mpViewer->RequestStop(); + while(!mpViewer->isStopped()) + usleep(3000); + } + + // Reset Local Mapping + cout << "Reseting Local Mapper..."; + mpLocalMapper->RequestReset(); + cout << " done" << endl; + + // Reset Loop Closing + cout << "Reseting Loop Closing..."; + mpLoopClosing->RequestReset(); + cout << " done" << endl; + + // Clear BoW Database + cout << "Reseting Database..."; + mpKeyFrameDB->clear(); + cout << " done" << endl; + + // Clear Map (this erase MapPoints and KeyFrames) + mpMap->clear(); + + KeyFrame::nNextId = 0; + Frame::nNextId = 0; + mState = NO_IMAGES_YET; + + if(mpInitializer) + { + delete mpInitializer; + mpInitializer = static_cast(NULL); + } + + mlRelativeFramePoses.clear(); + mlpReferences.clear(); + mlFrameTimes.clear(); + mlbLost.clear(); + + if(mpViewer) + mpViewer->Release(); +} + +void Tracking::ChangeCalibration(const string &strSettingPath) +{ + cv::FileStorage fSettings(strSettingPath, cv::FileStorage::READ); + float fx = fSettings["Camera.fx"]; + float fy = fSettings["Camera.fy"]; + float cx = fSettings["Camera.cx"]; + float cy = fSettings["Camera.cy"]; + + cv::Mat K = cv::Mat::eye(3,3,CV_32F); + K.at(0,0) = fx; + K.at(1,1) = fy; + K.at(0,2) = cx; + K.at(1,2) = cy; + K.copyTo(mK); + + cv::Mat DistCoef(4,1,CV_32F); + DistCoef.at(0) = fSettings["Camera.k1"]; + DistCoef.at(1) = fSettings["Camera.k2"]; + DistCoef.at(2) = fSettings["Camera.p1"]; + DistCoef.at(3) = fSettings["Camera.p2"]; + const float k3 = fSettings["Camera.k3"]; + if(k3!=0) + { + DistCoef.resize(5); + DistCoef.at(4) = k3; + } + DistCoef.copyTo(mDistCoef); + + mbf = fSettings["Camera.bf"]; + + Frame::mbInitialComputations = true; +} + +void Tracking::InformOnlyTracking(const bool &flag) +{ + mbOnlyTracking = flag; +} + + + +} //namespace ORB_SLAM diff --git a/for_orb_slam2/src/Viewer.cc b/for_orb_slam2/src/Viewer.cc new file mode 100644 index 00000000000..dec3204f535 --- /dev/null +++ b/for_orb_slam2/src/Viewer.cc @@ -0,0 +1,232 @@ +/** +* This file is part of ORB-SLAM2. +* +* Copyright (C) 2014-2016 Raúl Mur-Artal (University of Zaragoza) +* For more information see +* +* ORB-SLAM2 is free software: you can redistribute it and/or modify +* it under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* ORB-SLAM2 is distributed in the hope that it will be useful, +* but WITHOUT ANY WARRANTY; without even the implied warranty of +* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +* GNU General Public License for more details. +* +* You should have received a copy of the GNU General Public License +* along with ORB-SLAM2. If not, see . +*/ + +#include "Viewer.h" +#include + +#include + +namespace ORB_SLAM2 +{ + +Viewer::Viewer(System* pSystem, FrameDrawer *pFrameDrawer, MapDrawer *pMapDrawer, Tracking *pTracking, const string &strSettingPath): + mpSystem(pSystem), mpFrameDrawer(pFrameDrawer),mpMapDrawer(pMapDrawer), mpTracker(pTracking), + mbFinishRequested(false), mbFinished(true), mbStopped(true), mbStopRequested(false) +{ + cv::FileStorage fSettings(strSettingPath, cv::FileStorage::READ); + + float fps = fSettings["Camera.fps"]; + if(fps<1) + fps=30; + mT = 1e3/fps; + + mImageWidth = fSettings["Camera.width"]; + mImageHeight = fSettings["Camera.height"]; + if(mImageWidth<1 || mImageHeight<1) + { + mImageWidth = 640; + mImageHeight = 480; + } + + mViewpointX = fSettings["Viewer.ViewpointX"]; + mViewpointY = fSettings["Viewer.ViewpointY"]; + mViewpointZ = fSettings["Viewer.ViewpointZ"]; + mViewpointF = fSettings["Viewer.ViewpointF"]; +} + +void Viewer::Run() +{ + mbFinished = false; + mbStopped = false; + + pangolin::CreateWindowAndBind("ORB-SLAM2: Map Viewer",1024,768); + + // 3D Mouse handler requires depth testing to be enabled + glEnable(GL_DEPTH_TEST); + + // Issue specific OpenGl we might need + glEnable (GL_BLEND); + glBlendFunc (GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA); + + pangolin::CreatePanel("menu").SetBounds(0.0,1.0,0.0,pangolin::Attach::Pix(175)); + pangolin::Var menuFollowCamera("menu.Follow Camera",true,true); + pangolin::Var menuShowPoints("menu.Show Points",true,true); + pangolin::Var menuShowKeyFrames("menu.Show KeyFrames",true,true); + pangolin::Var menuShowGraph("menu.Show Graph",true,true); + pangolin::Var menuLocalizationMode("menu.Localization Mode",false,true); + pangolin::Var menuReset("menu.Reset",false,false); + + // Define Camera Render Object (for view / scene browsing) + pangolin::OpenGlRenderState s_cam( + pangolin::ProjectionMatrix(1024,768,mViewpointF,mViewpointF,512,389,0.1,1000), + pangolin::ModelViewLookAt(mViewpointX,mViewpointY,mViewpointZ, 0,0,0,0.0,-1.0, 0.0) + ); + + // Add named OpenGL viewport to window and provide 3D Handler + pangolin::View& d_cam = pangolin::CreateDisplay() + .SetBounds(0.0, 1.0, pangolin::Attach::Pix(175), 1.0, -1024.0f/768.0f) + .SetHandler(new pangolin::Handler3D(s_cam)); + + pangolin::OpenGlMatrix Twc; + Twc.SetIdentity(); + + cv::namedWindow("ORB-SLAM2: Current Frame"); + + bool bFollow = true; + bool bLocalizationMode = false; + + while(1) + { + glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT); + + mpMapDrawer->GetCurrentOpenGLCameraMatrix(Twc); + + if(menuFollowCamera && bFollow) + { + s_cam.Follow(Twc); + } + else if(menuFollowCamera && !bFollow) + { + s_cam.SetModelViewMatrix(pangolin::ModelViewLookAt(mViewpointX,mViewpointY,mViewpointZ, 0,0,0,0.0,-1.0, 0.0)); + s_cam.Follow(Twc); + bFollow = true; + } + else if(!menuFollowCamera && bFollow) + { + bFollow = false; + } + + if(menuLocalizationMode && !bLocalizationMode) + { + mpSystem->ActivateLocalizationMode(); + bLocalizationMode = true; + } + else if(!menuLocalizationMode && bLocalizationMode) + { + mpSystem->DeactivateLocalizationMode(); + bLocalizationMode = false; + } + + d_cam.Activate(s_cam); + glClearColor(1.0f,1.0f,1.0f,1.0f); + mpMapDrawer->DrawCurrentCamera(Twc); + if(menuShowKeyFrames || menuShowGraph) + mpMapDrawer->DrawKeyFrames(menuShowKeyFrames,menuShowGraph); + if(menuShowPoints) + mpMapDrawer->DrawMapPoints(); + + pangolin::FinishFrame(); + + cv::Mat im = mpFrameDrawer->DrawFrame(); + cv::imshow("ORB-SLAM2: Current Frame",im); + cv::waitKey(mT); + + if(menuReset) + { + menuShowGraph = true; + menuShowKeyFrames = true; + menuShowPoints = true; + menuLocalizationMode = false; + if(bLocalizationMode) + mpSystem->DeactivateLocalizationMode(); + bLocalizationMode = false; + bFollow = true; + menuFollowCamera = true; + mpSystem->Reset(); + menuReset = false; + } + + if(Stop()) + { + while(isStopped()) + { + usleep(3000); + } + } + + if(CheckFinish()) + break; + } + + SetFinish(); +} + +void Viewer::RequestFinish() +{ + unique_lock lock(mMutexFinish); + mbFinishRequested = true; +} + +bool Viewer::CheckFinish() +{ + unique_lock lock(mMutexFinish); + return mbFinishRequested; +} + +void Viewer::SetFinish() +{ + unique_lock lock(mMutexFinish); + mbFinished = true; +} + +bool Viewer::isFinished() +{ + unique_lock lock(mMutexFinish); + return mbFinished; +} + +void Viewer::RequestStop() +{ + unique_lock lock(mMutexStop); + if(!mbStopped) + mbStopRequested = true; +} + +bool Viewer::isStopped() +{ + unique_lock lock(mMutexStop); + return mbStopped; +} + +bool Viewer::Stop() +{ + unique_lock lock(mMutexStop); + unique_lock lock2(mMutexFinish); + + if(mbFinishRequested) + return false; + else if(mbStopRequested) + { + mbStopped = true; + mbStopRequested = false; + return true; + } + + return false; + +} + +void Viewer::Release() +{ + unique_lock lock(mMutexStop); + mbStopped = false; +} + +} diff --git a/include/Frame.h b/include/Frame.h index 572073f4036..cea3a8f9f16 100644 --- a/include/Frame.h +++ b/include/Frame.h @@ -316,6 +316,9 @@ class Frame // Assign keypoints to the grid for speed up feature matching (called in the constructor). void AssignFeaturesToGrid(); + // Sample grayscale at each keypoint from the given image + void SampleGrayValues(const cv::Mat &imGray); + bool mbIsSet; bool mbImuPreintegrated; @@ -354,6 +357,9 @@ class Frame cv::Mat imgLeft, imgRight; + // Per-keypoint grayscale intensity sampled from the source image + std::vector mvGrayValues; + void PrintPointDistribution(){ int left = 0, right = 0; int Nlim = (Nleft != -1) ? Nleft : N; diff --git a/include/G2oTypes.h b/include/G2oTypes.h index c6543f2afb4..ab6c90564f4 100644 --- a/include/G2oTypes.h +++ b/include/G2oTypes.h @@ -505,6 +505,11 @@ class EdgeInertial : public g2o::BaseMultiEdge<9,Vector9d> void computeError(); virtual void linearizeOplus(); + Vector9d GetErrorVector(){ + computeError(); + return _error; + } + Eigen::Matrix GetHessian(){ linearizeOplus(); Eigen::Matrix J; diff --git a/include/KeyFrame.h b/include/KeyFrame.h index 1b8d1c43921..818661946bf 100644 --- a/include/KeyFrame.h +++ b/include/KeyFrame.h @@ -384,6 +384,9 @@ class KeyFrame const std::vector mvDepth; // negative value for monocular points const cv::Mat mDescriptors; + // Per-keypoint grayscale values sampled from input image + std::vector mvGrayValues; + //BoW DBoW2::BowVector mBowVec; DBoW2::FeatureVector mFeatVec; diff --git a/include/LocalMapping.h b/include/LocalMapping.h index dd04a3d47ff..7c7889d3436 100644 --- a/include/LocalMapping.h +++ b/include/LocalMapping.h @@ -139,6 +139,8 @@ class LocalMapping void SearchInNeighbors(); void KeyFrameCulling(); + void ComputeLocalWindowReprojErrors(double &mean_err, double &max_err); + System *mpSystem; bool mbMonocular; @@ -195,6 +197,18 @@ class LocalMapping //DEBUG ofstream f_lm; + // Per-iteration CSV log + ofstream f_lm_csv; + long mLMIteration; + int mCsvKFCulled; // KFs culled in this iteration + double mCsvMeanReprojErr; + double mCsvMaxReprojErr; + double mCsvOptimMs; + int mCsvBADone; // 1 if Local BA was executed this iteration + int mCsvBAAborted; // 1 if Local BA was aborted mid-iteration + int mCsvBAIters; // actual g2o iterations performed + double mCsvFirstTimestamp; // first LocalMapping dataset timestamp for rel_time_s + }; } //namespace ORB_SLAM diff --git a/include/LoopClosing.h b/include/LoopClosing.h index c9ccb269ae4..6f535136dd0 100644 --- a/include/LoopClosing.h +++ b/include/LoopClosing.h @@ -31,6 +31,7 @@ #include #include #include +#include #include "Thirdparty/g2o/g2o/types/types_seven_dof_expmap.h" namespace ORB_SLAM3 @@ -118,6 +119,17 @@ class LoopClosing protected: + // ── Pipeline stats struct (must be declared before method decls that use it) ── + struct LcPipelineStats { + int bow_candidates = 0; + int max_bow_matches = 0; + int sim3_ransac_inliers = 0; + int proj_matches_coarse = 0; + int sim3_opt_inliers = 0; + int proj_matches_fine = 0; + int coincidences = 0; + }; + bool CheckNewKeyFrames(); @@ -126,7 +138,8 @@ class LoopClosing bool DetectAndReffineSim3FromLastKF(KeyFrame* pCurrentKF, KeyFrame* pMatchedKF, g2o::Sim3 &gScw, int &nNumProjMatches, std::vector &vpMPs, std::vector &vpMatchedMPs); bool DetectCommonRegionsFromBoW(std::vector &vpBowCand, KeyFrame* &pMatchedKF, KeyFrame* &pLastCurrentKF, g2o::Sim3 &g2oScw, - int &nNumCoincidences, std::vector &vpMPs, std::vector &vpMatchedMPs); + int &nNumCoincidences, std::vector &vpMPs, std::vector &vpMatchedMPs, + LcPipelineStats &outStats); bool DetectCommonRegionsFromLastKF(KeyFrame* pCurrentKF, KeyFrame* pMatchedKF, g2o::Sim3 &gScw, int &nNumProjMatches, std::vector &vpMPs, std::vector &vpMatchedMPs); int FindMatchesByProjection(KeyFrame* pCurrentKF, KeyFrame* pMatchedKFw, g2o::Sim3 &g2oScw, @@ -243,6 +256,26 @@ class LoopClosing #ifdef REGISTER_LOOP string mstrFolderLoop; #endif + + // ── CSV loop-closing logger ──────────────────────────────────────────── + void WriteLoopLog(const std::string& event_type, + const std::string& status, + double timestamp, + long unsigned int current_kf_id, + long long int matched_kf_id, + const LcPipelineStats& stats, + const g2o::Sim3& sim3, + int inliers, + double reprojection_error, + double detection_ms, + double optimization_ms); + LcPipelineStats mLastLoopDetStats; + LcPipelineStats mLastMergeDetStats; + std::ofstream mLcCsv; + std::mutex mMutexLcCsv; + double mLcFirstTimestamp = -1.0; + int mCsvLastCorrectedKFs = -1; + // ────────────────────────────────────────────────────────────────────── }; } //namespace ORB_SLAM diff --git a/include/MapPoint.h b/include/MapPoint.h index 29cb4203d0b..71753effb0c 100644 --- a/include/MapPoint.h +++ b/include/MapPoint.h @@ -197,6 +197,9 @@ class MapPoint Eigen::Vector3f mNormalVectorMerge; + // Grayscale intensity (0-255) sampled at the keypoint when MapPoint is created + unsigned char mGray = 128; + // Fopr inverse depth optimization double mInvDepth; double mInitU; diff --git a/include/Optimizer.h b/include/Optimizer.h index 5013f9bbc6b..2c9510dc3a9 100644 --- a/include/Optimizer.h +++ b/include/Optimizer.h @@ -46,6 +46,23 @@ class LoopClosing; class Optimizer { public: + struct InertialResidualStats + { + int count = 0; + double chi2_sum = 0.0; + double chi2_mean = -1.0; + double chi2_max = -1.0; + double rot_norm = -1.0; + double vel_norm = -1.0; + double pos_norm = -1.0; + double gyro_rw_chi2 = -1.0; + double acc_rw_chi2 = -1.0; + + int visual_count = 0; + double visual_chi2_sum = 0.0; + double visual_chi2_mean = -1.0; + double visual_chi2_max = -1.0; + }; void static BundleAdjustment(const std::vector &vpKF, const std::vector &vpMP, int nIterations = 5, bool *pbStopFlag=NULL, const unsigned long nLoopKF=0, @@ -54,11 +71,11 @@ class Optimizer const unsigned long nLoopKF=0, const bool bRobust = true); void static FullInertialBA(Map *pMap, int its, const bool bFixLocal=false, const unsigned long nLoopKF=0, bool *pbStopFlag=NULL, bool bInit=false, float priorG = 1e2, float priorA=1e6, Eigen::VectorXd *vSingVal = NULL, bool *bHess=NULL); - void static LocalBundleAdjustment(KeyFrame* pKF, bool *pbStopFlag, Map *pMap, int& num_fixedKF, int& num_OptKF, int& num_MPs, int& num_edges); + void static LocalBundleAdjustment(KeyFrame* pKF, bool *pbStopFlag, Map *pMap, int& num_fixedKF, int& num_OptKF, int& num_MPs, int& num_edges, int& num_iters, InertialResidualStats* pStats = NULL, InertialResidualStats* pStatsBefore = NULL); int static PoseOptimization(Frame* pFrame); - int static PoseInertialOptimizationLastKeyFrame(Frame* pFrame, bool bRecInit = false); - int static PoseInertialOptimizationLastFrame(Frame *pFrame, bool bRecInit = false); + int static PoseInertialOptimizationLastKeyFrame(Frame* pFrame, bool bRecInit = false, InertialResidualStats* pStats = NULL); + int static PoseInertialOptimizationLastFrame(Frame *pFrame, bool bRecInit = false, InertialResidualStats* pStats = NULL); // if bFixScale is true, 6DoF optimization (stereo,rgbd), 7DoF otherwise (mono) void static OptimizeEssentialGraph(Map* pMap, KeyFrame* pLoopKF, KeyFrame* pCurKF, @@ -83,7 +100,7 @@ class Optimizer // For inertial systems - void static LocalInertialBA(KeyFrame* pKF, bool *pbStopFlag, Map *pMap, int& num_fixedKF, int& num_OptKF, int& num_MPs, int& num_edges, bool bLarge = false, bool bRecInit = false); + void static LocalInertialBA(KeyFrame* pKF, bool *pbStopFlag, Map *pMap, int& num_fixedKF, int& num_OptKF, int& num_MPs, int& num_edges, int& num_iters, bool bLarge = false, bool bRecInit = false, InertialResidualStats* pStats = NULL, InertialResidualStats* pStatsBefore = NULL); void static MergeInertialBA(KeyFrame* pCurrKF, KeyFrame* pMergeKF, bool *pbStopFlag, Map *pMap, LoopClosing::KeyFrameAndPose &corrPoses); // Local BA in welding area when two maps are merged diff --git a/include/Settings.h b/include/Settings.h index beab7de2f7b..581623182d0 100644 --- a/include/Settings.h +++ b/include/Settings.h @@ -21,7 +21,7 @@ // Flag to activate the measurement of time in each process (track,localmap, place recognition). -//#define REGISTER_TIMES +#define REGISTER_TIMES #include "CameraModels/GeometricCamera.h" diff --git a/include/System.h b/include/System.h index 872c86e734d..a6857ad24ad 100644 --- a/include/System.h +++ b/include/System.h @@ -167,6 +167,14 @@ class System // See format details at: http://www.cvlibs.net/datasets/kitti/eval_odometry.php void SaveTrajectoryKITTI(const string &filename); + // Export all surviving MapPoints (x,y,z per line) to voxel/ + // Call after Shutdown() to ensure thread safety. + void SaveMapPoints(const std::string& filename); + + // Print the total number of surviving MapPoints in all maps to stdout. + // Call after Shutdown() to ensure thread safety. + void PrintMapPointCount() const; + // TODO: Save/Load functions // SaveMap(const string &filename); // LoadMap(const string &filename); diff --git a/include/Tracking.h b/include/Tracking.h index 863c98e4f29..2fc2ca8d14b 100644 --- a/include/Tracking.h +++ b/include/Tracking.h @@ -22,6 +22,7 @@ #include #include +#include #include "Viewer.h" #include "FrameDrawer.h" @@ -108,6 +109,9 @@ class Tracking float GetImageScale(); + void WriteFrameLog(); + void CloseFrameLog(); + #ifdef REGISTER_LOOP void RequestStop(); bool isStopped(); @@ -349,6 +353,95 @@ class Tracking double mTime_LocalMapTrack; double mTime_NewKF_Dec; + // ── Tracking Monitor ──────────────────────────────────────────────────── + // Per-frame log record; reset at the top of Track() each iteration. + struct FrameLog { + // identity + long unsigned int frame_id = 0; + double timestamp = 0.0; + long unsigned int kf_id = 0; + long unsigned int map_id = 0; + + // state + int state = 0; // eTrackingState value + int last_state = 0; + + // tracking path: 0=RefKF, 1=MotionModel, 2=IMU_MM, 3=Reloc, -1=none + int tracking_method = -1; + bool wider_window_used = false; + + // matches during initialization (SearchForInitialization, monocular only) + int init_matches = 0; + + // matches (filled inside TrackReferenceKeyFrame / TrackWithMotionModel) + int initial_matches = 0; + + // TrackLocalMap internals + int matches_before_tlm_opt = 0; + int outliers_before_tlm_opt = 0; + int matches_after_tlm_opt = 0; + int outliers_after_tlm_opt = 0; + + // final result (mnMatchesInliers) + int final_inliers = 0; + float outlier_ratio = 0.f; + + // optimizer type used in TrackLocalMap + // 0=PoseOptimization, 1=InertialLastFrame, 2=InertialLastKF + int opt_type = 0; + + // KF / map + bool need_new_kf = false; + bool new_kf_created = false; + int total_kf_in_map = 0; + int total_mp_in_map = 0; + int local_map_mp_count = 0; + int local_kf_count = 0; + bool map_updated = false; + int total_maps = 0; + + // relocalization + bool reloc_attempted = false; + bool reloc_success = false; + int reloc_candidates = 0; + int reloc_bow_pass = 0; + int reloc_pnp_inliers = 0; + + // IMU + bool imu_initialized = false; + bool imu_predicted = false; + float bias_acc_norm = 0.f; + float bias_gyro_norm = 0.f; + + // residual / correction diagnostics. -1 means unavailable for this frame. + double visual_chi2_mean = -1.0; + double visual_chi2_max = -1.0; + double imu_chi2_mean = -1.0; + double imu_chi2_max = -1.0; + double imu_rot_res_norm = -1.0; + double imu_vel_res_norm = -1.0; + double imu_pos_res_norm = -1.0; + double gyro_rw_chi2 = -1.0; + double acc_rw_chi2 = -1.0; + double pose_update_norm = -1.0; + double velocity_update_norm = -1.0; + double bias_update_norm = -1.0; + + // timing (ms) + double preintegration_ms = 0.0; + double pose_pred_ms = 0.0; + double track_local_map_ms = 0.0; + double need_new_kf_ms = 0.0; + double total_tracking_ms = 0.0; + }; + + FrameLog mCurLog; ///< reset every Track() call + ofstream mFrameLogFile; ///< per-frame CSV + bool mbFrameLogInit = false; + double mLogFirstTimestamp = -1.0; ///< timestamp of first logged frame (for rel_time_s) + void InitFrameLog(const std::string &path = "tracking_log.csv"); + // ── end Tracking Monitor ───────────────────────────────────────────────── + GeometricCamera* mpCamera, *mpCamera2; int initID, lastID; diff --git a/mono_euroc_v101_taligned.txt b/mono_euroc_v101_taligned.txt new file mode 100644 index 00000000000..4f27e7d42f9 --- /dev/null +++ b/mono_euroc_v101_taligned.txt @@ -0,0 +1,2801 @@ +1403715278812143104.000000 0.616588 1.159792 1.217456 +1403715278862142976.000000 0.619779 1.162237 1.225660 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3.999442 7.512390 -2.472725 +1403715608162142976.000000 4.003613 7.514460 -2.473070 +1403715608212143104.000000 4.001535 7.514838 -2.473556 +1403715608262143232.000000 3.998138 7.513351 -2.474472 +1403715608312143104.000000 4.001528 7.514803 -2.475671 +1403715608362142976.000000 4.001454 7.513764 -2.475920 +1403715608412143104.000000 3.999634 7.512028 -2.476963 +1403715608462142976.000000 4.001419 7.514160 -2.477245 +1403715608512143104.000000 4.001008 7.514530 -2.477460 +1403715608562142976.000000 4.001325 7.513497 -2.477976 +1403715608612143360.000000 4.001275 7.513338 -2.478274 +1403715608662142976.000000 4.000972 7.513135 -2.478797 +1403715608712143104.000000 4.001171 7.513584 -2.478819 +1403715608762143232.000000 4.000694 7.513724 -2.478944 +1403715608812143104.000000 4.000982 7.513514 -2.479049 +1403715608862142976.000000 4.001249 7.513090 -2.479095 +1403715608912143104.000000 4.000936 7.512924 -2.478903 +1403715608962142976.000000 4.001025 7.513373 -2.479234 +1403715609012143104.000000 4.000890 7.513446 -2.479563 +1403715609062142976.000000 4.000733 7.512954 -2.479635 +1403715609112143360.000000 4.000621 7.512925 -2.479405 +1403715609162142976.000000 4.000576 7.513312 -2.479497 +1403715609212143104.000000 4.000670 7.513713 -2.479727 +1403715609262143232.000000 4.000657 7.513015 -2.479423 +1403715609312143104.000000 4.000612 7.513121 -2.479690 +1403715609362142976.000000 4.000884 7.513109 -2.480169 \ No newline at end of file diff --git a/README.md b/origin_README.md similarity index 100% rename from README.md rename to origin_README.md diff --git a/result.pdf b/result.pdf new file mode 100644 index 00000000000..9d6ebdca90b Binary files /dev/null and b/result.pdf differ diff --git a/src/Frame.cc b/src/Frame.cc index a04c312ce07..59691b5bdc3 100644 --- a/src/Frame.cc +++ b/src/Frame.cc @@ -70,7 +70,8 @@ Frame::Frame(const Frame &frame) monoLeft(frame.monoLeft), monoRight(frame.monoRight), mvLeftToRightMatch(frame.mvLeftToRightMatch), mvRightToLeftMatch(frame.mvRightToLeftMatch), mvStereo3Dpoints(frame.mvStereo3Dpoints), mTlr(frame.mTlr), mRlr(frame.mRlr), mtlr(frame.mtlr), mTrl(frame.mTrl), - mTcw(frame.mTcw), mbHasPose(false), mbHasVelocity(false) + mTcw(frame.mTcw), mbHasPose(false), mbHasVelocity(false), + mvGrayValues(frame.mvGrayValues) { for(int i=0;i 0 ? N : 0, 128); + return; + } + mvGrayValues.resize(N); + for(int i = 0; i < N; i++){ + const cv::KeyPoint &kp = (Nleft == -1) ? mvKeysUn[i] + : (i < Nleft) ? mvKeys[i] + : mvKeysRight[i - Nleft]; + int px = cvRound(kp.pt.x), py = cvRound(kp.pt.y); + // For right-side keypoints in fisheye, sample from right image + const cv::Mat &img = (Nleft != -1 && i >= Nleft && !imgRight.empty()) ? imgRight : imGray; + if(px >= 0 && px < img.cols && py >= 0 && py < img.rows) + mvGrayValues[i] = img.at(py, px); + else + mvGrayValues[i] = 128; + } +} + void Frame::ExtractORB(int flag, const cv::Mat &im, const int x0, const int x1) { vector vLapping = {x0,x1}; @@ -1121,6 +1147,7 @@ Frame::Frame(const cv::Mat &imLeft, const cv::Mat &imRight, const double &timeSt UndistortKeyPoints(); + SampleGrayValues(imLeft); } void Frame::ComputeStereoFishEyeMatches() { diff --git a/src/KeyFrame.cc b/src/KeyFrame.cc index 293ab48182b..29a9bcb3ef4 100644 --- a/src/KeyFrame.cc +++ b/src/KeyFrame.cc @@ -49,7 +49,7 @@ KeyFrame::KeyFrame(Frame &F, Map *pMap, KeyFrameDatabase *pKFDB): mnLoopQuery(0), mnLoopWords(0), mnRelocQuery(0), mnRelocWords(0), mnBAGlobalForKF(0), mnPlaceRecognitionQuery(0), mnPlaceRecognitionWords(0), mPlaceRecognitionScore(0), fx(F.fx), fy(F.fy), cx(F.cx), cy(F.cy), invfx(F.invfx), invfy(F.invfy), mbf(F.mbf), mb(F.mb), mThDepth(F.mThDepth), N(F.N), mvKeys(F.mvKeys), mvKeysUn(F.mvKeysUn), - mvuRight(F.mvuRight), mvDepth(F.mvDepth), mDescriptors(F.mDescriptors.clone()), + mvuRight(F.mvuRight), mvDepth(F.mvDepth), mDescriptors(F.mDescriptors.clone()), mvGrayValues(F.mvGrayValues),// mvKeyColors(F.mvKeyColors), mBowVec(F.mBowVec), mFeatVec(F.mFeatVec), mnScaleLevels(F.mnScaleLevels), mfScaleFactor(F.mfScaleFactor), mfLogScaleFactor(F.mfLogScaleFactor), mvScaleFactors(F.mvScaleFactors), mvLevelSigma2(F.mvLevelSigma2), mvInvLevelSigma2(F.mvInvLevelSigma2), mnMinX(F.mnMinX), mnMinY(F.mnMinY), mnMaxX(F.mnMaxX), diff --git a/src/LocalMapping.cc b/src/LocalMapping.cc index 53df3324fae..8859e6200ff 100644 --- a/src/LocalMapping.cc +++ b/src/LocalMapping.cc @@ -26,9 +26,166 @@ #include #include +#include +#include +#include +#include +#include namespace ORB_SLAM3 { +namespace +{ +const char* TrackingStateNameForLog(Tracking::eTrackingState s) +{ + switch(s) + { + case Tracking::SYSTEM_NOT_READY: return "SYSTEM_NOT_READY"; + case Tracking::NO_IMAGES_YET: return "NO_IMAGES_YET"; + case Tracking::NOT_INITIALIZED: return "NOT_INITIALIZED"; + case Tracking::OK: return "OK"; + case Tracking::RECENTLY_LOST: return "RECENTLY_LOST"; + case Tracking::LOST: return "LOST"; + case Tracking::OK_KLT: return "OK_KLT"; + default: return "UNKNOWN"; + } +} + +struct KFCorrectionSnapshot +{ + Sophus::SE3f Tcw; + Eigen::Vector3f velocity; + IMU::Bias bias; + bool has_velocity = false; + bool has_bias = false; +}; + +double BiasDeltaNorm(const IMU::Bias &before, const IMU::Bias &after) +{ + const double dbax = static_cast(after.bax - before.bax); + const double dbay = static_cast(after.bay - before.bay); + const double dbaz = static_cast(after.baz - before.baz); + const double dbwx = static_cast(after.bwx - before.bwx); + const double dbwy = static_cast(after.bwy - before.bwy); + const double dbwz = static_cast(after.bwz - before.bwz); + return std::sqrt(dbax*dbax + dbay*dbay + dbaz*dbaz + + dbwx*dbwx + dbwy*dbwy + dbwz*dbwz); +} + +double RotationDeltaDeg(const Sophus::SE3f &before, const Sophus::SE3f &after) +{ + const Eigen::Matrix3f dR = after.rotationMatrix() * before.rotationMatrix().transpose(); + double cos_angle = (static_cast(dR.trace()) - 1.0) * 0.5; + cos_angle = std::max(-1.0, std::min(1.0, cos_angle)); + return std::acos(cos_angle) * 180.0 / 3.14159265358979323846; +} + +std::vector CollectCorrectionKeyFrames(KeyFrame* pKF, bool bInertialBA, bool bLarge) +{ + std::vector out; + std::set seen; + if(!pKF) + return out; + + Map* pMap = pKF->GetMap(); + if(!pMap) + return out; + auto addKF = [&](KeyFrame* pKFi) + { + if(!pKFi || pKFi->isBad() || pKFi->GetMap() != pMap || seen.count(pKFi)) + return; + seen.insert(pKFi); + out.push_back(pKFi); + }; + + addKF(pKF); + + if(bInertialBA) + { + const int maxOpt = bLarge ? 25 : 10; + const int Nd = std::min(static_cast(pMap->KeyFramesInMap()) - 2, maxOpt); + KeyFrame* pBack = pKF; + for(int i = 1; i < Nd && pBack && pBack->mPrevKF; ++i) + { + pBack = pBack->mPrevKF; + addKF(pBack); + } + } + else + { + const std::vector vNeighKFs = pKF->GetVectorCovisibleKeyFrames(); + for(size_t i = 0, iend = vNeighKFs.size(); i < iend; ++i) + addKF(vNeighKFs[i]); + } + + return out; +} + +std::map SnapshotKeyFrames(const std::vector &vpKFs) +{ + std::map snapshots; + for(size_t i = 0, iend = vpKFs.size(); i < iend; ++i) + { + KeyFrame* pKF = vpKFs[i]; + if(!pKF || pKF->isBad()) + continue; + + KFCorrectionSnapshot snap; + snap.Tcw = pKF->GetPose(); + snap.has_velocity = pKF->isVelocitySet(); + if(snap.has_velocity) + snap.velocity = pKF->GetVelocity(); + snap.has_bias = pKF->bImu; + if(snap.has_bias) + snap.bias = pKF->GetImuBias(); + snapshots[pKF->mnId] = snap; + } + return snapshots; +} + +void ComputeCorrectionStats(const std::map &before, + const std::vector &vpKFs, + double &bias_delta_norm, + double &velocity_delta_norm, + double &pose_correction_trans_norm, + double &pose_correction_rot_deg) +{ + bias_delta_norm = -1.0; + velocity_delta_norm = -1.0; + pose_correction_trans_norm = -1.0; + pose_correction_rot_deg = -1.0; + + for(size_t i = 0, iend = vpKFs.size(); i < iend; ++i) + { + KeyFrame* pKF = vpKFs[i]; + if(!pKF || pKF->isBad()) + continue; + + std::map::const_iterator it = before.find(pKF->mnId); + if(it == before.end()) + continue; + + const KFCorrectionSnapshot &snap = it->second; + const Sophus::SE3f TcwAfter = pKF->GetPose(); + const double trans_norm = static_cast((TcwAfter.translation() - snap.Tcw.translation()).norm()); + const double rot_deg = RotationDeltaDeg(snap.Tcw, TcwAfter); + pose_correction_trans_norm = std::max(pose_correction_trans_norm, trans_norm); + pose_correction_rot_deg = std::max(pose_correction_rot_deg, rot_deg); + + if(snap.has_velocity && pKF->isVelocitySet()) + { + const double vel_norm = static_cast((pKF->GetVelocity() - snap.velocity).norm()); + velocity_delta_norm = std::max(velocity_delta_norm, vel_norm); + } + + if(snap.has_bias && pKF->bImu) + { + const double bias_norm = BiasDeltaNorm(snap.bias, pKF->GetImuBias()); + bias_delta_norm = std::max(bias_delta_norm, bias_norm); + } + } +} +} LocalMapping::LocalMapping(System* pSys, Atlas *pAtlas, const float bMonocular, bool bInertial, const string &_strSeqName): mpSystem(pSys), mbMonocular(bMonocular), mbInertial(bInertial), mbResetRequested(false), mbResetRequestedActiveMap(false), mbFinishRequested(false), mbFinished(true), mpAtlas(pAtlas), bInitializing(false), @@ -49,6 +206,29 @@ LocalMapping::LocalMapping(System* pSys, Atlas *pAtlas, const float bMonocular, nLBA_abort = 0; #endif + // Open per-iteration CSV log + mLMIteration = 0; + mCsvKFCulled = 0; + mCsvMeanReprojErr = 0.0; + mCsvMaxReprojErr = 0.0; + mCsvOptimMs = 0.0; + mCsvBADone = 0; + mCsvBAAborted = 0; + mCsvBAIters = 0; + mCsvFirstTimestamp = -1.0; + f_lm_csv.open("localmapping_log.csv"); + f_lm_csv << "localmappingnumber,kf_id,track_frame_id,timestamp_ms," + "kf_insertion_ms," + "map_points_total,new_map_points,queue_length,kf_culled," + "mean_reprojection_error,max_reprojection_error," + "optimization_time_ms,ba_executed,ba_aborted,ba_iters,tracking_lost," + "source,event_type,timestamp,rel_time_s,map_id,tracking_state,imu_initialized," + "ba_type,num_opt_kf,num_fixed_kf,num_mappoints,num_edges," + "visual_chi2_before,visual_chi2_after,imu_chi2_before,imu_chi2_after," + "imu_chi2_mean,imu_chi2_max,imu_rot_res_mean,imu_vel_res_mean,imu_pos_res_mean," + "gyro_rw_chi2,acc_rw_chi2,bias_delta_norm,velocity_delta_norm," + "pose_correction_trans_norm,pose_correction_rot_deg," + "init_scale,init_bg_norm,init_ba_norm,init_prior_g,init_prior_a,init_full_ba\n"; } void LocalMapping::SetLoopCloser(LoopClosing* pLoopCloser) @@ -80,7 +260,10 @@ void LocalMapping::Run() std::chrono::steady_clock::time_point time_StartProcessKF = std::chrono::steady_clock::now(); #endif // BoW conversion and insertion in Map + std::chrono::steady_clock::time_point csv_t0 = std::chrono::steady_clock::now(); ProcessNewKeyFrame(); + double csvKFInsert_ms = std::chrono::duration_cast>( + std::chrono::steady_clock::now() - csv_t0).count(); #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_EndProcessKF = std::chrono::steady_clock::now(); @@ -89,7 +272,7 @@ void LocalMapping::Run() #endif // Check recent MapPoints - MapPointCulling(); + MapPointCulling();//開關 #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_EndMPCulling = std::chrono::steady_clock::now(); @@ -120,12 +303,26 @@ void LocalMapping::Run() int num_OptKF_BA = 0; int num_MPs_BA = 0; int num_edges_BA = 0; + string csvBAType = "none"; + Optimizer::InertialResidualStats csvBAStatsBefore; + Optimizer::InertialResidualStats csvBAStatsAfter; + std::vector csvCorrectionKFs; + std::map csvCorrectionBefore; + double csvBiasDeltaNorm = -1.0; + double csvVelocityDeltaNorm = -1.0; + double csvPoseCorrectionTransNorm = -1.0; + double csvPoseCorrectionRotDeg = -1.0; + mCsvOptimMs = 0.0; + mCsvMeanReprojErr = 0.0; + mCsvMaxReprojErr = 0.0; + mCsvBADone = 0; + mCsvBAAborted = 0; + mCsvBAIters = 0; if(!CheckNewKeyFrames() && !stopRequested()) { if(mpAtlas->KeyFramesInMap()>2) { - if(mbInertial && mpCurrentKeyFrame->GetMap()->isImuInitialized()) { float dist = (mpCurrentKeyFrame->mPrevKF->GetCameraCenter() - mpCurrentKeyFrame->GetCameraCenter()).norm() + @@ -146,15 +343,40 @@ void LocalMapping::Run() } bool bLarge = ((mpTracker->GetMatchesInliers()>75)&&mbMonocular)||((mpTracker->GetMatchesInliers()>100)&&!mbMonocular); - Optimizer::LocalInertialBA(mpCurrentKeyFrame, &mbAbortBA, mpCurrentKeyFrame->GetMap(),num_FixedKF_BA,num_OptKF_BA,num_MPs_BA,num_edges_BA, bLarge, !mpCurrentKeyFrame->GetMap()->GetIniertialBA2()); + csvBAType = "LocalInertialBA"; + csvCorrectionKFs = CollectCorrectionKeyFrames(mpCurrentKeyFrame, true, bLarge); + csvCorrectionBefore = SnapshotKeyFrames(csvCorrectionKFs); + auto csv_ba_t0 = std::chrono::steady_clock::now(); + Optimizer::LocalInertialBA(mpCurrentKeyFrame, &mbAbortBA, mpCurrentKeyFrame->GetMap(),num_FixedKF_BA,num_OptKF_BA,num_MPs_BA,num_edges_BA, mCsvBAIters, bLarge, !mpCurrentKeyFrame->GetMap()->GetIniertialBA2(), &csvBAStatsAfter, &csvBAStatsBefore); + mCsvOptimMs = std::chrono::duration_cast>( + std::chrono::steady_clock::now() - csv_ba_t0).count(); b_doneLBA = true; } else { - Optimizer::LocalBundleAdjustment(mpCurrentKeyFrame,&mbAbortBA, mpCurrentKeyFrame->GetMap(),num_FixedKF_BA,num_OptKF_BA,num_MPs_BA,num_edges_BA); + csvBAType = "LocalBundleAdjustment"; + csvCorrectionKFs = CollectCorrectionKeyFrames(mpCurrentKeyFrame, false, false); + csvCorrectionBefore = SnapshotKeyFrames(csvCorrectionKFs); + auto csv_ba_t0 = std::chrono::steady_clock::now(); + Optimizer::LocalBundleAdjustment(mpCurrentKeyFrame,&mbAbortBA, mpCurrentKeyFrame->GetMap(),num_FixedKF_BA,num_OptKF_BA,num_MPs_BA,num_edges_BA, mCsvBAIters, &csvBAStatsAfter, &csvBAStatsBefore); + mCsvOptimMs = std::chrono::duration_cast>( + std::chrono::steady_clock::now() - csv_ba_t0).count(); b_doneLBA = true; } + mCsvBADone = b_doneLBA ? 1 : 0; + mCsvBAAborted = (b_doneLBA && mbAbortBA) ? 1 : 0; + if(b_doneLBA) + { + ComputeCorrectionStats(csvCorrectionBefore, csvCorrectionKFs, + csvBiasDeltaNorm, + csvVelocityDeltaNorm, + csvPoseCorrectionTransNorm, + csvPoseCorrectionRotDeg); + } + + if(b_doneLBA && !mbAbortBA) + ComputeLocalWindowReprojErrors(mCsvMeanReprojErr, mCsvMaxReprojErr); } #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_EndLBA = std::chrono::steady_clock::now(); @@ -188,7 +410,8 @@ void LocalMapping::Run() // Check redundant local Keyframes - KeyFrameCulling(); + mCsvKFCulled = 0; + KeyFrameCulling();//開關 #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_EndKFCulling = std::chrono::steady_clock::now(); @@ -246,7 +469,7 @@ void LocalMapping::Run() vdLBASync_ms.push_back(timeKFCulling_ms); vdKFCullingSync_ms.push_back(timeKFCulling_ms); #endif - +//開關loopclosing的插入keyframe，讓localmapping的迴圈更快結束，方便debug localmapping的部分 mpLoopCloser->InsertKeyFrame(mpCurrentKeyFrame); #ifdef REGISTER_TIMES @@ -255,6 +478,92 @@ void LocalMapping::Run() double timeLocalMap = std::chrono::duration_cast >(time_EndLocalMap - time_StartProcessKF).count(); vdLMTotal_ms.push_back(timeLocalMap); #endif + + // Write CSV row + if(f_lm_csv.is_open()) + { + auto csv_wall = std::chrono::system_clock::now(); + long long ts_ms = std::chrono::duration_cast( + csv_wall.time_since_epoch()).count(); + + int queueLen; + { + unique_lock lk(mMutexNewKFs); + queueLen = (int)mlNewKeyFrames.size(); + } + + int csvTrackingLost = (mpTracker && + (mpTracker->mState == Tracking::LOST || + mpTracker->mState == Tracking::RECENTLY_LOST)) ? 1 : 0; + const double kfTimestamp = mpCurrentKeyFrame->mTimeStamp; + if(mCsvFirstTimestamp < 0.0) + mCsvFirstTimestamp = kfTimestamp; + const double relTimeS = kfTimestamp - mCsvFirstTimestamp; + Map* pCsvMap = mpCurrentKeyFrame->GetMap(); + const long unsigned int csvMapId = pCsvMap ? pCsvMap->GetId() : 0; + const bool csvImuInitialized = pCsvMap && pCsvMap->isImuInitialized(); + const string csvEventType = b_doneLBA ? + (csvBAType == "LocalInertialBA" ? "LOCAL_INERTIAL_BA" : "LOCAL_BA") : + "KF_PROCESSED"; + const char* csvTrackingState = mpTracker ? TrackingStateNameForLog(mpTracker->mState) : "UNKNOWN"; + + f_lm_csv + << ++mLMIteration << "," + << (long long)mpCurrentKeyFrame->mnId << "," + << (long long)mpCurrentKeyFrame->mnFrameId << "," + << ts_ms << "," + << std::fixed << std::setprecision(3) + << csvKFInsert_ms << "," + << (int)mpAtlas->MapPointsInMap() << "," + << (int)mlpRecentAddedMapPoints.size() << "," + << queueLen << "," + << mCsvKFCulled << "," + << std::setprecision(4) + << mCsvMeanReprojErr << "," + << mCsvMaxReprojErr << "," + << std::setprecision(3) + << mCsvOptimMs << "," + << mCsvBADone << "," + << mCsvBAAborted << "," + << mCsvBAIters << "," + << csvTrackingLost << "," + << "LocalMapping" << "," + << csvEventType << "," + << std::setprecision(6) + << kfTimestamp << "," + << relTimeS << "," + << csvMapId << "," + << csvTrackingState << "," + << (csvImuInitialized ? "yes" : "no") << "," + << csvBAType << "," + << num_OptKF_BA << "," + << num_FixedKF_BA << "," + << num_MPs_BA << "," + << num_edges_BA << "," + << csvBAStatsBefore.visual_chi2_mean << "," + << csvBAStatsAfter.visual_chi2_mean << "," + << csvBAStatsBefore.chi2_mean << "," + << csvBAStatsAfter.chi2_mean << "," + << csvBAStatsAfter.chi2_mean << "," + << csvBAStatsAfter.chi2_max << "," + << csvBAStatsAfter.rot_norm << "," + << csvBAStatsAfter.vel_norm << "," + << csvBAStatsAfter.pos_norm << "," + << csvBAStatsAfter.gyro_rw_chi2 << "," + << csvBAStatsAfter.acc_rw_chi2 << "," + << csvBiasDeltaNorm << "," + << csvVelocityDeltaNorm << "," + << csvPoseCorrectionTransNorm << "," + << csvPoseCorrectionRotDeg << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << 0 << "\n"; + if(mLMIteration % 30 == 0) // flush every 30 iterations + f_lm_csv.flush(); + } } else if(Stop() && !mbBadImu) { @@ -278,6 +587,20 @@ void LocalMapping::Run() usleep(3000); } + if(f_lm_csv.is_open()) + { + f_lm_csv << "#final_keyframes," << mpAtlas->KeyFramesInMap() << "\n"; + // Write surviving KF list + f_lm_csv << "#survive_kf_id,survive_track_frame_id\n"; + auto vpSurvived = mpAtlas->GetAllKeyFrames(); + std::sort(vpSurvived.begin(), vpSurvived.end(), + [](KeyFrame* a, KeyFrame* b){ return a->mnId < b->mnId; }); + for(auto* pKF : vpSurvived) + if(pKF && !pKF->isBad()) + f_lm_csv << pKF->mnId << "," << pKF->mnFrameId << "\n"; + f_lm_csv.flush(); + f_lm_csv.close(); + } SetFinish(); } @@ -388,7 +711,8 @@ void LocalMapping::MapPointCulling() void LocalMapping::CreateNewMapPoints() { // Retrieve neighbor keyframes in covisibility graph - int nn = 10; + // int nn = 10;// ← 20（stereo 增加鄰近 KF 搜尋數）開關 + int nn = 1; // For stereo inertial case if(mbMonocular) nn=30; @@ -433,9 +757,14 @@ void LocalMapping::CreateNewMapPoints() // Search matches with epipolar restriction and triangulate for(size_t i=0; i0 && CheckNewKeyFrames()) + if(i>0 && CheckNewKeyFrames())//開關原版 return; + // if(i>=2 )//開關 + // return; + + + KeyFrame* pKF2 = vpNeighKFs[i]; GeometricCamera* pCamera1 = mpCurrentKeyFrame->mpCamera, *pCamera2 = pKF2->mpCamera; @@ -692,6 +1021,8 @@ void LocalMapping::CreateNewMapPoints() // Triangulation is succesfull MapPoint* pMP = new MapPoint(x3D, mpCurrentKeyFrame, mpAtlas->GetCurrentMap()); + if(idx1 >= 0 && idx1 < (int)mpCurrentKeyFrame->mvGrayValues.size())//將當前 KeyFrame（關鍵幀）中的灰度值 (Gray-scale value) 同步給 MapPoint（地圖點） + pMP->mGray = mpCurrentKeyFrame->mvGrayValues[idx1]; if (bPointStereo) countStereo++; @@ -732,7 +1063,7 @@ void LocalMapping::SearchInNeighbors() // Extend to some second neighbors if abort is not requested for(int i=0, imax=vpTargetKFs.size(); i vpSecondNeighKFs = vpTargetKFs[i]->GetBestCovisibilityKeyFrames(20); + const vector vpSecondNeighKFs = vpTargetKFs[i]->GetBestCovisibilityKeyFrames(20);//20這裡是將第一層鄰居的前五個鄰居加入到第二層鄰居中,開關這裡可以讓 localmapping 的迴圈更快結束，方便 debug localmapping 的部分 for(vector::const_iterator vit2=vpSecondNeighKFs.begin(), vend2=vpSecondNeighKFs.end(); vit2!=vend2; vit2++) { KeyFrame* pKFi2 = *vit2; @@ -963,7 +1294,7 @@ void LocalMapping::KeyFrameCulling() } nMPs++; - if(pMP->Observations()>thObs) + if(pMP->Observations()>thObs)//開關 { const int &scaleLevel = (pKF -> NLeft == -1) ? pKF->mvKeysUn[i].octave : (i < pKF -> NLeft) ? pKF -> mvKeys[i].octave @@ -994,11 +1325,11 @@ void LocalMapping::KeyFrameCulling() if(scaleLeveli<=scaleLevel+1) { nObs++; - if(nObs>thObs) + if(nObs>thObs)//開關 break; } } - if(nObs>thObs) + if(nObs>thObs)//開關 { nRedundantObservations++; } @@ -1029,6 +1360,7 @@ void LocalMapping::KeyFrameCulling() pKF->mNextKF = NULL; pKF->mPrevKF = NULL; pKF->SetBadFlag(); + ++mCsvKFCulled; } else if(!mpCurrentKeyFrame->GetMap()->GetIniertialBA2() && ((pKF->GetImuPosition()-pKF->mPrevKF->GetImuPosition()).norm()<0.02) && (t<3)) { @@ -1038,12 +1370,14 @@ void LocalMapping::KeyFrameCulling() pKF->mNextKF = NULL; pKF->mPrevKF = NULL; pKF->SetBadFlag(); + ++mCsvKFCulled; } } } else { pKF->SetBadFlag(); + ++mCsvKFCulled; } } if((count > 20 && mbAbortBA) || count>100) @@ -1192,83 +1526,85 @@ void LocalMapping::InitializeIMU(float priorG, float priorA, bool bFIBA) if(mpAtlas->KeyFramesInMap() lpKF; KeyFrame* pKF = mpCurrentKeyFrame; - while(pKF->mPrevKF) + while(pKF->mPrevKF)//mPrevKF 是一條單向鏈，從最新指向最舊，只包含有預積分連接的 KF。 { - lpKF.push_front(pKF); - pKF = pKF->mPrevKF; + lpKF.push_front(pKF);// 插到最前面 + pKF = pKF->mPrevKF; // 往前一個 } - lpKF.push_front(pKF); + lpKF.push_front(pKF);// 把最舊的 KF 也加進去 vector vpKF(lpKF.begin(),lpKF.end()); - if(vpKF.size()mTimeStamp; - if(mpCurrentKeyFrame->mTimeStamp-mFirstTsmTimeStamp;//vpKF.front() = 最舊的 KF（KF0） + if(mpCurrentKeyFrame->mTimeStamp-mFirstTsGetMap()->isImuInitialized()) { Eigen::Matrix3f Rwg; Eigen::Vector3f dirG; - dirG.setZero(); - for(vector::iterator itKF = vpKF.begin(); itKF!=vpKF.end(); itKF++) + dirG.setZero();//dirG：一個用來累積估計重力方向的向量，初始為零 + for(vector::iterator itKF = vpKF.begin(); itKF!=vpKF.end(); itKF++)//遍歷所有參與初始化的 KF，利用它們的預積分 IMU 資訊來估計重力方向和 KF 的速度 { - if (!(*itKF)->mpImuPreintegrated) - continue; + if (!(*itKF)->mpImuPreintegrated)// 沒有 preintegration 資料 + continue;//跳過無效 KF if (!(*itKF)->mPrevKF) - continue; + continue;//跳過無效 KF - dirG -= (*itKF)->mPrevKF->GetImuRotation() * (*itKF)->mpImuPreintegrated->GetUpdatedDeltaVelocity(); - Eigen::Vector3f _vel = ((*itKF)->GetImuPosition() - (*itKF)->mPrevKF->GetImuPosition())/(*itKF)->mpImuPreintegrated->dT; - (*itKF)->SetVelocity(_vel); - (*itKF)->mPrevKF->SetVelocity(_vel); + dirG -= (*itKF)->mPrevKF->GetImuRotation() * (*itKF)->mpImuPreintegrated->GetUpdatedDeltaVelocity();//累加重力方向，GetUpdatedDeltaVelocity()在 IMU body frame 下，從 prevKF 到此 KF 的速度變化 ΔV + Eigen::Vector3f _vel = ((*itKF)->GetImuPosition() - (*itKF)->mPrevKF->GetImuPosition())/(*itKF)->mpImuPreintegrated->dT;//粗估速度 _vel ：平均速度估計（位移 / 時間） + (*itKF)->SetVelocity(_vel);//把粗估速度存回 KF，供後續優化使用 + (*itKF)->mPrevKF->SetVelocity(_vel);//把粗估速度也存回 prevKF，供後續優化使用 } - dirG = dirG/dirG.norm(); - Eigen::Vector3f gI(0.0f, 0.0f, -1.0f); - Eigen::Vector3f v = gI.cross(dirG); + dirG = dirG/dirG.norm();//歸一化重力方向 變成單位向量，只保留方向。 + Eigen::Vector3f gI(0.0f, 0.0f, -1.0f);//gI：IMU座標系下的重力方向，初始為(0,0,-1) 定義標準重力方向:理想世界座標系下，重力應該是沿 -Z 軸，這是目標方向。 + Eigen::Vector3f v = gI.cross(dirG);// 旋轉軸 const float nv = v.norm(); - const float cosg = gI.dot(dirG); - const float ang = acos(cosg); - Eigen::Vector3f vzg = v*ang/nv; - Rwg = Sophus::SO3f::exp(vzg).matrix(); - mRwg = Rwg.cast(); - mTinit = mpCurrentKeyFrame->mTimeStamp-mFirstTs; + const float cosg = gI.dot(dirG);//cos(θ) + const float ang = acos(cosg);// θ（旋轉角） + Eigen::Vector3f vzg = v*ang/nv;//組成軸角向量 一個向量同時編碼了轉哪個方向和轉多少角度。 + Rwg = Sophus::SO3f::exp(vzg).matrix();//轉成SO(3) 旋轉矩陣 + //Rwg 記錄了「需要旋轉多少」才能讓 -Z 對齊重力 + mRwg = Rwg.cast();//轉成 double 精度並儲存結果 + mTinit = mpCurrentKeyFrame->mTimeStamp-mFirstTs;//記錄此次初始化的時間跨度，供外層決定下一階段 } else { - mRwg = Eigen::Matrix3d::Identity(); - mbg = mpCurrentKeyFrame->GetGyroBias().cast(); - mba = mpCurrentKeyFrame->GetAccBias().cast(); + mRwg = Eigen::Matrix3d::Identity();//重力方向在第一次初始化時已經對齊過了，世界座標系的 -Z 已經是重力方向，不需要再旋轉，所以 Rwg = 單位矩陣（不旋轉）。 + mbg = mpCurrentKeyFrame->GetGyroBias().cast();//繼承已有的 bias 估計 + mba = mpCurrentKeyFrame->GetAccBias().cast();//繼承已有的 bias 估計 } mScale=1.0; - mInitTime = mpTracker->mLastFrame.mTimeStamp-vpKF.front()->mTimeStamp; + mInitTime = mpTracker->mLastFrame.mTimeStamp-vpKF.front()->mTimeStamp;//未使用到的變數 vpKF.front()（最舊 KF）mLastFrame（Tracker 的最後一幀，包含非 KF 的普通幀） std::chrono::steady_clock::time_point t0 = std::chrono::steady_clock::now(); Optimizer::InertialOptimization(mpAtlas->GetCurrentMap(), mRwg, mScale, mbg, mba, mbMonocular, infoInertial, false, false, priorG, priorA); std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now(); - if (mScale<1e-1) + if (mScale<1e-1)//檢查尺度有沒有被inertialoptimization弄壞 { cout << "scale too small" << endl; bInitializing=false; @@ -1277,31 +1613,31 @@ void LocalMapping::InitializeIMU(float priorG, float priorA, bool bFIBA) // Before this line we are not changing the map { - unique_lock lock(mpAtlas->GetCurrentMap()->mMutexMapUpdate); + unique_lock lock(mpAtlas->GetCurrentMap()->mMutexMapUpdate);//在這之前只是計算，不動地圖。從這行開始要修改地圖，需要加鎖防止 Tracking 同時讀取。 if ((fabs(mScale - 1.f) > 0.00001) || !mbMonocular) { - Sophus::SE3f Twg(mRwg.cast().transpose(), Eigen::Vector3f::Zero()); - mpAtlas->GetCurrentMap()->ApplyScaledRotation(Twg, mScale, true); - mpTracker->UpdateFrameIMU(mScale, vpKF[0]->GetImuBias(), mpCurrentKeyFrame); + Sophus::SE3f Twg(mRwg.cast().transpose(), Eigen::Vector3f::Zero());//建立重力對齊變換 + mpAtlas->GetCurrentMap()->ApplyScaledRotation(Twg, mScale, true);//對地圖裡所有 KF 位姿、速度、MapPoint 位置同時套用：旋轉 Twg（重力對齊） 縮放 mScale（尺度修正） + mpTracker->UpdateFrameIMU(mScale, vpKF[0]->GetImuBias(), mpCurrentKeyFrame);//把 scale 和 bias 同步回 Tracking 執行緒的當前幀，確保兩個執行緒狀態一致。 } // Check if initialization OK - if (!mpAtlas->isImuInitialized()) + if (!mpAtlas->isImuInitialized())// 只在第一次初始化時執行（VIBA 1/2 時 isImuInitialized() 已經是 true，跳過）。 for (int i = 0; i < N; i++) { KeyFrame *pKF2 = vpKF[i]; - pKF2->bImu = true; + pKF2->bImu = true;//// 標記這個 KF 參與了 IMU 初始化 bImu = true 讓後續的優化（BA）知道這些 KF 可以加入 IMU 約束邊。 } } - + //標記 IMU 初始化完成 mpTracker->UpdateFrameIMU(1.0,vpKF[0]->GetImuBias(),mpCurrentKeyFrame); if (!mpAtlas->isImuInitialized()) { - mpAtlas->SetImuInitialized(); - mpTracker->t0IMU = mpTracker->mCurrentFrame.mTimeStamp; - mpCurrentKeyFrame->bImu = true; + mpAtlas->SetImuInitialized(); // 全局旗標設為 true + mpTracker->t0IMU = mpTracker->mCurrentFrame.mTimeStamp;// 記錄初始化時間點 + mpCurrentKeyFrame->bImu = true;// 當前 KF 標記為 IMU KF } std::chrono::steady_clock::time_point t4 = std::chrono::steady_clock::now(); - if (bFIBA) + if (bFIBA)//true，意味著每次初始化都會跑 Full Inertial BA。 { if (priorA!=0.f) Optimizer::FullInertialBA(mpAtlas->GetCurrentMap(), 100, false, mpCurrentKeyFrame->mnId, NULL, true, priorG, priorA); @@ -1311,6 +1647,77 @@ void LocalMapping::InitializeIMU(float priorG, float priorA, bool bFIBA) std::chrono::steady_clock::time_point t5 = std::chrono::steady_clock::now(); + if(f_lm_csv.is_open() && mpCurrentKeyFrame) + { + const double kfTimestamp = mpCurrentKeyFrame->mTimeStamp; + if(mCsvFirstTimestamp < 0.0) + mCsvFirstTimestamp = kfTimestamp; + Map* pCsvMap = mpCurrentKeyFrame->GetMap(); + const long unsigned int csvMapId = pCsvMap ? pCsvMap->GetId() : 0; + const bool csvImuInitialized = pCsvMap && pCsvMap->isImuInitialized(); + const double initOptimMs = std::chrono::duration_cast>( + t5 - t0).count(); + const int queueLen = KeyframesInQueue(); + const char* csvTrackingState = mpTracker ? TrackingStateNameForLog(mpTracker->mState) : "UNKNOWN"; + const string csvBAType = bFIBA ? "InertialOptimizationInit+FullInertialBA" : "InertialOptimizationInit"; + + f_lm_csv + << ++mLMIteration << "," + << (long long)mpCurrentKeyFrame->mnId << "," + << (long long)mpCurrentKeyFrame->mnFrameId << "," + << std::fixed << std::setprecision(3) + << kfTimestamp * 1000.0 << "," + << -1.0 << "," + << (int)mpAtlas->MapPointsInMap() << "," + << (int)mlpRecentAddedMapPoints.size() << "," + << queueLen << "," + << 0 << "," + << std::setprecision(4) + << -1.0 << "," + << -1.0 << "," + << std::setprecision(3) + << initOptimMs << "," + << 1 << "," + << 0 << "," + << (bFIBA ? 100 : -1) << "," + << 0 << "," + << "LocalMapping" << "," + << "IMU_INITIALIZATION" << "," + << std::setprecision(6) + << kfTimestamp << "," + << (kfTimestamp - mCsvFirstTimestamp) << "," + << csvMapId << "," + << csvTrackingState << "," + << (csvImuInitialized ? "yes" : "no") << "," + << csvBAType << "," + << N << "," + << -1 << "," + << (int)mpAtlas->MapPointsInMap() << "," + << -1 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << -1.0 << "," + << mScale << "," + << mbg.norm() << "," + << mba.norm() << "," + << priorG << "," + << priorA << "," + << (bFIBA ? 1 : 0) << "\n"; + f_lm_csv.flush(); + } + Verbose::PrintMess("Global Bundle Adjustment finished\nUpdating map ...", Verbose::VERBOSITY_NORMAL); // Get Map Mutex @@ -1326,7 +1733,7 @@ void LocalMapping::InitializeIMU(float priorG, float priorA, bool bFIBA) lpKF.push_back(mpCurrentKeyFrame); } - // Correct keyframes starting at map first keyframe + // Correct keyframes starting at map first keyframe 這行用它來初始化 lpKFtoCheck，作為後續廣度優先遍歷（BFS）整棵 KF spanning tree 的起點： list lpKFtoCheck(mpAtlas->GetCurrentMap()->mvpKeyFrameOrigins.begin(),mpAtlas->GetCurrentMap()->mvpKeyFrameOrigins.end()); while(!lpKFtoCheck.empty()) @@ -1382,13 +1789,13 @@ void LocalMapping::InitializeIMU(float priorG, float priorA, bool bFIBA) { MapPoint* pMP = vpMPs[i]; - if(pMP->isBad()) + if(pMP->isBad())//跳過無效 MP continue; - if(pMP->mnBAGlobalForKF==GBAid) + if(pMP->mnBAGlobalForKF==GBAid)//mnBAGlobalForKF 記錄這個 MP 上次被哪次 BA 優化（用 KF id 標記）,GBAid 這次 BA 的 id（mpCurrentKeyFrame->mnId） { // If optimized by Global BA, just update - pMP->SetWorldPos(pMP->mPosGBA); + pMP->SetWorldPos(pMP->mPosGBA);// mPosGBA BA 優化後的新位置 } else { @@ -1408,20 +1815,20 @@ void LocalMapping::InitializeIMU(float priorG, float priorA, bool bFIBA) Verbose::PrintMess("Map updated!", Verbose::VERBOSITY_NORMAL); - mnKFs=vpKF.size(); - mIdxInit++; + mnKFs=vpKF.size();// 記錄這次初始化用了幾個 KF + mIdxInit++;// 初始化執行次數 +1（粗初始化/VIBA1/VIBA2 各算一次） - for(list::iterator lit = mlNewKeyFrames.begin(), lend=mlNewKeyFrames.end(); lit!=lend; lit++) + for(list::iterator lit = mlNewKeyFrames.begin(), lend=mlNewKeyFrames.end(); lit!=lend; lit++)//清理初始化期間積累的新 KF { - (*lit)->SetBadFlag(); - delete *lit; + (*lit)->SetBadFlag();// 標記為壞 KF + delete *lit;// 釋放記憶體 } - mlNewKeyFrames.clear(); + mlNewKeyFrames.clear();//mlNewKeyFrames 是初始化執行期間 Tracking 送進來但還沒處理的 KF。 這些 KF 在初始化過程中沒有被納入優化，位姿可能不一致，直接丟棄讓系統重新建立。 - mpTracker->mState=Tracking::OK; - bInitializing = false; + mpTracker->mState=Tracking::OK;// 通知 Tracking 初始化完成，可以繼續追蹤 + bInitializing = false;// 解除初始化鎖，LocalMapping 恢復正常工作 bInitializing = true 期間，LocalMapping 會拒絕某些操作（如 Loop Closing 觸發的 BA），這裡解除。 - mpCurrentKeyFrame->GetMap()->IncreaseChangeIndex(); + mpCurrentKeyFrame->GetMap()->IncreaseChangeIndex();// 通知地圖已變更 地圖的 ChangeIndex +1，讓 Tracking 偵測到地圖有更新： return; } @@ -1519,4 +1926,57 @@ KeyFrame* LocalMapping::GetCurrKF() return mpCurrentKeyFrame; } +void LocalMapping::ComputeLocalWindowReprojErrors(double &mean_err, double &max_err) +{ + mean_err = 0.0; + max_err = 0.0; + if(!mpCurrentKeyFrame) return; + + // Local window: current KF + its best covisible KFs + vector vpLocalKFs = mpCurrentKeyFrame->GetBestCovisibilityKeyFrames(10); + vpLocalKFs.push_back(mpCurrentKeyFrame); + + double sum_err = 0.0; + int count = 0; + + for(KeyFrame* pKF : vpLocalKFs) + { + if(!pKF || pKF->isBad()) continue; + + Sophus::SE3f Tcw = pKF->GetPose(); + const float fx = pKF->fx, fy = pKF->fy; + const float cx = pKF->cx, cy = pKF->cy; + + const vector vpMPs = pKF->GetMapPointMatches(); + // For stereo/fisheye rigs, only consider the left camera keypoints + int nLeft = (pKF->NLeft == -1) ? (int)vpMPs.size() : pKF->NLeft; + + for(int i = 0; i < nLeft && i < (int)vpMPs.size(); ++i) + { + MapPoint* pMP = vpMPs[i]; + if(!pMP || pMP->isBad()) continue; + + Eigen::Vector3f Pw = pMP->GetWorldPos(); + Eigen::Vector3f Pc = Tcw * Pw; + if(Pc(2) <= 0.0f) continue; + + float invz = 1.0f / Pc(2); + float u = fx * Pc(0) * invz + cx; + float v = fy * Pc(1) * invz + cy; + + const cv::KeyPoint& kp = pKF->mvKeysUn[i]; + float du = u - kp.pt.x; + float dv = v - kp.pt.y; + double err = std::sqrt((double)(du*du + dv*dv)); + + sum_err += err; + if(err > max_err) max_err = err; + ++count; + } + } + + if(count > 0) + mean_err = sum_err / count; +} + } //namespace ORB_SLAM diff --git a/src/LoopClosing.cc b/src/LoopClosing.cc index 319afb551b4..42ef150e8b0 100644 --- a/src/LoopClosing.cc +++ b/src/LoopClosing.cc @@ -27,6 +27,10 @@ #include #include +#include +#include +#include +#include namespace ORB_SLAM3 @@ -74,6 +78,26 @@ LoopClosing::LoopClosing(Atlas *pAtlas, KeyFrameDatabase *pDB, ORBVocabulary *pV mstrFolderSubTraj = "SubTrajectories/"; mnNumCorrection = 0; mnCorrectionGBA = 0; + + // Open loop-closing CSV log + mLcCsv.open("loop_closing_log.csv"); + if(mLcCsv.is_open()) + { + mLcCsv << "event_type,status,timestamp,current_kf_id,matched_kf_id," + "bow_candidates,max_bow_matches,sim3_ransac_inliers," + "proj_matches_coarse,sim3_opt_inliers,proj_matches_fine,coincidences," + "sim3_scale,rot_x,rot_y,rot_z,rot_w," + "trans_x,trans_y,trans_z," + "inliers,reprojection_error," + "detection_ms,optimization_ms,scale_delta," + "source,rel_time_s,track_frame_id,map_id,loop_detected,merge_detected," + "gba_started,gba_finished,num_corrected_kfs,sim3_correction_trans_norm," + "sim3_correction_rot_deg,gba_time_ms,merge_time_ms,loop_time_ms\n"; + } + else + { + std::cerr << "[LoopClosing] WARNING: cannot open loop_closing_log.csv\n"; + } } void LoopClosing::SetTracker(Tracking *pTracker) @@ -87,29 +111,141 @@ void LoopClosing::SetLocalMapper(LocalMapping *pLocalMapper) } +// ── CSV helper ───────────────────────────────────────────────────────────────── +void LoopClosing::WriteLoopLog(const std::string& event_type, + const std::string& status, + double timestamp, + long unsigned int current_kf_id, + long long int matched_kf_id, + const LcPipelineStats& stats, + const g2o::Sim3& sim3, + int inliers, + double reprojection_error, + double detection_ms, + double optimization_ms) +{ + unique_lock lock(mMutexLcCsv); + if(!mLcCsv.is_open()) return; + + double scale = sim3.scale(); + Eigen::Quaterniond q = sim3.rotation(); + Eigen::Vector3d t = sim3.translation(); + double scale_delta = scale - 1.0; + if(mLcFirstTimestamp < 0.0) + mLcFirstTimestamp = timestamp; + const double rel_time_s = timestamp - mLcFirstTimestamp; + const long unsigned int track_frame_id = mpCurrentKF ? mpCurrentKF->mnFrameId : 0; + Map* pCurrentMap = mpCurrentKF ? mpCurrentKF->GetMap() : static_cast(NULL); + const long unsigned int map_id = pCurrentMap ? pCurrentMap->GetId() : 0; + const bool loop_event = (event_type == "LOOP_DETECTED" || + event_type == "LOOP_CLOSED" || + event_type == "LOOP_CLOSURE"); + const bool merge_event = (event_type == "MAP_MERGE"); + const bool gba_started = (event_type == "GBA_STARTED"); + const bool gba_finished = (event_type == "GBA_FINISHED"); + int num_corrected_kfs = -1; + if((event_type == "LOOP_CLOSED" || merge_event) && status == "SUCCESS") + num_corrected_kfs = mCsvLastCorrectedKFs; + else if(gba_started || gba_finished) + num_corrected_kfs = inliers; + const double qw_abs = std::min(1.0, std::max(-1.0, std::fabs(q.w()))); + const double rot_deg = 2.0 * std::acos(qw_abs) * 180.0 / 3.14159265358979323846; + double merge_time_ms = -1.0; + double loop_time_ms = -1.0; + double gba_time_ms = -1.0; + if(merge_event) + merge_time_ms = optimization_ms; + else if(loop_event) + loop_time_ms = optimization_ms; + else if(gba_started || gba_finished) + gba_time_ms = optimization_ms; + + mLcCsv << std::fixed << std::setprecision(6) + << event_type << "," + << status << "," + << timestamp << "," + << current_kf_id << ","; + if(matched_kf_id >= 0) + mLcCsv << (long unsigned int)matched_kf_id; + mLcCsv << "," + << stats.bow_candidates << "," + << stats.max_bow_matches << "," + << stats.sim3_ransac_inliers << "," + << stats.proj_matches_coarse << "," + << stats.sim3_opt_inliers << "," + << stats.proj_matches_fine << "," + << stats.coincidences << "," + << scale << "," + << q.x() << "," + << q.y() << "," + << q.z() << "," + << q.w() << "," + << t.x() << "," + << t.y() << "," + << t.z() << "," + << inliers << "," + << reprojection_error << "," + << std::setprecision(3) + << detection_ms << "," + << optimization_ms << "," + << std::setprecision(6) + << scale_delta << "," + << "LoopClosing" << "," + << rel_time_s << "," + << track_frame_id << "," + << map_id << "," + << (loop_event ? "yes" : "no") << "," + << (merge_event ? "yes" : "no") << "," + << (gba_started ? "yes" : "no") << "," + << (gba_finished ? "yes" : "no") << "," + << num_corrected_kfs << "," + << t.norm() << "," + << rot_deg << "," + << gba_time_ms << "," + << merge_time_ms << "," + << loop_time_ms << "\n"; + mLcCsv.flush(); +} +// ───────────────────────────────────────────────────────────────────────── + void LoopClosing::Run() { - mbFinished =false; + mbFinished =false;//讓其他人知道LoopClosing正在運行 - while(1) + while(1)//常駐背景執行緒 { //NEW LOOP AND MERGE DETECTION ALGORITHM //---------------------------- - if(CheckNewKeyFrames()) + if(CheckNewKeyFrames())//清理舊的關鍵幀（KeyFrame）所暫存的迴圈檢測候選名單 { - if(mpLastCurrentKF) + if(mpLastCurrentKF)//當新的關鍵幀被加入地圖時，原本的 CurrentKF 會變成 LastCurrentKF。 { - mpLastCurrentKF->mvpLoopCandKFs.clear(); - mpLastCurrentKF->mvpMergeCandKFs.clear(); + mpLastCurrentKF->mvpLoopCandKFs.clear();//清空該關鍵幀中儲存的「迴圈檢測候選關鍵幀（Loop Closure Candidate KeyFrames）」列表。 + mpLastCurrentKF->mvpMergeCandKFs.clear();//清空該關鍵幀中儲存的「合併候選關鍵幀（Merge Candidate KeyFrames）」列表。 } #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_StartPR = std::chrono::steady_clock::now(); #endif + // ── detection timing ── + auto csv_det_t0 = std::chrono::steady_clock::now(); - bool bFindedRegion = NewDetectCommonRegions(); + bool bFindedRegion = NewDetectCommonRegions();//呼叫 NewDetectCommonRegions() 做 place recognition + 幾何驗證，回傳 bFindedRegion + double csv_detection_ms = std::chrono::duration_cast>( + std::chrono::steady_clock::now() - csv_det_t0).count(); + + // Log every KF that passes through LoopClosing + { + LcPipelineStats kfStats = mLastLoopDetStats; + kfStats.coincidences = mnLoopNumCoincidences; + g2o::Sim3 zeroSim3; + WriteLoopLog("KF_PROCESSED", + bFindedRegion ? "DETECTED" : "NOT_DETECTED", + mpCurrentKF->mTimeStamp, mpCurrentKF->mnId, -1LL, + kfStats, zeroSim3, 0, -1.0, csv_detection_ms, 0.0); + } #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_EndPR = std::chrono::steady_clock::now(); @@ -117,55 +253,63 @@ void LoopClosing::Run() double timePRTotal = std::chrono::duration_cast >(time_EndPR - time_StartPR).count(); vdPRTotal_ms.push_back(timePRTotal); #endif - if(bFindedRegion) + if(bFindedRegion)//Map Merging Phase { if(mbMergeDetected) { if ((mpTracker->mSensor==System::IMU_MONOCULAR || mpTracker->mSensor==System::IMU_STEREO || mpTracker->mSensor==System::IMU_RGBD) && (!mpCurrentKF->GetMap()->isImuInitialized())) { - cout << "IMU is not initilized, merge is aborted" << endl; + cout << "IMU is not initilized, merge is aborted" << endl;//IMU 模式但 map 尚未 IMU 初始化，直接中止 merge } else { - Sophus::SE3d mTmw = mpMergeMatchedKF->GetPose().cast(); - g2o::Sim3 gSmw2(mTmw.unit_quaternion(), mTmw.translation(), 1.0); - Sophus::SE3d mTcw = mpCurrentKF->GetPose().cast(); - g2o::Sim3 gScw1(mTcw.unit_quaternion(), mTcw.translation(), 1.0); - g2o::Sim3 gSw2c = mg2oMergeSlw.inverse(); + Sophus::SE3d mTmw = mpMergeMatchedKF->GetPose().cast();//取得被匹配的關鍵幀（MatchedKF）的位姿，並將其轉換為 Sophus::SE3d 類型，存儲在 mTmw 變數中。 + g2o::Sim3 gSmw2(mTmw.unit_quaternion(), mTmw.translation(), 1.0);//把 SE3 轉成 Sim3，scale 先固定 1.0。 + Sophus::SE3d mTcw = mpCurrentKF->GetPose().cast();//取得當前關鍵幀（CurrentKF）的位姿，並將其轉換為 Sophus::SE3d 類型，存儲在 mTcw 變數中。 + g2o::Sim3 gScw1(mTcw.unit_quaternion(), mTcw.translation(), 1.0);//把 SE3 轉成 Sim3，scale 先固定 1.0。 + g2o::Sim3 gSw2c = mg2oMergeSlw.inverse();//對 mg2oMergeSlw 進行 逆變換， mg2oMergeSlw 代表把來源地圖的點投影到世界地圖 g2o::Sim3 gSw1m = mg2oMergeSlw; - mSold_new = (gSw2c * gScw1); - + mSold_new = (gSw2c * gScw1);//把 current KF 映到 merge 座標去 - if(mpCurrentKF->GetMap()->IsInertial() && mpMergeMatchedKF->GetMap()->IsInertial()) + //Map Merging）過程中最關鍵的「安全性檢查與約束」機制。 + if(mpCurrentKF->GetMap()->IsInertial() && mpMergeMatchedKF->GetMap()->IsInertial())//只有當「目前的關鍵幀地圖」與「被匹配到的來源地圖」**兩者都啟用了 IMU 模式（Inertial Mode）**時，才會執行後續的嚴格檢查。 { cout << "Merge check transformation with IMU" << endl; - if(mSold_new.scale()<0.90||mSold_new.scale()>1.1){ + if(mSold_new.scale()<0.90||mSold_new.scale()>1.1){//尺度一致性檢查 mpMergeLastCurrentKF->SetErase(); mpMergeMatchedKF->SetErase(); - mnMergeNumCoincidences = 0; + mnMergeNumCoincidences = 0;//BoW path mvpMergeMatchedMPs.clear(); mvpMergeMPs.clear(); mnMergeNumNotFound = 0; mbMergeDetected = false; Verbose::PrintMess("scale bad estimated. Abort merging", Verbose::VERBOSITY_NORMAL); - continue; + WriteLoopLog("MAP_MERGE", "FAILED", + mpCurrentKF->mTimeStamp, + mpCurrentKF->mnId, + (long long int)mpMergeMatchedKF->mnId, + mLastMergeDetStats, + mSold_new, + mnMergeNumCoincidences, -1.0, + csv_detection_ms, 0.0); + continue;//使用 continue 直接放棄本次合併嘗試，回到迴圈開頭尋找下一個機會。 } - // If inertial, force only yaw + // If inertial, force only yaw 旋轉約束 —— 利用重力向量 強迫系統只允許「水平面內的旋轉」，這能極大地過濾掉那些雖然視覺相似但空間姿態（傾斜角度）完全錯誤的誤匹配。 if ((mpTracker->mSensor==System::IMU_MONOCULAR || mpTracker->mSensor==System::IMU_STEREO || mpTracker->mSensor==System::IMU_RGBD) && mpCurrentKF->GetMap()->GetIniertialBA1()) { - Eigen::Vector3d phi = LogSO3(mSold_new.rotation().toRotationMatrix()); - phi(0)=0; - phi(1)=0; - mSold_new = g2o::Sim3(ExpSO3(phi),mSold_new.translation(),1.0); + Eigen::Vector3d phi = LogSO3(mSold_new.rotation().toRotationMatrix());//把旋轉矩陣轉換成對應的李代數向量，存儲在 phi 變數中。這個向量包含了繞 x、y、z 軸的旋轉分量。 + phi(0)=0;//鎖死 Roll + phi(1)=0;//鎖死 Pitch + mSold_new = g2o::Sim3(ExpSO3(phi),mSold_new.translation(),1.0);//根據修改後的 phi 向量重新構造一個新的 Sim3 物件，並將其賦值回 mSold_new。這樣就實現了只保留 Yaw 旋轉分量的約束。 } } - mg2oMergeSmw = gSmw2 * gSw2c * gScw1; + mg2oMergeSmw = gSmw2 * gSw2c * gScw1;//把被匹配的 KF 映到 merge 座標去 - mg2oMergeScw = mg2oMergeSlw; + mg2oMergeScw = mg2oMergeSlw;//把 current KF 映到 merge 座標去 //mpTracker->SetStepByStep(true); @@ -176,11 +320,24 @@ void LoopClosing::Run() nMerges += 1; #endif + auto csv_merge_t0 = std::chrono::steady_clock::now(); + mCsvLastCorrectedKFs = -1; // TODO UNCOMMENT if (mpTracker->mSensor==System::IMU_MONOCULAR ||mpTracker->mSensor==System::IMU_STEREO || mpTracker->mSensor==System::IMU_RGBD) - MergeLocal2(); + MergeLocal2();//針對「慣性模態 (Inertial Mode)」 地圖合併不僅要對齊旋轉（Rotation）和平移（Translation），還必須處理尺度因子 (Scale Factor)。 else - MergeLocal(); + MergeLocal();//針對「純視覺模態 (Visual-Only Mode)」 + double csv_merge_ms = std::chrono::duration_cast>( + std::chrono::steady_clock::now() - csv_merge_t0).count(); + + WriteLoopLog("MAP_MERGE", "SUCCESS", + mpCurrentKF->mTimeStamp, + mpCurrentKF->mnId, + (long long int)mpMergeMatchedKF->mnId, + mLastMergeDetStats, + mg2oMergeScw, + mnMergeNumCoincidences, -1.0, + csv_detection_ms, csv_merge_ms); #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_EndMerge = std::chrono::steady_clock::now(); @@ -192,9 +349,9 @@ void LoopClosing::Run() Verbose::PrintMess("Merge finished!", Verbose::VERBOSITY_QUIET); } - vdPR_CurrentTime.push_back(mpCurrentKF->mTimeStamp); - vdPR_MatchedTime.push_back(mpMergeMatchedKF->mTimeStamp); - vnPR_TypeRecogn.push_back(1); + vdPR_CurrentTime.push_back(mpCurrentKF->mTimeStamp);//將當前關鍵幀的時間戳記添加到 vdPR_CurrentTime 向量中，這個向量用於記錄所有通過 LoopClosing 處理的關鍵幀的時間戳。 + vdPR_MatchedTime.push_back(mpMergeMatchedKF->mTimeStamp);//將被匹配的關鍵幀（MatchedKF）的時間戳記添加到 vdPR_MatchedTime 向量中，這個向量用於記錄所有被匹配到的關鍵幀的時間戳。 + vnPR_TypeRecogn.push_back(1);//將整數 1 添加到 vnPR_TypeRecogn 向量中，這個向量用於記錄每次 place recognition 的類型，1 代表這是一個「地圖合併 (Map Merge)」的事件。 // Reset all variables mpMergeLastCurrentKF->SetErase(); @@ -221,15 +378,15 @@ void LoopClosing::Run() if(mbLoopDetected) { - bool bGoodLoop = true; - vdPR_CurrentTime.push_back(mpCurrentKF->mTimeStamp); - vdPR_MatchedTime.push_back(mpLoopMatchedKF->mTimeStamp); - vnPR_TypeRecogn.push_back(0); + bool bGoodLoop = true;//設定一個預設標記，假設檢測結果是好的，除非後續檢查失敗。 + vdPR_CurrentTime.push_back(mpCurrentKF->mTimeStamp);//將當前關鍵幀的時間戳記添加到 vdPR_CurrentTime 向量中，這個向量用於記錄所有通過 LoopClosing 處理的關鍵幀的時間戳。 + vdPR_MatchedTime.push_back(mpLoopMatchedKF->mTimeStamp);//將被匹配的關鍵幀（MatchedKF）的時間戳記添加到 vdPR_MatchedTime 向量中，這個向量用於記錄所有被匹配到的關鍵幀的時間戳。 + vnPR_TypeRecogn.push_back(0);//將整數 0 添加到 vnPR_TypeRecogn 向量中，這個向量用於記錄每次 place recognition 的類型，0 代表這是一個「迴圈檢測 (Loop Closure)」的事件。 Verbose::PrintMess("*Loop detected", Verbose::VERBOSITY_QUIET); - mg2oLoopScw = mg2oLoopSlw; //*mvg2oSim3LoopTcw[nCurrentIndex]; - if(mpCurrentKF->GetMap()->IsInertial()) + mg2oLoopScw = mg2oLoopSlw; //*mvg2oSim3LoopTcw[nCurrentIndex]; 將迴圈檢測得到的變換矩陣（從 Source 到 World）賦值給優化器使用的變數。 + if(mpCurrentKF->GetMap()->IsInertial())//如果當前關鍵幀所在的地圖啟用了慣性模態（Inertial Mode），則進行額外的檢查和處理，以確保迴圈檢測的結果在慣性約束下是合理的。 { Sophus::SE3d Twc = mpCurrentKF->GetPoseInverse().cast(); g2o::Sim3 g2oTwc(Twc.unit_quaternion(),Twc.translation(),1.0); @@ -237,7 +394,7 @@ void LoopClosing::Run() Eigen::Vector3d phi = LogSO3(g2oSww_new.rotation().toRotationMatrix()); cout << "phi = " << phi.transpose() << endl; - if (fabs(phi(0))<0.008f && fabs(phi(1))<0.008f && fabs(phi(2))<0.349f) + if (fabs(phi(0))<0.008f && fabs(phi(1))<0.008f && fabs(phi(2))<0.349f)//這是在檢查旋轉軸是否發生了不合理的翻轉。 { if(mpCurrentKF->GetMap()->IsInertial()) { @@ -257,13 +414,21 @@ void LoopClosing::Run() { cout << "BAD LOOP!!!" << endl; bGoodLoop = false; + WriteLoopLog("LOOP_DETECTED", "FAILED", + mpCurrentKF->mTimeStamp, + mpCurrentKF->mnId, + (long long int)mpLoopMatchedKF->mnId, + mLastLoopDetStats, + mg2oLoopScw, + mnLoopNumCoincidences, -1.0, + csv_detection_ms, 0.0); } } - if (bGoodLoop) { + if (bGoodLoop) {//如果通過了上述所有檢查（包括尺度檢查和旋轉檢查），則認為這是一個有效的迴圈檢測結果，可以進行後續的優化和地圖校正。 - mvpLoopMapPoints = mvpLoopMPs; + mvpLoopMapPoints = mvpLoopMPs;//將迴圈檢測中找到的匹配地圖點（Matched MapPoints）列表賦值給 mvpLoopMapPoints，這個列表將在後續的優化過程中使用。 #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_StartLoop = std::chrono::steady_clock::now(); @@ -271,7 +436,25 @@ void LoopClosing::Run() nLoop += 1; #endif - CorrectLoop(); + auto csv_loop_t0 = std::chrono::steady_clock::now(); + mCsvLastCorrectedKFs = -1; + CorrectLoop();//執行迴圈校正（Loop Correction），這個函數會根據檢測到的迴圈變換對地圖進行全局優化，修正整個地圖的結構以消除累積誤差。 + double csv_loop_ms = std::chrono::duration_cast>( + std::chrono::steady_clock::now() - csv_loop_t0).count(); + + double loop_scale = mg2oLoopScw.scale(); + double scale_error = fabs(1.0 - loop_scale); + cout << "[LoopClosing] Scale error: " << scale_error + << " (scale factor: " << loop_scale << ")" << endl; + + WriteLoopLog("LOOP_CLOSED", "SUCCESS", + mpCurrentKF->mTimeStamp, + mpCurrentKF->mnId, + (long long int)mpLoopMatchedKF->mnId, + mLastLoopDetStats, + mg2oLoopScw, + mnLoopNumCoincidences, -1.0, + csv_detection_ms, csv_loop_ms); #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_EndLoop = std::chrono::steady_clock::now(); @@ -305,7 +488,7 @@ void LoopClosing::Run() usleep(5000); } - SetFinish(); + SetFinish();//讓其他人知道 LoopClosing 已經結束運行 } void LoopClosing::InsertKeyFrame(KeyFrame *pKF) @@ -345,6 +528,10 @@ bool LoopClosing::NewDetectCommonRegions() return false; } + // Reset per-KF detection stats + mLastLoopDetStats = LcPipelineStats{}; + mLastMergeDetStats = LcPipelineStats{}; + //如果關鍵幀數量太少（例如 Stereo 模式下小於 5 幀，或一般情況小於 12 幀） if(mpTracker->mSensor == System::STEREO && mpLastMap->GetAllKeyFrames().size() < 5) //12 { // cout << "LoopClousure: Stereo KF inserted without check: " << mpCurrentKF->mnId << endl; @@ -371,7 +558,8 @@ bool LoopClosing::NewDetectCommonRegions() #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_StartEstSim3_1 = std::chrono::steady_clock::now(); #endif - if(mnLoopNumCoincidences > 0) + //第一階段：基於「幾何連續性」的精細檢測 (Geometric Continuity Check) + if(mnLoopNumCoincidences > 0)//上一幀已經發現了一些潛在的匹配點 Loop Refine Path { bCheckSpatial = true; // Find from the last KF candidates @@ -380,7 +568,7 @@ bool LoopClosing::NewDetectCommonRegions() g2o::Sim3 gScw = gScl * mg2oLoopSlw; int numProjMatches = 0; vector vpMatchedMPs; - bool bCommonRegion = DetectAndReffineSim3FromLastKF(mpCurrentKF, mpLoopMatchedKF, gScw, numProjMatches, mvpLoopMPs, vpMatchedMPs); + bool bCommonRegion = DetectAndReffineSim3FromLastKF(mpCurrentKF, mpLoopMatchedKF, gScw, numProjMatches, mvpLoopMPs, vpMatchedMPs);//利用前一幀的匹配點作為種子，在當前幀進行幾何驗證與精細化 if(bCommonRegion) { @@ -402,11 +590,11 @@ bool LoopClosing::NewDetectCommonRegions() } } else - { + {//第二階段：基於「Bag-of-Words (BoW)」的全局檢索 (Global Retrieval) bLoopDetectedInKF = false; mnLoopNumNotFound++; - if(mnLoopNumNotFound >= 2) + if(mnLoopNumNotFound >= 2)//如果連續兩幀都無法在幾何上驗證出匹配點，則認為之前的匹配結果可能是誤匹配，重置相關變數以重新開始檢測。 { mpLoopLastCurrentKF->SetErase(); mpLoopMatchedKF->SetErase(); @@ -419,9 +607,9 @@ bool LoopClosing::NewDetectCommonRegions() } } - //Merge candidates + //Merge candidates 檢查是否可以進行地圖合併 bool bMergeDetectedInKF = false; - if(mnMergeNumCoincidences > 0) + if(mnMergeNumCoincidences > 0)//Refine Path 入口 { // Find from the last KF candidates Sophus::SE3d mTcl = (mpCurrentKF->GetPose() * mpMergeLastCurrentKF->GetPoseInverse()).cast(); @@ -441,7 +629,7 @@ bool LoopClosing::NewDetectCommonRegions() mg2oMergeSlw = gScw; mvpMergeMatchedMPs = vpMatchedMPs; - mbMergeDetected = mnMergeNumCoincidences >= 3; + mbMergeDetected = mnMergeNumCoincidences >= 3;//如果連續三幀都能在幾何上驗證出匹配點，則認為這是一個可靠的匹配，可以進行地圖合併。 } else { @@ -449,7 +637,7 @@ bool LoopClosing::NewDetectCommonRegions() bMergeDetectedInKF = false; mnMergeNumNotFound++; - if(mnMergeNumNotFound >= 2) + if(mnMergeNumNotFound >= 2)//如果連續兩幀都無法在幾何上驗證出匹配點，則認為之前的匹配結果可能是誤匹配，重置相關變數以重新開始檢測。 { mpMergeLastCurrentKF->SetErase(); mpMergeMatchedKF->SetErase(); @@ -473,6 +661,9 @@ bool LoopClosing::NewDetectCommonRegions() #ifdef REGISTER_TIMES vdEstSim3_ms.push_back(timeEstSim3); #endif + // Keep coincidences in sync even on the refine-path early return + mLastLoopDetStats.coincidences = mnLoopNumCoincidences; + mLastMergeDetStats.coincidences = mnMergeNumCoincidences; mpKeyFrameDB->add(mpCurrentKF); return true; } @@ -502,14 +693,16 @@ bool LoopClosing::NewDetectCommonRegions() #endif // Check the BoW candidates if the geometric candidate list is empty //Loop candidates - if(!bLoopDetectedInKF && !vpLoopBowCand.empty()) + if(!bLoopDetectedInKF && !vpLoopBowCand.empty())//Loop BoW Path { - mbLoopDetected = DetectCommonRegionsFromBoW(vpLoopBowCand, mpLoopMatchedKF, mpLoopLastCurrentKF, mg2oLoopSlw, mnLoopNumCoincidences, mvpLoopMPs, mvpLoopMatchedMPs); + mbLoopDetected = DetectCommonRegionsFromBoW(vpLoopBowCand, mpLoopMatchedKF, mpLoopLastCurrentKF, mg2oLoopSlw, mnLoopNumCoincidences, mvpLoopMPs, mvpLoopMatchedMPs, mLastLoopDetStats); + mLastLoopDetStats.coincidences = mnLoopNumCoincidences; } // Merge candidates - if(!bMergeDetectedInKF && !vpMergeBowCand.empty()) + if(!bMergeDetectedInKF && !vpMergeBowCand.empty())//BoW Path 入口 { - mbMergeDetected = DetectCommonRegionsFromBoW(vpMergeBowCand, mpMergeMatchedKF, mpMergeLastCurrentKF, mg2oMergeSlw, mnMergeNumCoincidences, mvpMergeMPs, mvpMergeMatchedMPs); + mbMergeDetected = DetectCommonRegionsFromBoW(vpMergeBowCand, mpMergeMatchedKF, mpMergeLastCurrentKF, mg2oMergeSlw, mnMergeNumCoincidences, mvpMergeMPs, mvpMergeMatchedMPs, mLastMergeDetStats); + mLastMergeDetStats.coincidences = mnMergeNumCoincidences; } #ifdef REGISTER_TIMES @@ -523,6 +716,8 @@ bool LoopClosing::NewDetectCommonRegions() if(mbMergeDetected || mbLoopDetected) { + mLastLoopDetStats.coincidences = mnLoopNumCoincidences; + mLastMergeDetStats.coincidences = mnMergeNumCoincidences; return true; } @@ -576,14 +771,19 @@ bool LoopClosing::DetectAndReffineSim3FromLastKF(KeyFrame* pCurrentKF, KeyFrame* } bool LoopClosing::DetectCommonRegionsFromBoW(std::vector &vpBowCand, KeyFrame* &pMatchedKF2, KeyFrame* &pLastCurrentKF, g2o::Sim3 &g2oScw, - int &nNumCoincidences, std::vector &vpMPs, std::vector &vpMatchedMPs) + int &nNumCoincidences, std::vector &vpMPs, std::vector &vpMatchedMPs, + LcPipelineStats &outStats) { + outStats = LcPipelineStats{}; int nBoWMatches = 20; int nBoWInliers = 15; int nSim3Inliers = 20; int nProjMatches = 50; int nProjOptMatches = 80; + // Tracking best pipeline performance across all candidates + int st_bestBoW = 0, st_bestRansac = 0, st_bestProjC = 0, st_bestOpt = 0, st_bestProjF = 0; + set spConnectedKeyFrames = mpCurrentKF->GetConnectedKeyFrames(); int nNumCovisibles = 10; @@ -688,6 +888,8 @@ bool LoopClosing::DetectCommonRegionsFromBoW(std::vector &vpBowCand, //pMostBoWMatchesKF = vpCovKFi[pMostBoWMatchesKF]; + if(nMostBoWNumMatches > st_bestBoW) st_bestBoW = nMostBoWNumMatches; + if(numBoWMatches >= nBoWMatches) // TODO pick a good threshold { // Geometric validation @@ -711,6 +913,7 @@ bool LoopClosing::DetectCommonRegionsFromBoW(std::vector &vpBowCand, if(bConverge) { + if(nInliers > st_bestRansac) st_bestRansac = nInliers; //std::cout << "Check BoW: SolverSim3 converged" << std::endl; //Verbose::PrintMess("BoW guess: Convergende with " + to_string(nInliers) + " geometrical inliers among " + to_string(nBoWInliers) + " BoW matches", Verbose::VERBOSITY_DEBUG); @@ -755,6 +958,8 @@ bool LoopClosing::DetectCommonRegionsFromBoW(std::vector &vpBowCand, int numProjMatches = matcher.SearchByProjection(mpCurrentKF, mScw, vpMapPoints, vpKeyFrames, vpMatchedMP, vpMatchedKF, 8, 1.5); //cout <<"BoW: " << numProjMatches << " matches between " << vpMapPoints.size() << " points with coarse Sim3" << endl; + if(numProjMatches > st_bestProjC) st_bestProjC = numProjMatches; + if(numProjMatches >= nProjMatches) { // Optimize Sim3 transformation with every matches @@ -766,6 +971,8 @@ bool LoopClosing::DetectCommonRegionsFromBoW(std::vector &vpBowCand, int numOptMatches = Optimizer::OptimizeSim3(mpCurrentKF, pKFi, vpMatchedMP, gScm, 10, mbFixScale, mHessian7x7, true); + if(numOptMatches > st_bestOpt) st_bestOpt = numOptMatches; + if(numOptMatches >= nSim3Inliers) { g2o::Sim3 gSmw(pMostBoWMatchesKF->GetRotation().cast(),pMostBoWMatchesKF->GetTranslation().cast(),1.0); @@ -776,6 +983,8 @@ bool LoopClosing::DetectCommonRegionsFromBoW(std::vector &vpBowCand, vpMatchedMP.resize(mpCurrentKF->GetMapPointMatches().size(), static_cast(NULL)); int numProjOptMatches = matcher.SearchByProjection(mpCurrentKF, mScw, vpMapPoints, vpMatchedMP, 5, 1.0); + if(numProjOptMatches > st_bestProjF) st_bestProjF = numProjOptMatches; + if(numProjOptMatches >= nProjOptMatches) { int max_x = -1, min_x = 1000000; @@ -867,6 +1076,14 @@ bool LoopClosing::DetectCommonRegionsFromBoW(std::vector &vpBowCand, index++; } + // Fill pipeline stats output + outStats.bow_candidates = numCandidates; + outStats.max_bow_matches = st_bestBoW; + outStats.sim3_ransac_inliers = st_bestRansac; + outStats.proj_matches_coarse = st_bestProjC; + outStats.sim3_opt_inliers = st_bestOpt; + outStats.proj_matches_fine = st_bestProjF; + if(nBestMatchesReproj > 0) { pLastCurrentKF = mpCurrentKF; @@ -1134,6 +1351,8 @@ void LoopClosing::CorrectLoop() //cout << "LC: end replacing duplicated" << endl; } + mCsvLastCorrectedKFs = static_cast(CorrectedSim3.size()); + // Project MapPoints observed in the neighborhood of the loop keyframe // into the current keyframe and neighbors using corrected poses. // Fuse duplications. @@ -1464,6 +1683,8 @@ void LoopClosing::MergeLocal() //TODO DEBUG to know which are the KFs that had been moved to the other map } + mCsvLastCorrectedKFs = static_cast(vCorrectedSim3.size()); + int numPointsWithCorrection = 0; //for(MapPoint* pMPi : spLocalWindowMPs) @@ -1766,7 +1987,7 @@ void LoopClosing::MergeLocal() mbRunningGBA = true; mbFinishedGBA = false; mbStopGBA = false; - mpThreadGBA = new thread(&LoopClosing::RunGlobalBundleAdjustment,this, pMergeMap, mpCurrentKF->mnId); + mpThreadGBA = new thread(&LoopClosing::RunGlobalBundleAdjustment,this, pMergeMap, mpCurrentKF->mnId);// } mpMergeMatchedKF->AddMergeEdge(mpCurrentKF); @@ -2050,6 +2271,7 @@ void LoopClosing::MergeLocal2() //cout << "start MergeInertialBA" << endl; Optimizer::MergeInertialBA(pCurrKF, mpMergeMatchedKF, &bStopFlag, pCurrentMap,CorrectedSim3); //cout << "end MergeInertialBA" << endl; + mCsvLastCorrectedKFs = static_cast(CorrectedSim3.size()); /*good = pCurrentMap->CheckEssentialGraph(); if(!good) @@ -2279,12 +2501,46 @@ void LoopClosing::RunGlobalBundleAdjustment(Map* pActiveMap, unsigned long nLoop #endif const bool bImuInit = pActiveMap->isImuInitialized(); + auto csv_gba_t0 = std::chrono::steady_clock::now(); + LcPipelineStats csvGBAStats; + g2o::Sim3 csvZeroSim3; + double csvGBATimestamp = 0.0; + long unsigned int csvGBAKfId = nLoopKF; + { + const vector vpKFs = pActiveMap->GetAllKeyFrames(); + for(size_t i=0; imnId == nLoopKF) + { + csvGBATimestamp = vpKFs[i]->mTimeStamp; + csvGBAKfId = vpKFs[i]->mnId; + break; + } + } + } + if(csvGBATimestamp <= 0.0 && mpCurrentKF) + csvGBATimestamp = mpCurrentKF->mTimeStamp; + + WriteLoopLog("GBA_STARTED", "STARTED", + csvGBATimestamp, csvGBAKfId, -1LL, + csvGBAStats, csvZeroSim3, + (int)pActiveMap->KeyFramesInMap(), -1.0, + 0.0, 0.0); if(!bImuInit) Optimizer::GlobalBundleAdjustemnt(pActiveMap,10,&mbStopGBA,nLoopKF,false); else Optimizer::FullInertialBA(pActiveMap,7,false,nLoopKF,&mbStopGBA); + double csv_gba_ms = std::chrono::duration_cast>( + std::chrono::steady_clock::now() - csv_gba_t0).count(); + + WriteLoopLog("GBA_FINISHED", mbStopGBA ? "ABORTED" : "OPTIMIZED", + csvGBATimestamp, csvGBAKfId, -1LL, + csvGBAStats, csvZeroSim3, + (int)pActiveMap->KeyFramesInMap(), -1.0, + 0.0, csv_gba_ms); + #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_EndGBA = std::chrono::steady_clock::now(); @@ -2525,6 +2781,11 @@ bool LoopClosing::CheckFinish() void LoopClosing::SetFinish() { + if(mLcCsv.is_open()) + { + mLcCsv.flush(); + mLcCsv.close(); + } unique_lock lock(mMutexFinish); mbFinished = true; } diff --git a/src/Optimizer.cc b/src/Optimizer.cc index b785be38299..35da7d02d52 100644 --- a/src/Optimizer.cc +++ b/src/Optimizer.cc @@ -20,6 +20,7 @@ #include "Optimizer.h" +#include #include #include @@ -44,6 +45,154 @@ namespace ORB_SLAM3 { +namespace +{ +void ResetResidualStats(Optimizer::InertialResidualStats* pStats) +{ + if(pStats) + *pStats = Optimizer::InertialResidualStats(); +} + +void FinalizeResidualStats(Optimizer::InertialResidualStats* pStats, + double rot_sum, double vel_sum, double pos_sum, + double gyro_rw_sum, int gyro_rw_count, + double acc_rw_sum, int acc_rw_count) +{ + if(!pStats) + return; + + if(pStats->count > 0) + { + pStats->chi2_mean = pStats->chi2_sum / static_cast(pStats->count); + pStats->rot_norm = rot_sum / static_cast(pStats->count); + pStats->vel_norm = vel_sum / static_cast(pStats->count); + pStats->pos_norm = pos_sum / static_cast(pStats->count); + } + + if(pStats->visual_count > 0) + pStats->visual_chi2_mean = pStats->visual_chi2_sum / static_cast(pStats->visual_count); + + if(gyro_rw_count > 0) + pStats->gyro_rw_chi2 = gyro_rw_sum / static_cast(gyro_rw_count); + if(acc_rw_count > 0) + pStats->acc_rw_chi2 = acc_rw_sum / static_cast(acc_rw_count); +} + +void AccumulateInertialEdge(EdgeInertial* pEdge, Optimizer::InertialResidualStats* pStats, + double &rot_sum, double &vel_sum, double &pos_sum) +{ + if(!pStats || !pEdge) + return; + + Vector9d err = pEdge->GetErrorVector(); + const double chi2 = pEdge->chi2(); + pStats->count++; + pStats->chi2_sum += chi2; + pStats->chi2_max = (pStats->chi2_max < 0.0) ? chi2 : std::max(pStats->chi2_max, chi2); + rot_sum += err.segment<3>(0).norm(); + vel_sum += err.segment<3>(3).norm(); + pos_sum += err.segment<3>(6).norm(); +} + +template +void AccumulateVisualEdge(EdgeT* pEdge, Optimizer::InertialResidualStats* pStats) +{ + if(!pStats || !pEdge) + return; + + pEdge->computeError(); + const double chi2 = pEdge->chi2(); + pStats->visual_count++; + pStats->visual_chi2_sum += chi2; + pStats->visual_chi2_max = (pStats->visual_chi2_max < 0.0) ? chi2 : std::max(pStats->visual_chi2_max, chi2); +} + +void AccumulateGyroRW(EdgeGyroRW* pEdge, double &sum, int &count) +{ + if(!pEdge) + return; + pEdge->computeError(); + sum += pEdge->chi2(); + count++; +} + +void AccumulateAccRW(EdgeAccRW* pEdge, double &sum, int &count) +{ + if(!pEdge) + return; + pEdge->computeError(); + sum += pEdge->chi2(); + count++; +} + +template +void AccumulateVisualEdges(const vector &edges, + Optimizer::InertialResidualStats* pStats) +{ + if(!pStats) + return; + + for(size_t i=0, iend=edges.size(); i +void CollectVisualResidualStats(const vector &vpEdgesMono, + const vector &vpEdgesStereo, + Optimizer::InertialResidualStats* pStats) +{ + if(!pStats) + return; + + ResetResidualStats(pStats); + AccumulateVisualEdges(vpEdgesMono, pStats); + AccumulateVisualEdges(vpEdgesStereo, pStats); + FinalizeResidualStats(pStats, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0); +} + +template +void CollectVisualResidualStats(const vector &vpEdgesMono, + const vector &vpEdgesBody, + const vector &vpEdgesStereo, + Optimizer::InertialResidualStats* pStats) +{ + if(!pStats) + return; + + ResetResidualStats(pStats); + AccumulateVisualEdges(vpEdgesMono, pStats); + AccumulateVisualEdges(vpEdgesBody, pStats); + AccumulateVisualEdges(vpEdgesStereo, pStats); + FinalizeResidualStats(pStats, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0); +} + +void CollectInertialBAResidualStats(const vector &vei, + const vector &vegr, + const vector &vear, + const vector &vpEdgesMono, + const vector &vpEdgesStereo, + Optimizer::InertialResidualStats* pStats) +{ + if(!pStats) + return; + + ResetResidualStats(pStats); + double imu_rot_sum = 0.0, imu_vel_sum = 0.0, imu_pos_sum = 0.0; + double gyro_rw_sum = 0.0, acc_rw_sum = 0.0; + int gyro_rw_count = 0, acc_rw_count = 0; + for(size_t i=0; i &a, const pair &b) { return (a.second < b.second); @@ -1113,8 +1262,11 @@ int Optimizer::PoseOptimization(Frame *pFrame) return nInitialCorrespondences-nBad; } -void Optimizer::LocalBundleAdjustment(KeyFrame *pKF, bool* pbStopFlag, Map* pMap, int& num_fixedKF, int& num_OptKF, int& num_MPs, int& num_edges) +void Optimizer::LocalBundleAdjustment(KeyFrame *pKF, bool* pbStopFlag, Map* pMap, int& num_fixedKF, int& num_OptKF, int& num_MPs, int& num_edges, int& num_iters, InertialResidualStats* pStats, InertialResidualStats* pStatsBefore) { + ResetResidualStats(pStats); + ResetResidualStats(pStatsBefore); + // Local KeyFrames: First Breath Search from Current Keyframe list lLocalKeyFrames; @@ -1122,7 +1274,8 @@ void Optimizer::LocalBundleAdjustment(KeyFrame *pKF, bool* pbStopFlag, Map* pMap pKF->mnBALocalForKF = pKF->mnId; Map* pCurrentMap = pKF->GetMap(); - const vector vNeighKFs = pKF->GetVectorCovisibleKeyFrames(); + const vector vNeighKFs = pKF->GetVectorCovisibleKeyFrames();//開關鄰居幀的數量，讓過渡幀進入局部地圖，拉入舊的固定關鍵幀原版 + // const vector vNeighKFs = pKF->GetBestCovisibilityKeyFrames(20); for(int i=0, iend=vNeighKFs.size(); i lLocalMapPoints; @@ -1159,6 +1314,9 @@ void Optimizer::LocalBundleAdjustment(KeyFrame *pKF, bool* pbStopFlag, Map* pMap } } + + + // Fixed Keyframes. Keyframes that see Local MapPoints but that are not Local Keyframes list lFixedCameras; for(list::iterator lit=lLocalMapPoints.begin(), lend=lLocalMapPoints.end(); lit!=lend; lit++) @@ -1402,13 +1560,71 @@ void Optimizer::LocalBundleAdjustment(KeyFrame *pKF, bool* pbStopFlag, Map* pMap } } num_edges = nEdges; + num_MPs = nPoints; if(pbStopFlag) if(*pbStopFlag) return; - + //開關，原版設定 optimizer.initializeOptimization(); - optimizer.optimize(10); + CollectVisualResidualStats(vpEdgesMono, vpEdgesBody, vpEdgesStereo, pStatsBefore); + num_iters = optimizer.optimize(10); + CollectVisualResidualStats(vpEdgesMono, vpEdgesBody, vpEdgesStereo, pStats); + + + //=====================2的============================= + + + // ── 第一階段：5次，含 robust kernel ── + // optimizer.initializeOptimization(); + // CollectVisualResidualStats(vpEdgesMono, vpEdgesBody, vpEdgesStereo, pStatsBefore); + // optimizer.optimize(5); + + // bool bDoMore = true; + // if(pbStopFlag) + // if(*pbStopFlag) + // bDoMore = false; + + // if(bDoMore) + // { + // // 把 outlier 設 level(1)，移除 robust kernel + // for(size_t i=0, iend=vpEdgesMono.size(); iisBad()) continue; + // if(e->chi2()>5.991 || !e->isDepthPositive()) + // e->setLevel(1); + // e->setRobustKernel(0); + // } + // for(size_t i=0, iend=vpEdgesBody.size(); iisBad()) continue; + // if(e->chi2()>5.991 || !e->isDepthPositive()) + // e->setLevel(1); + // e->setRobustKernel(0); + // } + // for(size_t i=0, iend=vpEdgesStereo.size(); iisBad()) continue; + // if(e->chi2()>7.815 || !e->isDepthPositive()) + // e->setLevel(1); + // e->setRobustKernel(0); + // } + + // // ── 第二階段：10次，不含 outlier 也不含 robust kernel ── + // optimizer.initializeOptimization(0); + // num_iters = optimizer.optimize(10); + // CollectVisualResidualStats(vpEdgesMono, vpEdgesBody, vpEdgesStereo, pStats); + // } + + + //=====================2的============================= + vector > vToErase; vToErase.reserve(vpEdgesMono.size()+vpEdgesBody.size()+vpEdgesStereo.size()); @@ -2380,8 +2596,11 @@ int Optimizer::OptimizeSim3(KeyFrame *pKF1, KeyFrame *pKF2, vector & return nIn; } -void Optimizer::LocalInertialBA(KeyFrame *pKF, bool *pbStopFlag, Map *pMap, int& num_fixedKF, int& num_OptKF, int& num_MPs, int& num_edges, bool bLarge, bool bRecInit) +void Optimizer::LocalInertialBA(KeyFrame *pKF, bool *pbStopFlag, Map *pMap, int& num_fixedKF, int& num_OptKF, int& num_MPs, int& num_edges, int& num_iters, bool bLarge, bool bRecInit, InertialResidualStats* pStats, InertialResidualStats* pStatsBefore) { + ResetResidualStats(pStats); + ResetResidualStats(pStatsBefore); + Map* pCurrentMap = pKF->GetMap(); int maxOpt=10; @@ -2837,11 +3056,19 @@ void Optimizer::LocalInertialBA(KeyFrame *pKF, bool *pbStopFlag, Map *pMap, int& assert(mit->second>=3); } + num_MPs = (int)lLocalMapPoints.size(); + num_edges = (int)(vpEdgesMono.size() + vpEdgesStereo.size()); + num_OptKF = (int)(vpOptimizableKFs.size() + lpOptVisKFs.size()); + num_fixedKF = (int)lFixedKeyFrames.size(); + optimizer.initializeOptimization(); optimizer.computeActiveErrors(); float err = optimizer.activeRobustChi2(); - optimizer.optimize(opt_it); // Originally to 2 + CollectInertialBAResidualStats(vei, vegr, vear, vpEdgesMono, vpEdgesStereo, pStatsBefore); + num_iters = optimizer.optimize(opt_it); // Originally to 2 float err_end = optimizer.activeRobustChi2(); + CollectInertialBAResidualStats(vei, vegr, vear, vpEdgesMono, vpEdgesStereo, pStats); + if(pbStopFlag) optimizer.setForceStopFlag(pbStopFlag); @@ -4488,8 +4715,10 @@ void Optimizer::MergeInertialBA(KeyFrame* pCurrKF, KeyFrame* pMergeKF, bool *pbS pMap->IncreaseChangeIndex(); } -int Optimizer::PoseInertialOptimizationLastKeyFrame(Frame *pFrame, bool bRecInit) +int Optimizer::PoseInertialOptimizationLastKeyFrame(Frame *pFrame, bool bRecInit, InertialResidualStats* pStats) { + ResetResidualStats(pStats); + g2o::SparseOptimizer optimizer; g2o::BlockSolverX::LinearSolverType * linearSolver; @@ -4569,12 +4798,12 @@ int Optimizer::PoseInertialOptimizationLastKeyFrame(Frame *pFrame, bool bRecInit e->setVertex(0,VP); e->setMeasurement(obs); - // Add here uncerteinty - const float unc2 = pFrame->mpCamera->uncertainty2(obs); + // Add here uncerteinty 視覺觀測的不確定性（如圖像邊緣的特徵點不穩定）。 + const float unc2 = pFrame->mpCamera->uncertainty2(obs);//相機模型本身的不確定性（如魚眼鏡頭邊緣畸變更大）。 const float invSigma2 = pFrame->mvInvLevelSigma2[kpUn.octave]/unc2; e->setInformation(Eigen::Matrix2d::Identity()*invSigma2); - + g2o::RobustKernelHuber* rk = new g2o::RobustKernelHuber; e->setRobustKernel(rk); rk->setDelta(thHuberMono); @@ -4682,14 +4911,14 @@ int Optimizer::PoseInertialOptimizationLastKeyFrame(Frame *pFrame, bool bRecInit EdgeGyroRW* egr = new EdgeGyroRW(); egr->setVertex(0,VGk); egr->setVertex(1,VG); - Eigen::Matrix3d InfoG = pFrame->mpImuPreintegrated->C.block<3,3>(9,9).cast().inverse(); + Eigen::Matrix3d InfoG = pFrame->mpImuPreintegrated->C.block<3,3>(9,9).cast().inverse();//陀螺儀隨機游走的資訊矩陣（反映了陀螺儀偏置隨時間變化的不確定性）。 egr->setInformation(InfoG); optimizer.addEdge(egr); EdgeAccRW* ear = new EdgeAccRW(); ear->setVertex(0,VAk); ear->setVertex(1,VA); - Eigen::Matrix3d InfoA = pFrame->mpImuPreintegrated->C.block<3,3>(12,12).cast().inverse(); + Eigen::Matrix3d InfoA = pFrame->mpImuPreintegrated->C.block<3,3>(12,12).cast().inverse();//加速度計隨機游走的資訊矩陣（反映了加速度計偏置隨時間變化的不確定性）。 ear->setInformation(InfoA); optimizer.addEdge(ear); @@ -4822,6 +5051,30 @@ int Optimizer::PoseInertialOptimizationLastKeyFrame(Frame *pFrame, bool bRecInit } } + double imu_rot_sum = 0.0, imu_vel_sum = 0.0, imu_pos_sum = 0.0; + double gyro_rw_sum = 0.0, acc_rw_sum = 0.0; + int gyro_rw_count = 0, acc_rw_count = 0; + if(pStats) + { + AccumulateInertialEdge(ei, pStats, imu_rot_sum, imu_vel_sum, imu_pos_sum); + AccumulateGyroRW(egr, gyro_rw_sum, gyro_rw_count); + AccumulateAccRW(ear, acc_rw_sum, acc_rw_count); + for(size_t i=0, iend=vpEdgesMono.size(); imvbOutlier[idx]) + AccumulateVisualEdge(vpEdgesMono[i], pStats); + } + for(size_t i=0, iend=vpEdgesStereo.size(); imvbOutlier[idx]) + AccumulateVisualEdge(vpEdgesStereo[i], pStats); + } + FinalizeResidualStats(pStats, imu_rot_sum, imu_vel_sum, imu_pos_sum, + gyro_rw_sum, gyro_rw_count, acc_rw_sum, acc_rw_count); + } + // Recover optimized pose, velocity and biases pFrame->SetImuPoseVelocity(VP->estimate().Rwb.cast(), VP->estimate().twb.cast(), VV->estimate().cast()); Vector6d b; @@ -4872,8 +5125,10 @@ int Optimizer::PoseInertialOptimizationLastKeyFrame(Frame *pFrame, bool bRecInit return nInitialCorrespondences-nBad; } -int Optimizer::PoseInertialOptimizationLastFrame(Frame *pFrame, bool bRecInit) +int Optimizer::PoseInertialOptimizationLastFrame(Frame *pFrame, bool bRecInit, InertialResidualStats* pStats) { + ResetResidualStats(pStats); + g2o::SparseOptimizer optimizer; g2o::BlockSolverX::LinearSolverType * linearSolver; @@ -5222,6 +5477,29 @@ int Optimizer::PoseInertialOptimizationLastFrame(Frame *pFrame, bool bRecInit) nInliers = nInliersMono + nInliersStereo; + double imu_rot_sum = 0.0, imu_vel_sum = 0.0, imu_pos_sum = 0.0; + double gyro_rw_sum = 0.0, acc_rw_sum = 0.0; + int gyro_rw_count = 0, acc_rw_count = 0; + if(pStats) + { + AccumulateInertialEdge(ei, pStats, imu_rot_sum, imu_vel_sum, imu_pos_sum); + AccumulateGyroRW(egr, gyro_rw_sum, gyro_rw_count); + AccumulateAccRW(ear, acc_rw_sum, acc_rw_count); + for(size_t i=0, iend=vpEdgesMono.size(); imvbOutlier[idx]) + AccumulateVisualEdge(vpEdgesMono[i], pStats); + } + for(size_t i=0, iend=vpEdgesStereo.size(); imvbOutlier[idx]) + AccumulateVisualEdge(vpEdgesStereo[i], pStats); + } + FinalizeResidualStats(pStats, imu_rot_sum, imu_vel_sum, imu_pos_sum, + gyro_rw_sum, gyro_rw_count, acc_rw_sum, acc_rw_count); + } // Recover optimized pose, velocity and biases pFrame->SetImuPoseVelocity(VP->estimate().Rwb.cast(), VP->estimate().twb.cast(), VV->estimate().cast()); diff --git a/src/Settings.cc b/src/Settings.cc index 93a50be6262..acbd9d51189 100644 --- a/src/Settings.cc +++ b/src/Settings.cc @@ -337,6 +337,7 @@ namespace ORB_SLAM3 { if(cameraType_ == Rectified){ b_ = readParameter(fSettings,"Stereo.b",found); bf_ = b_ * calibration1_->getParameter(0); + originalCalib2_ = originalCalib1_;//用於讓KITTI能正常運作 } else{ cv::Mat cvTlr = readParameter(fSettings,"Stereo.T_c1_c2",found); diff --git a/src/System.cc b/src/System.cc index 60d9c5185a0..b932125d16a 100644 --- a/src/System.cc +++ b/src/System.cc @@ -23,6 +23,7 @@ #include #include #include +#include #include #include #include @@ -211,7 +212,16 @@ System::System(const string &strVocFile, const string &strSettingsFile, const eS //Initialize the Loop Closing thread and launch // mSensor!=MONOCULAR && mSensor!=IMU_MONOCULAR mpLoopCloser = new LoopClosing(mpAtlas, mpKeyFrameDatabase, mpVocabulary, mSensor!=MONOCULAR, activeLC); // mSensor!=MONOCULAR); - mptLoopClosing = new thread(&ORB_SLAM3::LoopClosing::Run, mpLoopCloser); + //mptLoopClosing = new thread(&ORB_SLAM3::LoopClosing::Run, mpLoopCloser); + + + // 切換到單線程模式，關閉回環檢測215行 + if(activeLC) + mptLoopClosing = new thread(&ORB_SLAM3::LoopClosing::Run, mpLoopCloser); + else + mptLoopClosing = nullptr; + + //Set pointers between threads mpTracker->SetLocalMapper(mpLocalMapper); @@ -530,10 +540,10 @@ void System::Shutdown() usleep(5000); }*/ - // Wait until all thread have effectively stopped - /*while(!mpLocalMapper->isFinished() || !mpLoopCloser->isFinished() || mpLoopCloser->isRunningGBA()) + // Wait until all threads have effectively stopped + while(!mpLocalMapper->isFinished() || !mpLoopCloser->isFinished() || mpLoopCloser->isRunningGBA()) { - if(!mpLocalMapper->isFinished()) + /*if(!mpLocalMapper->isFinished()) cout << "mpLocalMapper is not finished" << endl;*/ /*if(!mpLoopCloser->isFinished()) cout << "mpLoopCloser is not finished" << endl; @@ -542,8 +552,8 @@ void System::Shutdown() cout << "break anyway..." << endl; break; }*/ - /*usleep(5000); - }*/ + usleep(5000); + } if(!mStrSaveAtlasToFile.empty()) { @@ -558,6 +568,9 @@ void System::Shutdown() mpTracker->PrintTimeStats(); #endif + // Flush and close per-frame CSV log + if(mpTracker) + mpTracker->CloseFrameLog(); } @@ -1505,6 +1518,98 @@ bool System::LoadAtlas(int type) return false; } +void System::SaveMapPoints(const std::string& filename) +{ + // 確保 voxel/ 目錄存在 + const std::string outDir = "voxel"; + struct stat st{}; + if (stat(outDir.c_str(), &st) != 0) + mkdir(outDir.c_str(), 0755); + + const std::string outPath = outDir + "/" + filename; + + std::ofstream f(outPath); + if (!f.is_open()) + { + std::cerr << "[SaveMapPoints] Cannot open file: " << outPath << std::endl; + return; + } + f << std::fixed; + + int totalPts = 0; + + // ── 第一遍：收集所有有效點，計算 centroid ── + struct PtEntry { Eigen::Vector3f pos; unsigned char gray; }; + std::vector allPts; + for (Map* pMap : mpAtlas->GetAllMaps()) + { + if (!pMap) continue; + for (MapPoint* pMP : pMap->GetAllMapPoints()) + { + if (!pMP || pMP->isBad()) continue; + allPts.push_back({pMP->GetWorldPos(), pMP->mGray}); + } + } + + if (allPts.empty()) + { + f.close(); + std::cout << "[SaveMapPoints] No valid points." << std::endl; + return; + } + + // 計算中位數作為 robust center + std::vector xs, ys, zs; + xs.reserve(allPts.size()); ys.reserve(allPts.size()); zs.reserve(allPts.size()); + for (auto& p : allPts) { xs.push_back(p.pos(0)); ys.push_back(p.pos(1)); zs.push_back(p.pos(2)); } + auto median = [](std::vector& v) { std::nth_element(v.begin(), v.begin()+v.size()/2, v.end()); return v[v.size()/2]; }; + float mx = median(xs), my = median(ys), mz = median(zs); + + // 計算到中位數的距離，取 90th percentile 作為閾值 + std::vector dists; + dists.reserve(allPts.size()); + for (auto& p : allPts) + { + float dx = p.pos(0)-mx, dy = p.pos(1)-my, dz = p.pos(2)-mz; + dists.push_back(dx*dx + dy*dy + dz*dz); + } + std::vector sortedDists(dists); + size_t idx90 = (size_t)(sortedDists.size() * 0.95); + std::nth_element(sortedDists.begin(), sortedDists.begin()+idx90, sortedDists.end()); + float threshold = sortedDists[idx90] * 4.0f; // 95th percentile 的 4 倍，只濾極端離群點 + + // ── 第二遍：過濾離群點並寫入 ── + int outliers = 0; + for (size_t i = 0; i < allPts.size(); ++i) + { + if (dists[i] > threshold) { ++outliers; continue; } + auto& pos = allPts[i].pos; + f << std::setprecision(9) + << pos(0) << "," << pos(1) << "," << pos(2) << "," << (int)allPts[i].gray << "\n"; + ++totalPts; + } + f.close(); + std::cout << "[SaveMapPoints] Saved " << totalPts + << " points (" << outliers << " outliers removed) to " << outPath << std::endl; +} + +void System::PrintMapPointCount() const +{ + int totalPts = 0; + for (Map* pMap : mpAtlas->GetAllMaps()) + { + if (!pMap) + continue; + for (MapPoint* pMP : pMap->GetAllMapPoints()) + { + if (!pMP || pMP->isBad()) + continue; + ++totalPts; + } + } + std::cout << "[MapPoints] Total surviving map points: " << totalPts << std::endl; +} + string System::CalculateCheckSum(string filename, int type) { string checksum = ""; diff --git a/src/Tracking.cc b/src/Tracking.cc index 5191451a815..de6f676fa5b 100644 --- a/src/Tracking.cc +++ b/src/Tracking.cc @@ -30,6 +30,8 @@ #include "GeometricTools.h" #include +#include +#include #include #include @@ -128,8 +130,214 @@ Tracking::Tracking(System *pSys, ORBVocabulary* pVoc, FrameDrawer *pFrameDrawer, vdNewKF_ms.clear(); vdTrackTotal_ms.clear(); #endif + + // Initialize per-frame CSV log + InitFrameLog("tracking_log.csv"); +} + +// ── Tracking Monitor implementation ────────────────────────────────────────── + +void Tracking::InitFrameLog(const std::string &path) +{ + mFrameLogFile.open(path, std::ios::out | std::ios::trunc); + if(!mFrameLogFile.is_open()) + { + std::cerr << "[TrackingMonitor] Cannot open log file: " << path << std::endl; + return; + } + mbFrameLogInit = true; + // Write CSV header + mFrameLogFile << + //基本資訊 + "frame_id," + "timestamp," + "rel_time_s," + //系統狀態 + "當前幀狀態," + "上一幀狀態," + "tracking_method," + // "wider_window_used," + //初始化匹配點數量 + "init_matches," + //匹配點數量 + "initial_matches," + "matches_before_tlm_opt," + // "outliers_before_tlm_opt," + "matches_after_tlm_opt," + // "outliers_after_tlm_opt," + "final_inliers," + //優化與品質評估 + // "outlier_ratio," + "opt_type," + //關鍵幀決策 + "need_new_kf," + "new_kf_created," + //地圖統計 + "total_kf_in_map," + "total_mp_in_map," + "local_map_mp_count," + "local_kf_count," + "map_updated," + "total_maps," + //重定位狀態 + "reloc_attempted," + "reloc_success," + "reloc_candidates," + "reloc_bow_pass," + "reloc_pnp_inliers," + //IMU狀態 + "imu_initialized," + // "imu_predicted," + "bias_acc_norm," + "bias_gyro_norm," + "preintegration_ms," + //時間統計 + "pose_pred_ms," + "track_local_map_ms," + "need_new_kf_ms," + "total_tracking_ms," + "source," + "event_type," + "kf_id," + "track_frame_id," + "map_id," + "tracking_state," + "visual_chi2_mean," + "visual_chi2_max," + "imu_chi2_mean," + "imu_chi2_max," + "imu_rot_res_norm," + "imu_vel_res_norm," + "imu_pos_res_norm," + "gyro_rw_chi2," + "acc_rw_chi2," + "pose_update_norm," + "velocity_update_norm," + "bias_update_norm\n"; + mFrameLogFile.flush(); +} + +void Tracking::CloseFrameLog() +{ + if(mFrameLogFile.is_open()) + { + mFrameLogFile.flush(); + mFrameLogFile.close(); + } +} + +void Tracking::WriteFrameLog() +{ + if(!mbFrameLogInit) return; + const FrameLog &L = mCurLog; + + // ── helper lambdas for readable labels ────────────────────────────────── + auto yn = [](bool v) -> const char* { return v ? "yes" : "no"; }; + + auto stateStr = [](int s) -> const char* { + switch(s) { + case -1: return "SYSTEM_NOT_READY"; + case 0: return "NO_IMAGES_YET"; + case 1: return "NOT_INITIALIZED"; + case 2: return "OK"; + case 3: return "RECENTLY_LOST"; + case 4: return "LOST"; + case 5: return "OK_KLT"; + default: return "UNKNOWN"; + } + }; + + auto methodStr = [](int m) -> const char* { + switch(m) { + case 0: return "RefKF"; + case 1: return "MM"; + case 2: return "IMU_MM"; + case 3: return "Reloc"; + default: return "none"; + } + }; + + auto optStr = [](int o) -> const char* { + switch(o) { + case 0: return "PoseOpt"; + case 1: return "InertialLastFrame"; + case 2: return "InertialLastKF"; + default: return "PoseOpt"; + } + }; + // ──────────────────────────────────────────────────────────────────────── + + if(mLogFirstTimestamp < 0.0) mLogFirstTimestamp = L.timestamp; + const double rel_time_s = L.timestamp - mLogFirstTimestamp; + + mFrameLogFile + << L.frame_id << "," + << std::fixed << std::setprecision(6) + << L.timestamp << "," + << rel_time_s << "," + << stateStr(L.state) << "," + << stateStr(L.last_state) << "," + << methodStr(L.tracking_method) << "," + //<< yn(L.wider_window_used) << "," + << L.init_matches << "," // [Monitor] init search matches + << L.initial_matches << "," + << L.matches_before_tlm_opt << "," + // << L.outliers_before_tlm_opt << "," + << L.matches_after_tlm_opt << "," + // << L.outliers_after_tlm_opt << "," + << L.final_inliers << "," + << std::setprecision(4) + // << L.outlier_ratio << "," + << optStr(L.opt_type) << "," + << yn(L.need_new_kf) << "," + << yn(L.new_kf_created) << "," + << L.total_kf_in_map << "," + << L.total_mp_in_map << "," + << L.local_map_mp_count << "," + << L.local_kf_count << "," + << yn(L.map_updated) << "," + << L.total_maps << "," + << yn(L.reloc_attempted) << "," + << yn(L.reloc_success) << "," + << L.reloc_candidates << "," + << L.reloc_bow_pass << "," + << L.reloc_pnp_inliers << "," + << yn(L.imu_initialized) << "," + // << yn(L.imu_predicted) << "," + << std::setprecision(6) + << L.bias_acc_norm << "," + << L.bias_gyro_norm << "," + << std::setprecision(3) + << L.preintegration_ms << "," + << L.pose_pred_ms << "," + << L.track_local_map_ms << "," + << L.need_new_kf_ms << "," + << L.total_tracking_ms << "," + << "Tracking" << "," + << "FRAME_PROCESSED" << "," + << L.kf_id << "," + << L.frame_id << "," + << L.map_id << "," + << stateStr(L.state) << "," + << std::setprecision(6) + << L.visual_chi2_mean << "," + << L.visual_chi2_max << "," + << L.imu_chi2_mean << "," + << L.imu_chi2_max << "," + << L.imu_rot_res_norm << "," + << L.imu_vel_res_norm << "," + << L.imu_pos_res_norm << "," + << L.gyro_rw_chi2 << "," + << L.acc_rw_chi2 << "," + << L.pose_update_norm << "," + << L.velocity_update_norm << "," + << L.bias_update_norm << "\n"; +if(L.frame_id % 5 == 0)// flush every 5 frames + mFrameLogFile.flush(); } +// ── end Tracking Monitor ────────────────────────────────────────────────────── + #ifdef REGISTER_TIMES double calcAverage(vector v_times) { @@ -529,7 +737,7 @@ void Tracking::PrintTimeStats() Tracking::~Tracking() { //f_track_stats.close(); - + CloseFrameLog(); } void Tracking::newParameterLoader(Settings *settings) { @@ -581,8 +789,14 @@ void Tracking::newParameterLoader(Settings *settings) { mDepthMapFactor = 1.0f/mDepthMapFactor; } - mMinFrames = 0; + // mMinFrames = 0; + // mMaxFrames = settings->fps();//開關 限制速度 + mMaxFrames = settings->fps(); + mMinFrames = mMaxFrames / 4; + + + mbRGB = settings->rgb(); //ORB parameters @@ -1154,8 +1368,12 @@ bool Tracking::ParseCamParamFile(cv::FileStorage &fSettings) fps=30; // Max/Min Frames to insert keyframes and to check relocalisation - mMinFrames = 0; + // mMinFrames = 0; + // mMaxFrames = fps;//開關 限制速度 + mMaxFrames = fps; + mMinFrames = mMaxFrames / 4; + cout << "- fps: " << fps << endl; @@ -1793,6 +2011,9 @@ void Tracking::ResetFrameIMU() void Tracking::Track() { + // [Monitor] Reset per-frame log and start total timer + mCurLog = FrameLog{}; + auto t_track_start = std::chrono::steady_clock::now(); if (bStepByStep) { @@ -1809,15 +2030,16 @@ void Tracking::Track() return; } + //假設正常情況一定有 active map，這裡只是做 sanity check Map* pCurrentMap = mpAtlas->GetCurrentMap(); if(!pCurrentMap) { cout << "ERROR: There is not an active map in the atlas" << endl; } - if(mState!=NO_IMAGES_YET) + if(mState!=NO_IMAGES_YET)//如果當前狀態不是 NO_IMAGES_YET，說明之前已經處理過幾幀了，這裡需要檢查時間戳是否有問題 { - if(mLastFrame.mTimeStamp>mCurrentFrame.mTimeStamp) + if(mLastFrame.mTimeStamp>mCurrentFrame.mTimeStamp)//如果當前幀的時間戳比上一幀的還小，說明時間戳出現了問題，這種情況下直接清空 IMU 數據隊列，重置地圖，並返回 { cerr << "ERROR: Frame with a timestamp older than previous frame detected!" << endl; unique_lock lock(mMutexImuQueue); @@ -1825,11 +2047,11 @@ void Tracking::Track() CreateMapInAtlas(); return; } - else if(mCurrentFrame.mTimeStamp>mLastFrame.mTimeStamp+1.0) + else if(mCurrentFrame.mTimeStamp>mLastFrame.mTimeStamp+1.0)//如果當前幀的時間戳比上一幀的還大超過1秒，說明時間戳出現了跳變，這種情況下需要重置 IMU 整合 { // cout << mCurrentFrame.mTimeStamp << ", " << mLastFrame.mTimeStamp << endl; // cout << "id last: " << mLastFrame.mnId << " id curr: " << mCurrentFrame.mnId << endl; - if(mpAtlas->isInertial()) + if(mpAtlas->isInertial())//IMU對時間敏感 { if(mpAtlas->isImuInitialized()) @@ -1866,12 +2088,22 @@ void Tracking::Track() mLastProcessedState=mState; + // [Monitor] snapshot identity & state + mCurLog.frame_id = mCurrentFrame.mnId; + mCurLog.timestamp = mCurrentFrame.mTimeStamp; + mCurLog.last_state = (int)mLastProcessedState; + mCurLog.state = (int)mState; // 記錄這一幀開始時的狀態，避免被後續 mState=OK 覆蓋 + if ((mSensor == System::IMU_MONOCULAR || mSensor == System::IMU_STEREO || mSensor == System::IMU_RGBD) && !mbCreatedMap) { #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_StartPreIMU = std::chrono::steady_clock::now(); #endif + // [Monitor] time PreintegrateIMU + auto t_preimu_start = std::chrono::steady_clock::now(); PreintegrateIMU(); + auto t_preimu_end = std::chrono::steady_clock::now(); + mCurLog.preintegration_ms = std::chrono::duration(t_preimu_end - t_preimu_start).count(); #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_EndPreIMU = std::chrono::steady_clock::now(); @@ -1884,9 +2116,9 @@ void Tracking::Track() // Get Map Mutex -> Map cannot be changed unique_lock lock(pCurrentMap->mMutexMapUpdate); - + //偵測地圖是否更新 mbMapUpdated = false; - + //偵測地圖是否更新 int nCurMapChangeIndex = pCurrentMap->GetMapChangeIndex(); int nMapChangeIndex = pCurrentMap->GetLastMapChange(); if(nCurMapChangeIndex>nMapChangeIndex) @@ -1912,6 +2144,8 @@ void Tracking::Track() if(mState!=OK) // If rightly initialized, mState=OK { mLastFrame = Frame(mCurrentFrame); + mCurLog.state = (int)mState; + WriteFrameLog(); return; } @@ -1928,6 +2162,8 @@ void Tracking::Track() #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_StartPosePred = std::chrono::steady_clock::now(); #endif + // [Monitor] start pose prediction timer + auto t_pose_pred_start = std::chrono::steady_clock::now(); // Initial camera pose estimation using motion model or relocalization (if tracking is lost) if(!mbOnlyTracking) @@ -1945,14 +2181,19 @@ void Tracking::Track() if((!mbVelocity && !pCurrentMap->isImuInitialized()) || mCurrentFrame.mnIdKeyFramesInMap()>10) { // cout << "KF in map: " << pCurrentMap->KeyFramesInMap() << endl; - mState = RECENTLY_LOST; - mTimeStampLost = mCurrentFrame.mTimeStamp; + // mState = RECENTLY_LOST; + // mTimeStampLost = mCurrentFrame.mTimeStamp; + //開關LOST跟RECENTLY_LOST + mState = LOST; } else { @@ -2000,7 +2243,10 @@ void Tracking::Track() else { // Relocalization + mCurLog.reloc_attempted = true; // [Monitor] bOK = Relocalization(); + mCurLog.reloc_success = bOK; // [Monitor] + mCurLog.tracking_method = 3; // [Monitor] //std::cout << "mCurrentFrame.mTimeStamp:" << to_string(mCurrentFrame.mTimeStamp) << std::endl; //std::cout << "mTimeStampLost:" << to_string(mTimeStampLost) << std::endl; if(mCurrentFrame.mTimeStamp-mTimeStampLost>3.0f && !bOK) @@ -2028,8 +2274,42 @@ void Tracking::Track() Verbose::PrintMess("done", Verbose::VERBOSITY_NORMAL); + mCurLog.state = (int)mState; + WriteFrameLog(); return; - } + }//開關lost後還是在同一張圖進行重定位 + + + // else if (mState == LOST) + // { + // if (pCurrentMap->KeyFramesInMap()<10) + // { + // mpSystem->ResetActiveMap(); + // Verbose::PrintMess("Reseting current map...", Verbose::VERBOSITY_NORMAL); + // if(mpLastKeyFrame) + // mpLastKeyFrame = static_cast(NULL); + // mCurLog.state = (int)mState; + // WriteFrameLog(); + // return; + // } + // else + // { + // // SLAM2-style: keep trying relocalization in the same map + // bool bRelocOK = Relocalization(); + // if(bRelocOK) + // { + // mState = OK; + // Verbose::PrintMess("Relocalized in existing map", Verbose::VERBOSITY_NORMAL); + // } + // else + // { + // mCurLog.state = (int)mState; + // WriteFrameLog(); + // return; + // } + // } + // } + } } @@ -2114,11 +2394,19 @@ void Tracking::Track() double timePosePred = std::chrono::duration_cast >(time_EndPosePred - time_StartPosePred).count(); vdPosePred_ms.push_back(timePosePred); #endif + // [Monitor] end pose prediction timer + mCurLog.pose_pred_ms = std::chrono::duration( + std::chrono::steady_clock::now() - t_pose_pred_start).count(); + // [Monitor] record IMU prediction flag + mCurLog.imu_initialized = (bool)mpAtlas->isImuInitialized(); #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_StartLMTrack = std::chrono::steady_clock::now(); #endif + // [Monitor] start TrackLocalMap timer + auto t_tlm_start = std::chrono::steady_clock::now(); + // If we have an initial estimation of the camera pose and matching. Track the local map. if(!mbOnlyTracking) { @@ -2139,6 +2427,10 @@ void Tracking::Track() bOK = TrackLocalMap(); } + // [Monitor] end TrackLocalMap timer + mCurLog.track_local_map_ms = std::chrono::duration( + std::chrono::steady_clock::now() - t_tlm_start).count(); + if(bOK) mState = OK; else if (mState == OK) @@ -2152,10 +2444,12 @@ void Tracking::Track() mpSystem->ResetActiveMap(); } - mState=RECENTLY_LOST; + // mState=RECENTLY_LOST;//開關LOST跟RECENTLY_LOST + mState=LOST; } else - mState=RECENTLY_LOST; // visual to lost + // mState=RECENTLY_LOST; // visual to lost 開關LOST跟RECENTLY_LOST + mState=LOST; /*if(mCurrentFrame.mnId>mnLastRelocFrameId+mMaxFrames) {*/ @@ -2241,13 +2535,21 @@ void Tracking::Track() #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_StartNewKF = std::chrono::steady_clock::now(); #endif + // [Monitor] time NeedNewKeyFrame + auto t_nkf_start = std::chrono::steady_clock::now(); bool bNeedKF = NeedNewKeyFrame(); + mCurLog.need_new_kf_ms = std::chrono::duration( + std::chrono::steady_clock::now() - t_nkf_start).count(); + mCurLog.need_new_kf = bNeedKF; // [Monitor] // Check if we need to insert a new keyframe // if(bNeedKF && bOK) if(bNeedKF && (bOK || (mInsertKFsLost && mState==RECENTLY_LOST && (mSensor == System::IMU_MONOCULAR || mSensor == System::IMU_STEREO || mSensor == System::IMU_RGBD)))) + { CreateNewKeyFrame(); + mCurLog.new_kf_created = true; // [Monitor] + } #ifdef REGISTER_TIMES std::chrono::steady_clock::time_point time_EndNewKF = std::chrono::steady_clock::now(); @@ -2294,6 +2596,43 @@ void Tracking::Track() mLastFrame = Frame(mCurrentFrame); } + // [Monitor] finalize and write log for this frame + { + mCurLog.final_inliers = mnMatchesInliers; + + // outlier ratio (based on TLM post-opt counts) + if(mCurLog.matches_after_tlm_opt > 0) + mCurLog.outlier_ratio = (float)mCurLog.outliers_after_tlm_opt / + (float)mCurLog.matches_after_tlm_opt; + + // map stats + Map* pLogMap = mpAtlas->GetCurrentMap(); + if(pLogMap) + { + mCurLog.total_kf_in_map = (int)pLogMap->KeyFramesInMap(); + mCurLog.total_mp_in_map = (int)mpAtlas->MapPointsInMap(); + mCurLog.map_id = pLogMap->GetId(); + } + if(mpLastKeyFrame) + mCurLog.kf_id = mpLastKeyFrame->mnId; + mCurLog.map_updated = mbMapUpdated; + mCurLog.total_maps = mpAtlas->CountMaps(); + + // IMU bias norms + if(mSensor==System::IMU_MONOCULAR || mSensor==System::IMU_STEREO || mSensor==System::IMU_RGBD) + { + const IMU::Bias &b = mCurrentFrame.mImuBias; + mCurLog.bias_acc_norm = std::sqrt(b.bax*b.bax + b.bay*b.bay + b.baz*b.baz); + mCurLog.bias_gyro_norm = std::sqrt(b.bwx*b.bwx + b.bwy*b.bwy + b.bwz*b.bwz); + } + + // total time + mCurLog.total_tracking_ms = std::chrono::duration( + std::chrono::steady_clock::now() - t_track_start).count(); + + WriteFrameLog(); + } + @@ -2389,6 +2728,8 @@ void Tracking::StereoInitialization() pKFini->AddMapPoint(pNewMP,i); pNewMP->ComputeDistinctiveDescriptors(); pNewMP->UpdateNormalAndDepth(); + if(i < (int)mCurrentFrame.mvGrayValues.size()) + pNewMP->mGray = mCurrentFrame.mvGrayValues[i]; mpAtlas->AddMapPoint(pNewMP); mCurrentFrame.mvpMapPoints[i]=pNewMP; @@ -2410,6 +2751,8 @@ void Tracking::StereoInitialization() pNewMP->ComputeDistinctiveDescriptors(); pNewMP->UpdateNormalAndDepth(); + if(i < (int)mCurrentFrame.mvGrayValues.size()) + pNewMP->mGray = mCurrentFrame.mvGrayValues[i]; mpAtlas->AddMapPoint(pNewMP); mCurrentFrame.mvpMapPoints[i]=pNewMP; @@ -2490,6 +2833,7 @@ void Tracking::MonocularInitialization() // Find correspondences ORBmatcher matcher(0.9,true); int nmatches = matcher.SearchForInitialization(mInitialFrame,mCurrentFrame,mvbPrevMatched,mvIniMatches,100); + mCurLog.init_matches = nmatches; // [Monitor] init search matches // Check if there are enough correspondences if(nmatches<100) @@ -2559,6 +2903,9 @@ void Tracking::CreateInitialMapMonocular() pMP->ComputeDistinctiveDescriptors(); pMP->UpdateNormalAndDepth(); + if(mvIniMatches[i] >= 0 && mvIniMatches[i] < (int)mCurrentFrame.mvGrayValues.size()) + pMP->mGray = mCurrentFrame.mvGrayValues[mvIniMatches[i]]; + //Fill Current Frame structure mCurrentFrame.mvpMapPoints[mvIniMatches[i]] = pMP; mCurrentFrame.mvbOutlier[mvIniMatches[i]] = false; @@ -2586,7 +2933,7 @@ void Tracking::CreateInitialMapMonocular() else invMedianDepth = 1.0f/medianDepth; - if(medianDepth<0 || pKFcur->TrackedMapPoints(1)<50) // TODO Check, originally 100 tracks + if(medianDepth<0 || pKFcur->TrackedMapPoints(1)<100) // TODO Check, originally 100 tracks { Verbose::PrintMess("Wrong initialization, reseting...", Verbose::VERBOSITY_QUIET); mpSystem->ResetActiveMap(); @@ -2735,6 +3082,8 @@ bool Tracking::TrackReferenceKeyFrame() return false; } + mCurLog.initial_matches = nmatches; // [Monitor] BoW matches before PoseOpt + mCurrentFrame.mvpMapPoints = vpMapPointMatches; mCurrentFrame.SetPose(mLastFrame.GetPose()); @@ -2835,6 +3184,8 @@ void Tracking::UpdateLastFrame() } MapPoint* pNewMP = new MapPoint(x3D,mpAtlas->GetCurrentMap(),&mLastFrame,i); + if(i < (int)mLastFrame.mvGrayValues.size()) + pNewMP->mGray = mLastFrame.mvGrayValues[i]; mLastFrame.mvpMapPoints[i]=pNewMP; mlpTemporalPoints.push_back(pNewMP); @@ -2863,6 +3214,8 @@ bool Tracking::TrackWithMotionModel() { // Predict state with IMU if it is initialized and it doesnt need reset PredictStateIMU(); + mCurLog.imu_predicted = true; // [Monitor] + mCurLog.tracking_method = 2; // [Monitor] IMU_MM return true; } else @@ -2893,9 +3246,11 @@ bool Tracking::TrackWithMotionModel() nmatches = matcher.SearchByProjection(mCurrentFrame,mLastFrame,2*th,mSensor==System::MONOCULAR || mSensor==System::IMU_MONOCULAR); Verbose::PrintMess("Matches with wider search: " + to_string(nmatches), Verbose::VERBOSITY_NORMAL); - + mCurLog.wider_window_used = true; // [Monitor] } + mCurLog.initial_matches = nmatches; // [Monitor] projection matches before PoseOpt + if(nmatches<20) { Verbose::PrintMess("Not enough matches!!", Verbose::VERBOSITY_NORMAL); @@ -2953,10 +3308,11 @@ bool Tracking::TrackLocalMap() // We retrieve the local map and try to find matches to points in the local map. mTrackedFr++; - UpdateLocalMap(); - SearchLocalPoints(); + UpdateLocalMap();// 更新 mvpLocalKeyFrames 和 mvpLocalMapPoints + SearchLocalPoints();// 把 local map 中可見的點投影到當前幀，補充匹配 - // TOO check outliers before PO + mCurLog.local_map_mp_count = (int)mvpLocalMapPoints.size(); // [Monitor] + mCurLog.local_kf_count = (int)mvpLocalKeyFrames.size(); // [Monitor] int aux1 = 0, aux2=0; for(int i=0; iisImuInitialized()) - Optimizer::PoseOptimization(&mCurrentFrame); + { + Optimizer::PoseOptimization(&mCurrentFrame);//純視覺優化 + mCurLog.opt_type = 0; // [Monitor] + } else { if(mCurrentFrame.mnId<=mnLastRelocFrameId+mnFramesToResetIMU) { Verbose::PrintMess("TLM: PoseOptimization ", Verbose::VERBOSITY_DEBUG); - Optimizer::PoseOptimization(&mCurrentFrame); + Optimizer::PoseOptimization(&mCurrentFrame);//純視覺優化(不考慮IMU)，因為剛重定位完，IMU狀態不可靠) + mCurLog.opt_type = 0; // [Monitor] } else { @@ -2982,25 +3349,68 @@ bool Tracking::TrackLocalMap() if(!mbMapUpdated) // && (mnMatchesInliers>30)) { Verbose::PrintMess("TLM: PoseInertialOptimizationLastFrame ", Verbose::VERBOSITY_DEBUG); - inliers = Optimizer::PoseInertialOptimizationLastFrame(&mCurrentFrame); // , !mpLastKeyFrame->GetMap()->GetIniertialBA1()); + Optimizer::InertialResidualStats inertialStats; + inliers = Optimizer::PoseInertialOptimizationLastFrame(&mCurrentFrame, false, &inertialStats); // , !mpLastKeyFrame->GetMap()->GetIniertialBA1()); + mCurLog.opt_type = 1; // [Monitor] + mCurLog.visual_chi2_mean = inertialStats.visual_chi2_mean; + mCurLog.visual_chi2_max = inertialStats.visual_chi2_max; + mCurLog.imu_chi2_mean = inertialStats.chi2_mean; + mCurLog.imu_chi2_max = inertialStats.chi2_max; + mCurLog.imu_rot_res_norm = inertialStats.rot_norm; + mCurLog.imu_vel_res_norm = inertialStats.vel_norm; + mCurLog.imu_pos_res_norm = inertialStats.pos_norm; + mCurLog.gyro_rw_chi2 = inertialStats.gyro_rw_chi2; + mCurLog.acc_rw_chi2 = inertialStats.acc_rw_chi2; } else { Verbose::PrintMess("TLM: PoseInertialOptimizationLastKeyFrame ", Verbose::VERBOSITY_DEBUG); - inliers = Optimizer::PoseInertialOptimizationLastKeyFrame(&mCurrentFrame); // , !mpLastKeyFrame->GetMap()->GetIniertialBA1()); + Optimizer::InertialResidualStats inertialStats; + inliers = Optimizer::PoseInertialOptimizationLastKeyFrame(&mCurrentFrame, false, &inertialStats); // , !mpLastKeyFrame->GetMap()->GetIniertialBA1()); + mCurLog.opt_type = 2; // [Monitor] + mCurLog.visual_chi2_mean = inertialStats.visual_chi2_mean; + mCurLog.visual_chi2_max = inertialStats.visual_chi2_max; + mCurLog.imu_chi2_mean = inertialStats.chi2_mean; + mCurLog.imu_chi2_max = inertialStats.chi2_max; + mCurLog.imu_rot_res_norm = inertialStats.rot_norm; + mCurLog.imu_vel_res_norm = inertialStats.vel_norm; + mCurLog.imu_pos_res_norm = inertialStats.pos_norm; + mCurLog.gyro_rw_chi2 = inertialStats.gyro_rw_chi2; + mCurLog.acc_rw_chi2 = inertialStats.acc_rw_chi2; } } } + const Sophus::SE3f TcwAfter = mCurrentFrame.GetPose(); + const Eigen::Vector3f velAfter = mCurrentFrame.GetVelocity(); + const IMU::Bias &biasAfter = mCurrentFrame.mImuBias; + mCurLog.pose_update_norm = (TcwAfter.translation() - TcwBefore.translation()).norm(); + mCurLog.velocity_update_norm = (velAfter - velBefore).norm(); + const double dbax = biasAfter.bax - biasBefore.bax; + const double dbay = biasAfter.bay - biasBefore.bay; + const double dbaz = biasAfter.baz - biasBefore.baz; + const double dbwx = biasAfter.bwx - biasBefore.bwx; + const double dbwy = biasAfter.bwy - biasBefore.bwy; + const double dbwz = biasAfter.bwz - biasBefore.bwz; + mCurLog.bias_update_norm = std::sqrt(dbax*dbax + dbay*dbay + dbaz*dbaz + + dbwx*dbwx + dbwy*dbwy + dbwz*dbwz); + aux1 = 0, aux2 = 0; for(int i=0; i > vDepthIdx; @@ -3312,6 +3724,8 @@ void Tracking::CreateNewKeyFrame() pKF->AddMapPoint(pNewMP,i); pNewMP->ComputeDistinctiveDescriptors(); pNewMP->UpdateNormalAndDepth(); + if(i < (int)mCurrentFrame.mvGrayValues.size()) + pNewMP->mGray = mCurrentFrame.mvGrayValues[i]; mpAtlas->AddMapPoint(pNewMP); mCurrentFrame.mvpMapPoints[i]=pNewMP; @@ -3622,6 +4036,7 @@ bool Tracking::Relocalization() } const int nKFs = vpCandidateKFs.size(); + mCurLog.reloc_candidates = nKFs; // [Monitor] // We perform first an ORB matching with each candidate // If enough matches are found we setup a PnP solver @@ -3661,7 +4076,7 @@ bool Tracking::Relocalization() } } - // Alternatively perform some iterations of P4P RANSAC + mCurLog.reloc_bow_pass = nCandidates; // [Monitor] candidates that passed BoW match // Until we found a camera pose supported by enough inliers bool bMatch = false; ORBmatcher matcher2(0.9,true); @@ -3756,6 +4171,7 @@ bool Tracking::Relocalization() // If the pose is supported by enough inliers stop ransacs and continue if(nGood>=50) { + mCurLog.reloc_pnp_inliers = nGood; // [Monitor] bMatch = true; break; } @@ -4065,6 +4481,7 @@ int Tracking::GetMatchesInliers() return mnMatchesInliers; } + void Tracking::SaveSubTrajectory(string strNameFile_frames, string strNameFile_kf, string strFolder) { mpSystem->SaveTrajectoryEuRoC(strFolder + strNameFile_frames); diff --git a/study/full_inertial_ba_math.md b/study/full_inertial_ba_math.md new file mode 100644 index 00000000000..7dd68a8fbb6 --- /dev/null +++ b/study/full_inertial_ba_math.md @@ -0,0 +1,213 @@ +# FullInertialBA() 數學步驟 + +對應程式：`Optimizer::FullInertialBA()`（`Optimizer.cc:392`） +Edge 實作：`EdgeInertial::computeError()`（`G2oTypes.cc:514`） + +--- + +## 1. 兩種呼叫模式（`bInit` 參數） + +`FullInertialBA` 在初始化和正常 BA 時行為不同： + +| | `bInit = true` | `bInit = false` | +|-|----------------|-----------------| +| bias 頂點 | 所有 KF 共用一組 | 每個 KF 各一組 | +| EdgeGyroRW / EdgeAccRW | 無 | 有 | +| bias prior | 有（`EdgePriorGyro` + `EdgePriorAcc`）| 無 | + +`bInit` 的值由呼叫端的 `priorA` 決定（`LocalMapping.cc:1403`）： + +```cpp +if (priorA != 0.f) + FullInertialBA(..., bInit=true, priorG, priorA); // 初始化、VIBA 1 +else + FullInertialBA(..., bInit=false); // VIBA 2 +``` + +| 階段 | priorA | bInit | +|------|--------|-------| +| 初始化（t ≈ 2s） | 1e5 / 1e10 | true | +| VIBA 1（t > 5s） | 1e5 | true | +| VIBA 2（t > 15s）| 0 | **false** | + +VIBA 2 用 `bInit=false`，代表每個 KF 有各自的 bias，並加上 `EdgeGyroRW`/`EdgeAccRW` 約束 bias 連續性，不加 prior。 + +--- + +## 2. 優化變數（頂點） + +### bInit = true 時 + +| 頂點 | 維度 | 說明 | +|------|------|------| +| `VertexPose` × N | 6 | KF 位姿 $(\mathbf{R}_{wb_i}, \mathbf{t}_{wb_i})$，**可優化** | +| `VertexVelocity` × N | 3 | 各 KF 速度 $\mathbf{v}_i$ | +| `VertexGyroBias` × 1 | 3 | 陀螺儀 bias $\mathbf{b}^g$，所有 KF 共用 | +| `VertexAccBias` × 1 | 3 | 加速度計 bias $\mathbf{b}^a$，所有 KF 共用 | +| `VertexSBAPointXYZ` × M | 3 | MapPoint 3D 位置（marginalized）| + +**注意**：沒有 `VertexGDir`、`VertexScale`，因為 scale 和重力對齊在 `InertialOptimization` 後已套用到地圖，不再是優化變數。 + +--- + +## 3. 總 Cost Function + +$$\min \left( +\underbrace{\sum_{i=1}^{N} \|\mathbf{r}_{\mathcal{I}_{i-1,i}}\|^2_{\Sigma^{-1}_i}}_{\text{IMU}} + +\underbrace{\sum_{j} \rho_{\text{Hub}}\!\left(\|\mathbf{r}_{\text{reproj},j}\|^2_{\Sigma^{-1}_j}\right)}_{\text{視覺}} + +\underbrace{\|\mathbf{0} - \mathbf{b}^g\|^2_{\text{priorG}\cdot I} + \|\mathbf{0} - \mathbf{b}^a\|^2_{\text{priorA}\cdot I}}_{\text{bias prior（bInit=true 才有）}} +\right)$$ + +--- + +## 4. IMU Preintegration Residual（`EdgeInertial`） + +每對相鄰 KF 建一條邊，連接 **6 個頂點**（比 `EdgeInertialGS` 少 scale 和 Rwg）： + +$$(\mathbf{T}_{i-1},\ \mathbf{v}_{i-1},\ \mathbf{b}^g_{i-1},\ \mathbf{b}^a_{i-1},\ \mathbf{T}_i,\ \mathbf{v}_i)$$ + +重力向量 $\mathbf{g}$ 在這裡是**固定常數**（已在 `InertialOptimization` 估好）： + +$$\mathbf{g} = [0,\ 0,\ -9.81]^T \quad \text{（world frame，z 軸向上）}$$ + +### 4.1 預積分量的 bias 修正（與 EdgeInertialGS 相同） + +$$\Delta\mathbf{R}(\mathbf{b}) = \bar{\Delta\mathbf{R}} \cdot \text{Exp}\!\left(\mathbf{J}^R_g \cdot \delta\mathbf{b}^g\right)$$ + +$$\Delta\mathbf{v}(\mathbf{b}) = \bar{\Delta\mathbf{v}} + \mathbf{J}^v_g \cdot \delta\mathbf{b}^g + \mathbf{J}^v_a \cdot \delta\mathbf{b}^a$$ + +$$\Delta\mathbf{p}(\mathbf{b}) = \bar{\Delta\mathbf{p}} + \mathbf{J}^p_g \cdot \delta\mathbf{b}^g + \mathbf{J}^p_a \cdot \delta\mathbf{b}^a$$ + +### 4.2 Residual（`G2oTypes.cc:514`） + +**旋轉殘差**（與 EdgeInertialGS 相同）： + +$$\mathbf{r}_{\Delta R} = \text{Log}\!\left(\Delta\mathbf{R}(\mathbf{b})^T \cdot \mathbf{R}_{wb_1}^T \cdot \mathbf{R}_{wb_2}\right)$$ + +**速度殘差**（無 scale $s$）： + +$$\mathbf{r}_{\Delta v} = \mathbf{R}_{wb_1}^T \left(\mathbf{v}_2 - \mathbf{v}_1 - \mathbf{g}\cdot\Delta t\right) - \Delta\mathbf{v}(\mathbf{b})$$ + +**位置殘差**（無 scale $s$）： + +$$\mathbf{r}_{\Delta p} = \mathbf{R}_{wb_1}^T \left(\mathbf{t}_{wb_2} - \mathbf{t}_{wb_1} - \mathbf{v}_1\Delta t - \frac{1}{2}\mathbf{g}\Delta t^2\right) - \Delta\mathbf{p}(\mathbf{b})$$ + +和 `EdgeInertialGS` 的差異：速度和位置殘差中不乘 scale $s$，因為 scale 已套用到整張地圖。 + +### 4.3 Information Matrix + +$$\Omega_i = \mathbf{C}_{[0:9,\ 0:9]}^{-1}$$ + +與 `EdgeInertialGS` 相同，取預積分 covariance 矩陣的前 9×9 塊反矩陣。 + +### 4.4 Robust Kernel + +``` +RobustKernelHuber，delta = sqrt(16.92) +``` + +`EdgeInertialGS` 沒有 robust kernel，`EdgeInertial` 有。 + +--- + +## 5. Bias Random Walk（`EdgeGyroRW` + `EdgeAccRW`） + +**僅 `bInit = false` 時使用。** + +IMU 的 bias 不是完全固定的常數，會隨時間緩慢漂移（random walk 模型）。這條邊約束相鄰 KF 的 bias 差異不要太大。 + +### Residual（`G2oTypes.h:645`、`G2oTypes.h:681`） + +$$\mathbf{r}_{\text{gyroRW}} = \mathbf{b}^g_{i} - \mathbf{b}^g_{i-1}$$ + +$$\mathbf{r}_{\text{accRW}} = \mathbf{b}^a_{i} - \mathbf{b}^a_{i-1}$$ + +bias 在相鄰 KF 間應盡量不變，差值越小越好。 + +### Information Matrix + +$$\Omega_{\text{gyroRW}} = \mathbf{C}_{[9:12,\ 9:12]}^{-1}$$ + +$$\Omega_{\text{accRW}} = \mathbf{C}_{[12:15,\ 12:15]}^{-1}$$ + +取預積分 covariance 矩陣中 gyro bias（9:12）和 acc bias（12:15）對應的塊反矩陣。 + +### Jacobian + +$$\frac{\partial \mathbf{r}}{\partial \mathbf{b}^g_{i-1}} = -I_3, \quad \frac{\partial \mathbf{r}}{\partial \mathbf{b}^g_i} = +I_3$$ + +--- + +## 6. 視覺 Reprojection Error + +### Monocular（`EdgeMono`，`G2oTypes.h:353`） + +$$\mathbf{r}_{\text{mono}} = \mathbf{u}_{obs} - \Pi\!\left(\mathbf{T}_{cw} \cdot \mathbf{X}_w\right)$$ + +- $\mathbf{u}_{obs} \in \mathbb{R}^2$：觀測到的像素座標 +- $\Pi$：投影函式（針孔或魚眼，依 camera model） +- $\mathbf{T}_{cw}$：world-to-camera 變換，由 `VertexPose` 提供 +- information：$\sigma^{-2}_{\text{level}} \cdot I_2$（依特徵點金字塔層數縮放） +- Huber kernel：$\delta = \sqrt{5.991}$（$\chi^2$，2 自由度，95%） + +### Stereo（`EdgeStereo`，`G2oTypes.h:435`） + +$$\mathbf{r}_{\text{stereo}} = \begin{bmatrix} u \\ v \\ u_r \end{bmatrix}_{obs} - \Pi_{\text{stereo}}\!\left(\mathbf{T}_{cw} \cdot \mathbf{X}_w\right)$$ + +- 多一維：右相機的 $u_r$（水平視差） +- information：$\sigma^{-2}_{\text{level}} \cdot I_3$ +- Huber kernel：$\delta = \sqrt{7.815}$（$\chi^2$，3 自由度，95%） + +--- + +## 7. Bias Prior（bInit = true 時） + +與 `InertialOptimization` 完全相同： + +$$\mathbf{r}_{\text{prior},g} = \mathbf{0} - \mathbf{b}^g, \quad \Omega = \text{priorG} \cdot I_3$$ + +$$\mathbf{r}_{\text{prior},a} = \mathbf{0} - \mathbf{b}^a, \quad \Omega = \text{priorA} \cdot I_3$$ + +--- + +## 8. 優化流程 + +``` +1. 建立頂點： + VertexPose × N（可優化） + VertexVelocity × N + VertexGyroBias × N（bInit=false）或 × 1（bInit=true） + VertexAccBias × N（bInit=false）或 × 1（bInit=true） + VertexSBAPointXYZ × M（marginalized） + +2. 建立邊： + EdgeInertial × N → IMU preintegration（Huber kernel） + EdgeGyroRW × N → bias 連續性（bInit=false 才有） + EdgeAccRW × N → bias 連續性（bInit=false 才有） + EdgeMono / EdgeStereo × K → 視覺 reprojection（Huber kernel） + EdgePriorGyro → bias prior（bInit=true 才有） + EdgePriorAcc → bias prior（bInit=true 才有） + +3. Levenberg-Marquardt 迭代 100 次（its 參數傳入） + λ 初始值 = 1e-5（比 InertialOptimization 的 1e3 小很多） + +4. MapPoint 被 marginalized，Schur complement 消去後 + 實際求解變數只剩 KF 的位姿、速度、bias +``` + +--- + +## 9. 和 InertialOptimization 的關鍵差異 + +| | `InertialOptimization` | `FullInertialBA` | +|-|------------------------|-----------------| +| KF 位姿 | 固定 | **可優化** | +| scale $s$ | 優化變數 | 不存在 | +| 重力 $\mathbf{R}_{wg}$ | 優化變數 | 不存在（$\mathbf{g}$ 為固定常數）| +| bias | 共用一組 | 每 KF 一組（bInit=false）| +| IMU edge | `EdgeInertialGS`（含 $s$, $\mathbf{R}_{wg}$）| `EdgeInertial`（不含）| +| 速度/位置殘差 | 乘 scale $s$ | **不乘** $s$ | +| bias 連續性 | 無 | `EdgeGyroRW` + `EdgeAccRW` | +| 視覺項 | 無 | `EdgeMono` + `EdgeStereo` | +| IMU edge robust kernel | 無 | Huber（$\delta = \sqrt{16.92}$）| +| LM 初始 $\lambda$ | 1e3（有 prior 時）| 1e-5 | diff --git a/study/imu_bias_optimization_flowchart.md b/study/imu_bias_optimization_flowchart.md new file mode 100644 index 00000000000..59621da060f --- /dev/null +++ b/study/imu_bias_optimization_flowchart.md @@ -0,0 +1,174 @@ +# IMU Bias 參與優化流程圖 + +## 1. 一句話結論 + +ORB-SLAM3 不是把 IMU bias 當成固定常數，而是把 gyro bias 和 accelerometer bias 放進最佳化問題中，和速度、重力方向、尺度一起被估計。初始化階段先估一組共同 bias，後續 Full Inertial BA / Tracking 再持續更新每個 KeyFrame 或 Frame 的 bias。 + +## 2. Vertex 和 Edge 是什麼 + +在 ORB-SLAM3 使用的非線性最小平方最佳化裡，可以先不要把 vertex / edge 想成抽象圖論。比較實際的說法是： + +- **Vertex**：最佳化要求解的未知變數。 +- **Edge**：一條誤差方程式，也就是 residual term。 +- **Optimizer**：反覆調整未知變數，讓所有誤差項的加權平方和最小。 + +簡單講，vertex 是「我要解什麼」，edge 是「我用哪一條量測方程式檢查它合不合理」。 + +```mermaid +flowchart LR + A["Vertex: 未知變數"] --> C["Edge: 誤差方程式"] + B["量測資料: 影像或 IMU"] --> C + C --> D["Residual: r(x)"] + D --> E["Cost: 加權平方誤差"] + E --> F["Optimizer"] + F --> G["更新未知變數"] + G --> A +``` + +在 ORB-SLAM3 裡可以這樣對應： + +| 類型 | 意義 | ORB-SLAM3 例子 | +| --- | --- | --- | +| Vertex | 待估未知變數 | camera pose、KeyFrame pose、MapPoint 位置、velocity、gyro bias、acc bias、scale、gravity direction | +| Edge | 誤差方程式 / residual term | reprojection error、IMU preintegration error、bias prior error、bias random-walk error | +| Measurement | 觀測資料 | feature pixel position、stereo depth、IMU preintegration、bias prior | +| Cost | 要最小化的加權誤差 | reprojection cost、IMU cost、bias drift cost | + +更正式的說法： + +```text +未知變數 x = 所有 vertex + +最小化： + + sum over all residual terms: + r_i(x)^T Omega_i r_i(x) +``` + +其中 `x` 是所有待估未知變數，`r_i(x)` 是第 i 條誤差方程式根據目前變數算出的 residual，`Omega_i` 是這條量測的資訊矩陣，也就是信任程度。ORB-SLAM3 / g2o 把未知變數叫 vertex，把誤差方程式叫 edge。 + +## 3. ORB-SLAM3 中常見的未知變數與誤差項 + +### 3.1 視覺重投影誤差 + +```mermaid +flowchart LR + A["相機 / KeyFrame pose: 未知變數"] --> C["重投影誤差方程式"] + B["MapPoint 3D 位置: 未知變數"] --> C + D["影像上的特徵點觀測"] --> C + C --> E["Reprojection residual"] +``` + +意思是：如果目前估計的相機位姿和 3D 地圖點是對的，把 3D 點投影回影像時，應該會落在觀測到的 feature 附近。 + +### 3.2 IMU preintegration 誤差 + +```mermaid +flowchart LR + A["上一個 KF 狀態: pose, velocity, bias"] --> E["IMU preintegration 誤差方程式"] + B["目前 KF 狀態: pose, velocity"] --> E + C["IMU preintegration measurement: DeltaR / DeltaV / DeltaP"] --> E + D["Gravity / scale"] --> E + E --> F["IMU residual"] +``` + +意思是：如果兩個 KeyFrame 的 pose、velocity、bias、gravity、scale 是合理的，那它們之間的運動應該和 IMU 預積分結果一致。 + +### 3.3 Bias prior 和 random walk 誤差 + +```mermaid +flowchart LR + A["Gyro bias: 未知變數"] --> B["Bias prior 誤差"] + C["Acc bias: 未知變數"] --> D["Bias prior 誤差"] + + E["KF i 的 bias"] --> G["Bias random-walk 誤差"] + F["KF i+1 的 bias"] --> G + G --> H["限制 bias 不要突然跳太大"] +``` + +意思是： + +- prior 誤差：初始化時給 bias 一個合理先驗，例如接近 0。 +- random-walk 誤差：後續 BA 允許 bias 慢慢變，但不希望相鄰 KeyFrame 的 bias 突然差很多。 + +## 4. IMU 初始化中的 bias 優化流程 + +```mermaid +flowchart TD + A["Tracking 持續接收影像與 IMU"] --> B["建立 KeyFrame"] + B --> C["兩個 KeyFrame 之間做 IMU Preintegration"] + C --> D["LocalMapping 累積足夠 KeyFrame"] + D --> E["呼叫 InitializeIMU"] + + E --> F["建立初始化最佳化問題"] + F --> G1["固定 KeyFrame pose"] + F --> G2["加入每個 KeyFrame velocity"] + F --> G3["加入一組共同 gyro bias"] + F --> G4["加入一組共同 acc bias"] + F --> G5["加入 gravity direction"] + F --> G6["加入 scale"] + + G1 --> H["建立 IMU residual 誤差項"] + G2 --> H + G3 --> H + G4 --> H + G5 --> H + G6 --> H + + H --> I["用 bias 修正 preintegrated DeltaR / DeltaV / DeltaP"] + I --> J["最小化 IMU residual"] + J --> K["得到最佳化後的 gyro bias / acc bias / gravity / scale / velocity"] + K --> L["把 bias 寫回所有 KeyFrame"] + L --> M["必要時重新 preintegrate IMU"] + M --> N["同步 bias 到 Tracking"] +``` + +## 5. Bias 在 IMU residual 中的位置 + +```mermaid +flowchart LR + A["前一個 KeyFrame 狀態: pose, velocity"] --> E["IMU residual"] + B["目前 KeyFrame 狀態: pose, velocity"] --> E + C["Gyro bias"] --> D["修正 IMU preintegration"] + D --> E + F["Accelerometer bias"] --> D + + E --> G["rotation residual"] + E --> H["velocity residual"] + E --> I["position residual"] + + C --> G + C --> H + C --> I + F --> H + F --> I +``` + +重點： + +- Gyro bias 會影響 rotation、velocity、position residual。 +- Accelerometer bias 主要影響 velocity、position residual。 +- 所以 bias 不是事後校正，而是直接參與最佳化的狀態變數。 + +## 6. 初始化與後續 BA 的差異 + +```mermaid +flowchart TD + A["IMU 初始化階段"] --> B["使用一組 shared bias"] + B --> C["所有 IMU 誤差項共用同一組 gyro / acc bias"] + C --> D["估計初始 bias, gravity, scale, velocity"] + + E["Full Inertial BA / 後續優化"] --> F["每個 KeyFrame 可有自己的 bias"] + F --> G["相鄰 KeyFrame bias 之間加入 random walk 誤差"] + G --> H["允許 bias 緩慢變化，但避免跳動太大"] +``` + +## 7. 適合口頭報告的說法 + +### 7.1 Vertex / Edge 版本 + +在 ORB-SLAM3 的非線性最小平方問題裡，vertex 可以理解成要求解的未知變數，例如相機位姿、地圖點、速度、IMU bias、尺度和重力方向。Edge 可以理解成一條誤差方程式，它會把量測資料和目前未知變數代進去，算出 residual。例如視覺的誤差方程式會算重投影誤差，IMU 的誤差方程式會算預積分運動和目前狀態預測之間的差。Optimizer 的工作就是調整這些未知變數，讓全部 residual 的加權平方和最小。 + +### 7.2 IMU Bias 版本 + +ORB-SLAM3 在 IMU 初始化時，會先把視覺估出的 KeyFrame pose 固定，然後利用 KeyFrame 之間的 IMU preintegration 建立慣性誤差項。這個最佳化問題裡，gyro bias 和 accelerometer bias 會被當成待估變數。每一條 IMU residual 都會使用目前估計的 bias 去修正預積分量，因此 bias 會直接影響旋轉、速度與位置誤差。最佳化完成後，系統會把估出的 bias 寫回 KeyFrame，並同步到 Tracking。後續 Full Inertial BA 則進一步把 bias 放入更完整的 visual-inertial 最小平方問題中，並用 random walk model 約束相鄰 KeyFrame 的 bias 變化。 diff --git a/study/imu_init_full_derivation.md b/study/imu_init_full_derivation.md new file mode 100644 index 00000000000..bda672ee3cf --- /dev/null +++ b/study/imu_init_full_derivation.md @@ -0,0 +1,1350 @@ +# InertialOptimization 完整推導（k=2） + +從頭到尾推導一次 ORB-SLAM3 IMU 初始化（InertialOptimization），設定 k=2（2 個 KF：KF₀ 和 KF₁，1 條 EdgeInertialGS）。每個數學步驟對應到具體程式碼行號。 + +--- + +## Step 1：初始值計算 + +**對應程式：** `LocalMapping.cc:1322-1356` + +進入 `InitializeIMU` 時，已知量（從視覺 SLAM）： +- $\mathbf{R}_{wb_0}, \mathbf{t}_{wb_0}$：KF₀ 位姿（fixed） +- $\mathbf{R}_{wb_1}, \mathbf{t}_{wb_1}$：KF₁ 位姿（fixed） +- KF₀→KF₁ 的預積分量：$\bar{\Delta\mathbf{R}}, \bar{\Delta\mathbf{v}}, \bar{\Delta\mathbf{p}}$ +- 對 bias 的 Jacobian：$\mathbf{J}^R_g, \mathbf{J}^v_g, \mathbf{J}^v_a, \mathbf{J}^p_g, \mathbf{J}^p_a$ +- Covariance $\mathbf{C} \in \mathbb{R}^{15\times15}$ +- 時間差 $\Delta t$ + +### 1.1 粗估速度（line 1336） + +對 KF₁（唯一有 mPrevKF 的 KF）： + +$$\mathbf{v}_0 = \mathbf{v}_1 = \frac{\mathbf{t}_{wb_1} - \mathbf{t}_{wb_0}}{\Delta t}$$ + +兩個 KF 的速度被設成同一個粗估值。 + +### 1.2 累積重力方向（line 1335） + +$$\mathbf{dirG} = -\mathbf{R}_{wb_0} \cdot \bar{\Delta\mathbf{v}}_{\text{updated}}$$ + +`GetUpdatedDeltaVelocity()` 回傳的是用目前 bias（這裡 b=0）修正後的 $\bar{\Delta\mathbf{v}}$。$\bar{\Delta\mathbf{v}}$ 是 IMU body frame 下從 KF₀ 到 KF₁ 的速度變化（包含重力影響）。 + +### 1.3 計算 Rwg（line 1341-1349） + +歸一化： + +$$\hat{\mathbf{dirG}} = \frac{\mathbf{dirG}}{\|\mathbf{dirG}\|}$$ + +定義目標方向： + +$$\mathbf{g}_I = [0, 0, -1]^T$$ + +算旋轉軸（外積）： + +$$\mathbf{v} = \mathbf{g}_I \times \hat{\mathbf{dirG}}$$ + +算旋轉角： + +$$\cos\theta = \mathbf{g}_I \cdot \hat{\mathbf{dirG}}, \quad \theta = \arccos(\cos\theta)$$ + +組成軸角向量： + +$$\mathbf{v}_{zg} = \mathbf{v} \cdot \frac{\theta}{\|\mathbf{v}\|}$$ + +轉成旋轉矩陣： + +$$\mathbf{R}_{wg} = \text{Exp}(\mathbf{v}_{zg})$$ + +### 1.4 其他初始值 + +``` +s = 1.0 (line 1357) +bg = 0, ba = 0 (b 初始為零) +``` + +--- + +## Step 2：InertialOptimization 建立 Graph + +**對應程式：** `Optimizer.cc:3042-3170` + +### 2.1 LM 求解器設定（line 3057-3061） + +```cpp +g2o::OptimizationAlgorithmLevenberg* solver = ...; +if (priorG != 0.f) + solver->setUserLambdaInit(1e3); // 初始 λ = 1000 +``` + +priorG=0 時用 g2o 預設 $\lambda$（通常根據 H 的對角線元素自動算）。 + +### 2.2 頂點清單 + +k=2 時： + +| 頂點 | 維度 | 初始值 | fixed? | +|------|------|--------|--------| +| `VertexPose` VP₀ | 6 | $\mathbf{R}_{wb_0}, \mathbf{t}_{wb_0}$ | **true** | +| `VertexPose` VP₁ | 6 | $\mathbf{R}_{wb_1}, \mathbf{t}_{wb_1}$ | **true** | +| `VertexVelocity` VV₀ | 3 | $\mathbf{v}_0$ | false | +| `VertexVelocity` VV₁ | 3 | $\mathbf{v}_1$ | false | +| `VertexGyroBias` VG | 3 | $[0,0,0]^T$ | false | +| `VertexAccBias` VA | 3 | $[0,0,0]^T$ | false | +| `VertexGDir` VGDir | 2 | $\mathbf{R}_{wg}$ | false | +| `VertexScale` VS | 1 | 1.0 | mono=false / stereo=true | + +優化變數共 **3+3+3+3+2+1 = 15 維**（mono），位姿 12 維被 fix 不算。 + +### 2.3 邊清單 + +**EdgePriorGyro**（line 3119-3123）： +``` +連接：VG +information = priorG · I₃ +``` + +**EdgePriorAcc**（line 3113-3118）： +``` +連接：VA +information = priorA · I₃ +``` + +**EdgeInertialGS**（line 3151-3160）： +``` +連接：VP₀, VV₀, VG, VA, VP₁, VV₁, VGDir, VS +information = (C[0:9, 0:9])^{-1}（並做特徵值正則化，line 600-609） +``` + +--- + +## Step 3：computeError() + +**對應程式：** `G2oTypes.cc:617-640` + +### 3.1 Bias 修正（ImuTypes.cc:283-308） + +設 $\bar{\mathbf{b}}$ 是預積分時的 bias 線性化點（這裡是 0），目前估計 $\mathbf{b} = (\mathbf{b}^a, \mathbf{b}^g)$，差值： + +$$\delta\mathbf{b}^g = \mathbf{b}^g - \bar{\mathbf{b}}^g, \quad \delta\mathbf{b}^a = \mathbf{b}^a - \bar{\mathbf{b}}^a$$ + +第一次迭代 $\mathbf{b} = 0$，所以 $\delta\mathbf{b}^g = \delta\mathbf{b}^a = 0$，但之後每次迭代會變。 + +修正後的預積分量： + +$$\Delta\mathbf{R}(\mathbf{b}) = \bar{\Delta\mathbf{R}} \cdot \text{Exp}(\mathbf{J}^R_g \cdot \delta\mathbf{b}^g)$$ + +$$\Delta\mathbf{v}(\mathbf{b}) = \bar{\Delta\mathbf{v}} + \mathbf{J}^v_g \cdot \delta\mathbf{b}^g + \mathbf{J}^v_a \cdot \delta\mathbf{b}^a$$ + +$$\Delta\mathbf{p}(\mathbf{b}) = \bar{\Delta\mathbf{p}} + \mathbf{J}^p_g \cdot \delta\mathbf{b}^g + \mathbf{J}^p_a \cdot \delta\mathbf{b}^a$$ + +### 3.2 重力向量（line 631） + +$$\mathbf{g} = \mathbf{R}_{wg} \cdot \mathbf{g}_I, \quad \mathbf{g}_I = [0, 0, -9.81]^T$$ + +### 3.3 旋轉殘差（line 636） + +$$\mathbf{r}_{\Delta R} = \text{Log}\!\left(\Delta\mathbf{R}(\mathbf{b})^T \cdot \mathbf{R}_{wb_0}^T \cdot \mathbf{R}_{wb_1}\right)$$ + +定義中間量： + +$$\mathbf{e}_R = \Delta\mathbf{R}(\mathbf{b})^T \cdot \mathbf{R}_{wb_0}^T \cdot \mathbf{R}_{wb_1}$$ + +$$\mathbf{r}_{\Delta R} = \text{Log}(\mathbf{e}_R) \in \mathbb{R}^3$$ + +### 3.4 速度殘差（line 637） + +$$\mathbf{r}_{\Delta v} = \mathbf{R}_{wb_0}^T \left(s(\mathbf{v}_1 - \mathbf{v}_0) - \mathbf{g}\Delta t\right) - \Delta\mathbf{v}(\mathbf{b}) \in \mathbb{R}^3$$ + +### 3.5 位置殘差（line 638） + +$$\mathbf{r}_{\Delta p} = \mathbf{R}_{wb_0}^T \left(s(\mathbf{t}_{wb_1} - \mathbf{t}_{wb_0} - \mathbf{v}_0 \Delta t) - \frac{1}{2}\mathbf{g}\Delta t^2\right) - \Delta\mathbf{p}(\mathbf{b}) \in \mathbb{R}^3$$ + +### 3.6 合併成 9 維殘差 + +$$\mathbf{r}_{\mathcal{I}} = \begin{bmatrix} \mathbf{r}_{\Delta R} \\ \mathbf{r}_{\Delta v} \\ \mathbf{r}_{\Delta p} \end{bmatrix} \in \mathbb{R}^9$$ + +### 3.7 Prior 殘差（G2oTypes.h:779, 805） + +$$\mathbf{r}_{pg} = \mathbf{0} - \mathbf{b}^g \in \mathbb{R}^3$$ + +$$\mathbf{r}_{pa} = \mathbf{0} - \mathbf{b}^a \in \mathbb{R}^3$$ + +### 3.8 Total cost + +$$C = \mathbf{r}_{\mathcal{I}}^T \mathbf{\Omega}_I \mathbf{r}_{\mathcal{I}} + \mathbf{r}_{pg}^T (\text{priorG} \cdot I_3) \mathbf{r}_{pg} + \mathbf{r}_{pa}^T (\text{priorA} \cdot I_3) \mathbf{r}_{pa}$$ + +其中 $\mathbf{\Omega}_I = (\mathbf{C}_{[0:9,0:9]})^{-1}$。 + +--- + +## Step 4：linearizeOplus() + +**對應程式：** `G2oTypes.cc:642-738`（EdgeInertialGS）、`G2oTypes.cc:762-775`（EdgePriorGyro / EdgePriorAcc） + +優化變數順序：$\mathbf{x} = [\mathbf{v}_0, \mathbf{v}_1, \mathbf{b}^g, \mathbf{b}^a, \mathbf{R}_{wg}, s]$，共 15 維（mono；stereo 時 $s$ fixed，少 1 維變 14 維）。 + +每條邊各自負責計算自己對所連接頂點的 Jacobian，存進 `_jacobianOplus[i]`。g2o 在組裝 H 時自動跳過 fixed 頂點對應的 block。 + +### 4.1 EdgeInertialGS Jacobian（9×15，連接 8 頂點） + +中間量： +- $\mathbf{R}_{bw_0} = \mathbf{R}_{wb_0}^T$ +- $\mathbf{e}_R = \Delta\mathbf{R}(\mathbf{b})^T \mathbf{R}_{bw_0} \mathbf{R}_{wb_1}$（這是 $\mathbf{r}_{\Delta R}$ 取 Log 之前的旋轉矩陣） +- $\mathbf{J}_r^{-1} = \text{InverseRightJacobianSO3}(\text{Log}(\mathbf{e}_R))$ +- $\mathbf{G}_m = \begin{bmatrix} 0 & -9.81 \\ 9.81 & 0 \\ 0 & 0 \end{bmatrix}$ +- $\frac{\partial\mathbf{g}}{\partial\theta} = \mathbf{R}_{wg} \cdot \mathbf{G}_m$ +- $\delta\mathbf{b}^g$ 的當前值（從 GetDeltaBias 拿） + +**InverseRightJacobianSO3 是什麼？** + +SO(3) 的右 Jacobian $\mathbf{J}_r(\boldsymbol{\phi})$ 連接「旋轉向量空間」和「旋轉矩陣流形」之間的微分關係： + +$$\text{Exp}(\boldsymbol{\phi} + \delta\boldsymbol{\phi}) \approx \text{Exp}(\boldsymbol{\phi}) \cdot \text{Exp}(\mathbf{J}_r(\boldsymbol{\phi}) \cdot \delta\boldsymbol{\phi})$$ + +逆右 Jacobian $\mathbf{J}_r^{-1}(\boldsymbol{\phi})$ 是反方向： + +$$\text{Log}(\text{Exp}(\boldsymbol{\phi}) \cdot \text{Exp}(\delta\boldsymbol{\phi})) \approx \boldsymbol{\phi} + \mathbf{J}_r^{-1}(\boldsymbol{\phi}) \cdot \delta\boldsymbol{\phi}$$ + +**為什麼這裡需要 $\mathbf{J}_r^{-1}$？** + +旋轉殘差 $\mathbf{r}_{\Delta R} = \text{Log}(\mathbf{e}_R)$ 經過 Log 映射，要對 $\mathbf{R}_{wb_1}$、$\mathbf{R}_{wb_0}$、$\mathbf{b}^g$ 等變數做偏微分時，需要 $\mathbf{J}_r^{-1}$ 把流形上的變化「拉回」到向量空間： + +$$\frac{\partial \text{Log}(\mathbf{e}_R)}{\partial \mathbf{R}_{wb_1}} \propto \mathbf{J}_r^{-1}(\text{Log}(\mathbf{e}_R))$$ + +所以你會在 $\partial \mathbf{r}_{\Delta R} / \partial \cdot$ 的所有公式裡看到 $\mathbf{J}_r^{-1}$ 出現。 + +**閉式公式**（G2oTypes.cc:821-832）： + +設 $\boldsymbol{\phi} = (x, y, z)$，$d = \|\boldsymbol{\phi}\|$，$\mathbf{W} = [\boldsymbol{\phi}]_\times$（hat operator）： + +$$\mathbf{J}_r^{-1}(\boldsymbol{\phi}) = \mathbf{I}_3 + \frac{1}{2}\mathbf{W} + \left(\frac{1}{d^2} - \frac{1+\cos d}{2d \sin d}\right)\mathbf{W}^2$$ + +當 $d < 10^{-5}$（小角度）退化為 $\mathbf{I}_3$，避免數值問題。 + +對應的 `RightJacobianSO3`： + +$$\mathbf{J}_r(\boldsymbol{\phi}) = \mathbf{I}_3 - \frac{1-\cos d}{d^2}\mathbf{W} + \frac{d - \sin d}{d^3}\mathbf{W}^2$$ + +**為什麼 $\partial \mathbf{r}_{\Delta R} / \partial \mathbf{b}^g$ 裡同時出現 $\mathbf{J}_r^{-1}$ 和 $\mathbf{J}_r$？** + +$$\frac{\partial \mathbf{r}_{\Delta R}}{\partial \mathbf{b}^g} = -\mathbf{J}_r^{-1}(\mathbf{r}_{\Delta R}) \cdot \mathbf{e}_R^T \cdot \mathbf{J}_r(\mathbf{J}^R_g \delta\mathbf{b}^g) \cdot \mathbf{J}^R_g$$ + +- $\mathbf{J}_r^{-1}(\mathbf{r}_{\Delta R})$：把 Log 後的向量微分**拉回**到流形 +- $\mathbf{J}_r(\mathbf{J}^R_g \delta\mathbf{b}^g)$：bias 變化引起 $\Delta\mathbf{R}(\mathbf{b}) = \bar{\Delta\mathbf{R}} \cdot \text{Exp}(\mathbf{J}^R_g \delta\mathbf{b}^g)$，這個 Exp 的右 Jacobian 把 bias 空間的小變化**推到**流形上的旋轉變化 +- 兩個方向的「轉換」串接，加上鏈式法則的中間項 $\mathbf{e}_R^T$ 和 $\mathbf{J}^R_g$，得到完整的偏微分 + +**對 $\mathbf{v}_0$（3 欄，line 690-693）**： + +$$\frac{\partial \mathbf{r}_{\Delta R}}{\partial \mathbf{v}_0} = \mathbf{0}_{3\times 3}$$ + +$$\frac{\partial \mathbf{r}_{\Delta v}}{\partial \mathbf{v}_0} = -s \cdot \mathbf{R}_{bw_0}$$ + +$$\frac{\partial \mathbf{r}_{\Delta p}}{\partial \mathbf{v}_0} = -s \cdot \mathbf{R}_{bw_0} \cdot \Delta t$$ + +**對 $\mathbf{v}_1$（3 欄，line 717-719）**： + +$$\frac{\partial \mathbf{r}_{\Delta R}}{\partial \mathbf{v}_1} = \mathbf{0}_{3\times 3}$$ + +$$\frac{\partial \mathbf{r}_{\Delta v}}{\partial \mathbf{v}_1} = s \cdot \mathbf{R}_{bw_0}$$ + +$$\frac{\partial \mathbf{r}_{\Delta p}}{\partial \mathbf{v}_1} = \mathbf{0}_{3\times 3}$$ + +**對 $\mathbf{b}^g$（3 欄，line 695-699）**： + +$$\frac{\partial \mathbf{r}_{\Delta R}}{\partial \mathbf{b}^g} = -\mathbf{J}_r^{-1} \cdot \mathbf{e}_R^T \cdot \mathbf{J}_r(\mathbf{J}^R_g \cdot \delta\mathbf{b}^g) \cdot \mathbf{J}^R_g$$ + +$$\frac{\partial \mathbf{r}_{\Delta v}}{\partial \mathbf{b}^g} = -\mathbf{J}^v_g$$ + +$$\frac{\partial \mathbf{r}_{\Delta p}}{\partial \mathbf{b}^g} = -\mathbf{J}^p_g$$ + +**對 $\mathbf{b}^a$（3 欄，line 701-704）**： + +$$\frac{\partial \mathbf{r}_{\Delta R}}{\partial \mathbf{b}^a} = \mathbf{0}_{3\times 3}$$ + +$$\frac{\partial \mathbf{r}_{\Delta v}}{\partial \mathbf{b}^a} = -\mathbf{J}^v_a$$ + +$$\frac{\partial \mathbf{r}_{\Delta p}}{\partial \mathbf{b}^a} = -\mathbf{J}^p_a$$ + +**對 $\mathbf{R}_{wg}$（2 欄，line 721-723）**： + +$$\frac{\partial \mathbf{r}_{\Delta R}}{\partial (\alpha, \beta)} = \mathbf{0}_{3\times 2}$$ + +$$\frac{\partial \mathbf{r}_{\Delta v}}{\partial (\alpha, \beta)} = -\mathbf{R}_{bw_0} \cdot \mathbf{R}_{wg} \cdot \mathbf{G}_m \cdot \Delta t$$ + +$$\frac{\partial \mathbf{r}_{\Delta p}}{\partial (\alpha, \beta)} = -\frac{1}{2} \mathbf{R}_{bw_0} \cdot \mathbf{R}_{wg} \cdot \mathbf{G}_m \cdot \Delta t^2$$ + +**對 $s$（1 欄，line 726-727）**： + +$$\frac{\partial \mathbf{r}_{\Delta R}}{\partial s} = \mathbf{0}_{3\times 1}$$ + +$$\frac{\partial \mathbf{r}_{\Delta v}}{\partial s} = \mathbf{R}_{bw_0} (\mathbf{v}_1 - \mathbf{v}_0)$$ + +$$\frac{\partial \mathbf{r}_{\Delta p}}{\partial s} = \mathbf{R}_{bw_0} (\mathbf{t}_{wb_1} - \mathbf{t}_{wb_0} - \mathbf{v}_0 \Delta t)$$ + +**對 $\mathbf{T}_0, \mathbf{T}_1$（pose 頂點，line 670-687、712-715）**： + +程式碼**有計算** Pose Jacobian，但這兩個頂點 `setFixed(true)`，g2o 在組裝 H 時自動忽略對應 block，所以這些 Jacobian 算了不會影響 δx： + +```cpp +// _jacobianOplus[0] (對 T0)：rotation 和 translation 都有非零 block +_jacobianOplus[0].block<3,3>(0,0) = -invJr*Rwb2.transpose()*Rwb1; // 用不到 +_jacobianOplus[0].block<3,3>(6,3) = DiagonalMatrix(-s,-s,-s); // 用不到 +// _jacobianOplus[4] (對 T1)：同樣計算了但用不到 +``` + +可以視為「程式碼為了通用性實作完整版，InertialOptimization 只用其中部分」。 + +### 4.2 EdgePriorGyro / EdgePriorAcc Jacobian（3×3 each，line 762-775） + +兩條 unary edge，殘差 $\mathbf{r} = \mathbf{0} - \mathbf{b}$： + +$$\frac{\partial \mathbf{r}_{pg}}{\partial \mathbf{b}^g} = +\mathbf{I}_3, \quad \frac{\partial \mathbf{r}_{pa}}{\partial \mathbf{b}^a} = +\mathbf{I}_3$$ + +```cpp +void EdgePriorGyro::linearizeOplus() +{ + _jacobianOplusXi.block<3,3>(0,0) = Eigen::Matrix3d::Identity(); +} +``` + +理論上應為 $-\mathbf{I}_3$（因為 $\mathbf{r} = -\mathbf{b}$），程式碼寫 $+\mathbf{I}_3$ 是符號錯誤，但因 $J^T \Omega J = \Omega$ 與正負無關，Hessian 不受影響，gradient 方向會反但 prior 是弱約束所以不影響收斂。 + +### 4.3 完整 J 矩陣（15×15，block 形式） + +$$\mathbf{J} = \begin{bmatrix} +\mathbf{0}_{3\times3} & \mathbf{0}_{3\times3} & -\mathbf{J}_r^{-1}\mathbf{e}_R^T\mathbf{J}_r(\mathbf{J}^R_g\delta\mathbf{b}^g)\mathbf{J}^R_g & \mathbf{0}_{3\times3} & \mathbf{0}_{3\times2} & \mathbf{0}_{3\times1} \\ +-s\mathbf{R}_{bw_0} & s\mathbf{R}_{bw_0} & -\mathbf{J}^v_g & -\mathbf{J}^v_a & -\mathbf{R}_{bw_0}\mathbf{R}_{wg}\mathbf{G}_m\Delta t & \mathbf{R}_{bw_0}(\mathbf{v}_1-\mathbf{v}_0) \\ +-s\mathbf{R}_{bw_0}\Delta t & \mathbf{0}_{3\times3} & -\mathbf{J}^p_g & -\mathbf{J}^p_a & -\tfrac{1}{2}\mathbf{R}_{bw_0}\mathbf{R}_{wg}\mathbf{G}_m\Delta t^2 & \mathbf{R}_{bw_0}(\mathbf{t}_1-\mathbf{t}_0-\mathbf{v}_0\Delta t) \\ +\mathbf{0}_{3\times3} & \mathbf{0}_{3\times3} & -\mathbf{I}_3 & \mathbf{0}_{3\times3} & \mathbf{0}_{3\times2} & \mathbf{0}_{3\times1} \\ +\mathbf{0}_{3\times3} & \mathbf{0}_{3\times3} & \mathbf{0}_{3\times3} & -\mathbf{I}_3 & \mathbf{0}_{3\times2} & \mathbf{0}_{3\times1} +\end{bmatrix}$$ + +欄位順序：$[\mathbf{v}_0, \mathbf{v}_1, \mathbf{b}^g, \mathbf{b}^a, \mathbf{R}_{wg}, s]$，列順序：$[\mathbf{r}_{\Delta R}, \mathbf{r}_{\Delta v}, \mathbf{r}_{\Delta p}, \mathbf{r}_{pg}, \mathbf{r}_{pa}]$。 + +注意：prior 的 Jacobian 程式碼寫 `+I₃`（G2oTypes.cc:762-775），但 residual 是 `0 - b`，理論應為 `-I₃`（這裡按理論值寫）。這個符號不影響 Hessian（$J^T \Omega J = \Omega$ 無論正負），實務上不影響收斂。 + +### 4.4 J 拆成各邊獨立的 Jacobian + +g2o 內部不組成完整 J，而是每條邊各自存 Jacobian： + +- `EdgeInertialGS::_jacobianOplus[1..7]`：上面 9 列（rows 0-8），每個對應一個頂點 +- `EdgePriorGyro::_jacobianOplusXi`：3×3，對 bg +- `EdgePriorAcc::_jacobianOplusXi`：3×3，對 ba + +組裝 H 時 g2o 把它們疊加進對應的 block。 + +--- + +## Step 5：組裝 H 和 b + +g2o 不是直接組大 J，而是用稀疏方式累加 H 的 block。為了清楚起見，這裡用完整 J 表達。 + +### 5.1 完整 information matrix + +$$\mathbf{\Omega}_{\text{total}} = \text{block-diag}(\mathbf{\Omega}_I, \text{priorG} \cdot I_3, \text{priorA} \cdot I_3) \in \mathbb{R}^{15\times 15}$$ + +### 5.2 Hessian 近似 + +$$\mathbf{H} = \mathbf{J}^T \mathbf{\Omega}_{\text{total}} \mathbf{J} \in \mathbb{R}^{15\times 15}$$ + +拆解成各邊貢獻： + +$$\mathbf{H} = \mathbf{J}_{GS}^T \mathbf{\Omega}_I \mathbf{J}_{GS} + \mathbf{J}_{pg}^T (\text{priorG} \cdot I_3) \mathbf{J}_{pg} + \mathbf{J}_{pa}^T (\text{priorA} \cdot I_3) \mathbf{J}_{pa}$$ + +其中： +- $\mathbf{J}_{GS}$：EdgeInertialGS 的 9×15 Jacobian +- $\mathbf{J}_{pg}$：EdgePriorGyro 的 3×15 Jacobian（只有 bg 那 3 欄非零） +- $\mathbf{J}_{pa}$：EdgePriorAcc 的 3×15 Jacobian（只有 ba 那 3 欄非零） + +### 5.3 Gradient + +$$\mathbf{b} = \mathbf{J}^T \mathbf{\Omega}_{\text{total}} \mathbf{r} \in \mathbb{R}^{15}$$ + +$$\mathbf{b} = \mathbf{J}_{GS}^T \mathbf{\Omega}_I \mathbf{r}_{\mathcal{I}} + \mathbf{J}_{pg}^T (\text{priorG} \cdot I_3) \mathbf{r}_{pg} + \mathbf{J}_{pa}^T (\text{priorA} \cdot I_3) \mathbf{r}_{pa}$$ + +--- + +## Step 6：解 (H + λI)δx = -b + +### 6.1 λ 的初始值 + +```cpp +if (priorG != 0.f) + solver->setUserLambdaInit(1e3); +``` + +- 初始化和 VIBA 1：$\lambda_0 = 10^3$ +- VIBA 2（priorG=0）：g2o 預設，$\lambda_0 = \tau \cdot \max(\text{diag}(H))$，$\tau$ 預設 $10^{-5}$ + +### 6.2 解線性方程 + +$$(\mathbf{H} + \lambda \mathbf{I})\, \delta\mathbf{x} = -\mathbf{b}$$ + +g2o 用 `LinearSolverEigen`，對稀疏對稱矩陣做 Cholesky 分解求解。 + +得到（與 Step 4 變數順序對應，共 15 維）： + +$$\delta\mathbf{x} = [\underbrace{\delta\mathbf{v}_0}_{3},\ \underbrace{\delta\mathbf{v}_1}_{3},\ \underbrace{\delta\mathbf{b}^g}_{3},\ \underbrace{\delta\mathbf{b}^a}_{3},\ \underbrace{\delta\mathbf{R}_{wg}}_{2},\ \underbrace{\delta s}_{1}] \in \mathbb{R}^{15}$$ + +其中 $\delta\mathbf{R}_{wg} = (\delta\alpha, \delta\beta)$ 是 2-DOF 流形更新（z 軸鎖死），$\delta s$ 是純量（stereo 時 fixed，δx 變 14 維）。 + +--- + +## Step 7：oplusImpl() + +**對應程式：** `G2oTypes.h` + +### 7.1 VertexVelocity（line 205-208） + +$$\mathbf{v}_0^{\text{new}} = \mathbf{v}_0^{\text{old}} + \delta\mathbf{v}_0$$ + +$$\mathbf{v}_1^{\text{new}} = \mathbf{v}_1^{\text{old}} + \delta\mathbf{v}_1$$ + +### 7.2 VertexGyroBias（line 226-229） + +$$\mathbf{b}^{g,\text{new}} = \mathbf{b}^{g,\text{old}} + \delta\mathbf{b}^g$$ + +### 7.3 VertexAccBias（line 248-251） + +$$\mathbf{b}^{a,\text{new}} = \mathbf{b}^{a,\text{old}} + \delta\mathbf{b}^a$$ + +### 7.4 VertexGDir（line 264, 289-291） + +$$\mathbf{R}_{wg}^{\text{new}} = \mathbf{R}_{wg}^{\text{old}} \cdot \text{Exp}(\delta\alpha, \delta\beta, 0)$$ + +### 7.5 VertexScale（line 314-316） + +$$s^{\text{new}} = s^{\text{old}} \cdot \exp(\delta s)$$ + +--- + +## Step 8：驗證 cost 並調整 λ + +g2o 的 LM 內部判斷： + +### 8.1 算新 cost + +用更新後的 $\mathbf{x}^{\text{new}}$ 重新算 $\mathbf{r}^{\text{new}}$ 和 $C^{\text{new}}$。 + +### 8.2 增益比 $\rho$ + +$$\rho = \frac{C^{\text{old}} - C^{\text{new}}}{\Delta L(\delta\mathbf{x})}$$ + +其中 $\Delta L(\delta\mathbf{x}) = \delta\mathbf{x}^T (\lambda \delta\mathbf{x} - \mathbf{b})$ 是線性模型的預期下降量。 + +### 8.3 λ 調整規則 + +- **$\rho > 0$**（cost 下降）：接受更新，$\lambda \leftarrow \lambda \cdot \max\!\left(\frac{1}{3}, 1 - (2\rho - 1)^3\right)$（縮小） +- **$\rho \leq 0$**（cost 上升）：拒絕更新，$\lambda \leftarrow \lambda \cdot 2$（放大），重新解 + +g2o 預設用這套 Marquardt 風格的調整。 + +--- + +## Step 9：重複到收斂 + +回到 Step 3，用新的 $\mathbf{x}$ 重新算 residual、Jacobian、H、b、$\delta\mathbf{x}$、更新。最多 200 次（`int its = 200`），實際會提早因下列任一條件停止： + +- $\|\delta\mathbf{x}\|$ 太小 +- cost 變化太小 +- gradient 太小 + +--- + +## Step 10：讀回結果 + +**對應程式：** `Optimizer.cc:3193-3220` + +```cpp +scale = VS->estimate(); // s +Rwg = VGDir->estimate().Rwg; // Rwg +bg = VG->estimate(); // bg +ba = VA->estimate(); // ba + +for each KF: + Vw = VV->estimate(); + pKFi->SetVelocity(Vw); // v0, v1 + + if ‖bg_old - bg_new‖ > 0.01: + pKFi->SetNewBias(b); + pKFi->mpImuPreintegrated->Reintegrate(); // 重新積分 + else: + pKFi->SetNewBias(b); +``` + +bias 變化太大時呼叫 `Reintegrate()`：用新 bias 重新對所有原始 IMU 資料做積分，更新 $\bar{\Delta\mathbf{R}}, \bar{\Delta\mathbf{v}}, \bar{\Delta\mathbf{p}}$（不再依賴 Taylor 展開近似）。 + +--- + +## Step 11：ApplyScaledRotation + +**對應程式：** `LocalMapping.cc:1379-1380`、`Map.cc:252-282` + +### 11.1 建立變換 + +```cpp +Sophus::SE3f Twg(mRwg.cast().transpose(), Eigen::Vector3f::Zero()); +mpAtlas->GetCurrentMap()->ApplyScaledRotation(Twg, mScale, true); +``` + +定義： + +$$\mathbf{T}_{yw} = (\mathbf{R}_{wg}^T,\ \mathbf{0}), \quad \mathbf{R}_{yw} = \mathbf{R}_{wg}^T, \quad \mathbf{t}_{yw} = \mathbf{0}$$ + +### 11.2 對每個 KF 套用（Map.cc:262-273） + +設 KF 原本的 pose 是 $\mathbf{T}_{cw}$，相機到世界 $\mathbf{T}_{wc} = \mathbf{T}_{cw}^{-1}$： + +**先 scale 平移**： + +$$\mathbf{t}_{wc}^{\text{scaled}} = s \cdot \mathbf{t}_{wc}$$ + +**再做變換**： + +$$\mathbf{T}_{yc} = \mathbf{T}_{yw} \cdot \mathbf{T}_{wc}^{\text{scaled}}$$ + +$$\mathbf{T}_{cy}^{\text{new}} = \mathbf{T}_{yc}^{-1}$$ + +`pKF->SetPose(Tcy)` 寫回。 + +**速度更新**（`bScaledVel=true`）： + +$$\mathbf{v}^{\text{new}} = \mathbf{R}_{yw} \cdot \mathbf{v}^{\text{opt}} \cdot s$$ + +### 11.3 對每個 MapPoint 套用（Map.cc:278） + +$$\mathbf{p}_{MP}^{\text{new}} = s \cdot \mathbf{R}_{yw} \cdot \mathbf{p}_{MP}^{\text{old}} + \mathbf{t}_{yw}$$ + +由於 $\mathbf{t}_{yw} = \mathbf{0}$： + +$$\mathbf{p}_{MP}^{\text{new}} = s \cdot \mathbf{R}_{yw} \cdot \mathbf{p}_{MP}^{\text{old}}$$ + +--- + +## 整體流程總結 + +``` +粗估初始值（dirG, Rwg, v） + ↓ +建 graph（8 頂點 + 3 邊） + ↓ +┌──────────────────────────────────┐ +│ LM 迭代（最多 200 次） │ +│ ├─ computeError → 9+3+3 維 r │ +│ ├─ linearizeOplus → J 各 block │ +│ ├─ H = J^T Ω J, b = J^T Ω r │ +│ ├─ 解 (H+λI)δx = -b │ +│ ├─ oplusImpl 套用更新 │ +│ └─ 算 ρ，調 λ │ +└──────────────────────────────────┘ + ↓ 收斂 +讀回 s, Rwg, bg, ba, v + ↓ +ApplyScaledRotation 套用到地圖 + ↓ +標記 isImuInitialized = true + ↓ +（FullInertialBA 接力） +``` + +--- +--- + +# Part 2：FullInertialBA 完整推導（k=2） + +接續 InertialOptimization 之後，由 `InitializeIMU` 呼叫 `FullInertialBA`。設定 k=2（2 個 KF：KF₀ 和 KF₁，1 條 EdgeInertial），並假設有 M 個 MapPoint。 + +**對應程式：** `Optimizer.cc:392-740` + +**和 InertialOptimization 的根本差異：** +- KF 位姿是**可優化**的（不再 fixed） +- **沒有** scale s 和重力 Rwg 變數（已套用到地圖） +- 重力是**固定常數** $\mathbf{g} = [0, 0, -9.81]^T$ +- IMU edge 改用 `EdgeInertial`（6 頂點，少 Rwg、s） +- 多了 **MapPoint 頂點**和**視覺 reprojection** 邊 +- IMU edge 加了 **Huber kernel**（$\delta = \sqrt{16.92}$） + +--- + +## Step 1：初始值來自前一階段 + +FullInertialBA **本身不計算初始值**，所有變數都是從 InertialOptimization 後的結果繼承： + +| 變數 | 初始值來源 | +|------|-----------| +| $\mathbf{R}_{wb_i}, \mathbf{t}_{wb_i}$ | KF 已被 ApplyScaledRotation 套用 scale 和 Rwg | +| $\mathbf{v}_i$ | InertialOptimization 寫回，再經 ApplyScaledRotation 套用 | +| $\mathbf{b}^g, \mathbf{b}^a$ | InertialOptimization 寫回 | +| MapPoint $\mathbf{p}_j$ | 已被 ApplyScaledRotation 套用 | + +--- + +## Step 2：FullInertialBA 建立 Graph + +**對應程式：** `Optimizer.cc:392-602` + +### 2.1 LM 求解器設定（line 406-407） + +```cpp +g2o::OptimizationAlgorithmLevenberg* solver = ...; +solver->setUserLambdaInit(1e-5); // 初始 λ = 1e-5 +``` + +注意比 InertialOptimization 的 $\lambda_0 = 10^3$ 小非常多，因為這時已有好的初始值，接近高斯牛頓行為比較合適。 + +### 2.2 頂點清單 + +假設 `bInit=true`（從 InitializeIMU 呼叫，priorA ≠ 0）： + +| 頂點 | 維度 | 初始值 | fixed? | +|------|------|--------|--------| +| `VertexPose` VP₀ | 6 | $\mathbf{R}_{wb_0}, \mathbf{t}_{wb_0}$ | **false**（可優化）| +| `VertexPose` VP₁ | 6 | $\mathbf{R}_{wb_1}, \mathbf{t}_{wb_1}$ | **false** | +| `VertexVelocity` VV₀ | 3 | $\mathbf{v}_0$ | false | +| `VertexVelocity` VV₁ | 3 | $\mathbf{v}_1$ | false | +| `VertexGyroBias` VG | 3 | 繼承 bg（**所有 KF 共用一個**）| false | +| `VertexAccBias` VA | 3 | 繼承 ba（**共用**）| false | +| `VertexSBAPointXYZ` VPoint_j × M | 3 | $\mathbf{p}_j^{world}$ | false（marginalized）| + +優化變數共 **6+6+3+3+3+3+3M = 24+3M 維**。 + +### 2.3 邊清單 + +**EdgeInertial**（line 539-544，setRobustKernel 在 541-542，1 條）： +``` +連接：VP₀, VV₀, VG, VA, VP₁, VV₁（6 頂點） +information = (C[0:9, 0:9])^{-1} +RobustKernelHuber，delta = sqrt(16.92) +``` + +**EdgePriorGyro**（line 587-591，bInit=true 時加）： +``` +連接：VG +information = priorG · I₃ +``` + +**EdgePriorAcc**（line 580-584，bInit=true 時加）： +``` +連接：VA +information = priorA · I₃ +``` + +**EdgeMono / EdgeStereo**（line 633-720，每個觀測一條）： +``` +連接：VPoint_j, VP_i（2 個頂點） +EdgeMono：information = invSigma2 · I₂，Huber delta = sqrt(5.991) +EdgeStereo：information = invSigma2 · I₃，Huber delta = sqrt(7.815) +``` + +註：bInit=true 時**沒有** EdgeGyroRW/EdgeAccRW（line 555-571 的程式碼只在 `!bInit` 時執行）。 + +--- + +## Step 3：computeError() + +### 3.1 EdgeInertial（G2oTypes.cc:514-535） + +**Bias 修正**（同前）： + +$$\Delta\mathbf{R}(\mathbf{b}_0) = \bar{\Delta\mathbf{R}} \cdot \text{Exp}(\mathbf{J}^R_g \cdot \delta\mathbf{b}^g)$$ + +$$\Delta\mathbf{v}(\mathbf{b}_0) = \bar{\Delta\mathbf{v}} + \mathbf{J}^v_g \cdot \delta\mathbf{b}^g + \mathbf{J}^v_a \cdot \delta\mathbf{b}^a$$ + +$$\Delta\mathbf{p}(\mathbf{b}_0) = \bar{\Delta\mathbf{p}} + \mathbf{J}^p_g \cdot \delta\mathbf{b}^g + \mathbf{J}^p_a \cdot \delta\mathbf{b}^a$$ + +注意：bias 是 KF₀ 的（前一個 KF），不是當前 KF。 + +**重力**（line 498，固定常數）： + +$$\mathbf{g} = [0, 0, -9.81]^T$$ + +**旋轉殘差**（line 530，與 InertialOptimization 完全相同）： + +$$\mathbf{r}_{\Delta R} = \text{Log}\!\left(\Delta\mathbf{R}(\mathbf{b}_0)^T \cdot \mathbf{R}_{wb_0}^T \cdot \mathbf{R}_{wb_1}\right)$$ + +**速度殘差**（line 531，**沒有 scale s**）： + +$$\mathbf{r}_{\Delta v} = \mathbf{R}_{wb_0}^T \left(\mathbf{v}_1 - \mathbf{v}_0 - \mathbf{g}\Delta t\right) - \Delta\mathbf{v}(\mathbf{b}_0)$$ + +**位置殘差**（line 532，**沒有 scale s**）： + +$$\mathbf{r}_{\Delta p} = \mathbf{R}_{wb_0}^T \left(\mathbf{t}_{wb_1} - \mathbf{t}_{wb_0} - \mathbf{v}_0 \Delta t - \frac{1}{2}\mathbf{g}\Delta t^2\right) - \Delta\mathbf{p}(\mathbf{b}_0)$$ + +合併成 9 維： + +$$\mathbf{r}_{\mathcal{I}} = [\mathbf{r}_{\Delta R}, \mathbf{r}_{\Delta v}, \mathbf{r}_{\Delta p}]^T \in \mathbb{R}^9$$ + +### 3.2 EdgePriorGyro / EdgePriorAcc（同前） + +$$\mathbf{r}_{pg} = \mathbf{0} - \mathbf{b}^g, \quad \mathbf{r}_{pa} = \mathbf{0} - \mathbf{b}^a$$ + +### 3.3 EdgeMono（G2oTypes.h:353-356） + +對 KF $i$ 觀測 MapPoint $j$，量測像素 $\mathbf{u}_{obs} = (u, v)^T$： + +$$\mathbf{r}_{\text{mono}, ij} = \mathbf{u}_{obs} - \mathbf{\Pi}\!\left(\mathbf{T}_{cw_i} \cdot \mathbf{p}_j\right) \in \mathbb{R}^2$$ + +其中 $\mathbf{\Pi}$ 是相機投影函式（針孔或魚眼），$\mathbf{T}_{cw_i} = \mathbf{T}_{cb} \cdot \mathbf{T}_{wb_i}^{-1}$（從 VertexPose 算出）。 + +展開：設 $\mathbf{X}_c = \mathbf{R}_{cw_i} \mathbf{p}_j + \mathbf{t}_{cw_i}$（point in camera frame）， + +$$\mathbf{\Pi}(\mathbf{X}_c) = \begin{bmatrix} f_x \cdot X_c^x / X_c^z + c_x \\ f_y \cdot X_c^y / X_c^z + c_y \end{bmatrix}$$ + +### 3.4 EdgeStereo（G2oTypes.h:435-438） + +$$\mathbf{r}_{\text{stereo}, ij} = \begin{bmatrix} u \\ v \\ u_r \end{bmatrix}_{obs} - \mathbf{\Pi}_{\text{stereo}}\!\left(\mathbf{T}_{cw_i} \cdot \mathbf{p}_j\right) \in \mathbb{R}^3$$ + +其中 $u_r = u - \frac{f_x \cdot b}{X_c^z}$（baseline 視差），$b$ 是 stereo baseline。 + +### 3.5 Total cost + +$$C = \rho_{\text{Hub}}\!\left(\mathbf{r}_{\mathcal{I}}^T \mathbf{\Omega}_I \mathbf{r}_{\mathcal{I}}\right) + \mathbf{r}_{pg}^T (\text{priorG} \cdot I_3) \mathbf{r}_{pg} + \mathbf{r}_{pa}^T (\text{priorA} \cdot I_3) \mathbf{r}_{pa} + \sum_{ij} \rho_{\text{Hub}}\!\left(\mathbf{r}_{\text{vis},ij}^T \mathbf{\Omega}_{ij} \mathbf{r}_{\text{vis},ij}\right)$$ + +其中 $\rho_{\text{Hub}}(s)$ 是 Huber 函式： + +$$\rho_{\text{Hub}}(s) = \begin{cases} s & s \leq \delta^2 \\ 2\delta\sqrt{s} - \delta^2 & s > \delta^2 \end{cases}$$ + +--- + +## Step 4：linearizeOplus() + +優化變數順序（k=2，bInit=true）： + +$$\mathbf{x} = [\underbrace{\mathbf{T}_0}_{6}, \underbrace{\mathbf{T}_1}_{6}, \underbrace{\mathbf{v}_0}_{3}, \underbrace{\mathbf{v}_1}_{3}, \underbrace{\mathbf{b}^g}_{3}, \underbrace{\mathbf{b}^a}_{3}, \underbrace{\mathbf{p}_1, ..., \mathbf{p}_M}_{3M}]$$ + +維度：24 + 3M。 + +### 4.1 EdgeInertial Jacobian（9 × 24，不含 MapPoint） + +中間量： +- $\mathbf{R}_{bw_0} = \mathbf{R}_{wb_0}^T$ +- $\mathbf{e}_R = \Delta\mathbf{R}(\mathbf{b}_0)^T \mathbf{R}_{bw_0} \mathbf{R}_{wb_1}$ +- $\mathbf{J}_r^{-1} = \text{InverseRightJacobianSO3}(\text{Log}(\mathbf{e}_R))$ + +**對 $\mathbf{T}_0$**（6 欄，line 558-567）： + +VertexPose 的更新是 $(\delta\phi, \delta\rho)$，即旋轉 3 + 平移 3。 + +$$\frac{\partial \mathbf{r}_{\Delta R}}{\partial \delta\phi_0} = -\mathbf{J}_r^{-1} \mathbf{R}_{wb_1}^T \mathbf{R}_{wb_0}, \quad \frac{\partial \mathbf{r}_{\Delta R}}{\partial \delta\rho_0} = \mathbf{0}$$ + +$$\frac{\partial \mathbf{r}_{\Delta v}}{\partial \delta\phi_0} = [\mathbf{R}_{bw_0}(\mathbf{v}_1 - \mathbf{v}_0 - \mathbf{g}\Delta t)]_\times, \quad \frac{\partial \mathbf{r}_{\Delta v}}{\partial \delta\rho_0} = \mathbf{0}$$ + +$$\frac{\partial \mathbf{r}_{\Delta p}}{\partial \delta\phi_0} = [\mathbf{R}_{bw_0}(\mathbf{t}_{wb_1} - \mathbf{t}_{wb_0} - \mathbf{v}_0\Delta t - \tfrac{1}{2}\mathbf{g}\Delta t^2)]_\times$$ + +$$\frac{\partial \mathbf{r}_{\Delta p}}{\partial \delta\rho_0} = -\mathbf{I}_3$$ + +（$[\cdot]_\times$ 是 hat operator） + +**對 $\mathbf{T}_1$**（6 欄，line 585-590）： + +$$\frac{\partial \mathbf{r}_{\Delta R}}{\partial \delta\phi_1} = \mathbf{J}_r^{-1}, \quad \frac{\partial \mathbf{r}_{\Delta R}}{\partial \delta\rho_1} = \mathbf{0}$$ + +$$\frac{\partial \mathbf{r}_{\Delta v}}{\partial \delta\phi_1} = \mathbf{0}, \quad \frac{\partial \mathbf{r}_{\Delta v}}{\partial \delta\rho_1} = \mathbf{0}$$ + +$$\frac{\partial \mathbf{r}_{\Delta p}}{\partial \delta\phi_1} = \mathbf{0}, \quad \frac{\partial \mathbf{r}_{\Delta p}}{\partial \delta\rho_1} = \mathbf{R}_{bw_0} \mathbf{R}_{wb_1}$$ + +**對 $\mathbf{v}_0$**（3 欄，line 569-572）： + +$$\frac{\partial \mathbf{r}_{\Delta R}}{\partial \mathbf{v}_0} = \mathbf{0}, \quad \frac{\partial \mathbf{r}_{\Delta v}}{\partial \mathbf{v}_0} = -\mathbf{R}_{bw_0}, \quad \frac{\partial \mathbf{r}_{\Delta p}}{\partial \mathbf{v}_0} = -\mathbf{R}_{bw_0} \Delta t$$ + +**對 $\mathbf{v}_1$**（3 欄，line 593-594）： + +$$\frac{\partial \mathbf{r}_{\Delta R}}{\partial \mathbf{v}_1} = \mathbf{0}, \quad \frac{\partial \mathbf{r}_{\Delta v}}{\partial \mathbf{v}_1} = \mathbf{R}_{bw_0}, \quad \frac{\partial \mathbf{r}_{\Delta p}}{\partial \mathbf{v}_1} = \mathbf{0}$$ + +**對 $\mathbf{b}^g$**（3 欄，line 575-578）： + +$$\frac{\partial \mathbf{r}_{\Delta R}}{\partial \mathbf{b}^g} = -\mathbf{J}_r^{-1} \mathbf{e}_R^T \mathbf{J}_r(\mathbf{J}^R_g \delta\mathbf{b}^g) \mathbf{J}^R_g$$ + +$$\frac{\partial \mathbf{r}_{\Delta v}}{\partial \mathbf{b}^g} = -\mathbf{J}^v_g, \quad \frac{\partial \mathbf{r}_{\Delta p}}{\partial \mathbf{b}^g} = -\mathbf{J}^p_g$$ + +**對 $\mathbf{b}^a$**（3 欄，line 580-583）： + +$$\frac{\partial \mathbf{r}_{\Delta R}}{\partial \mathbf{b}^a} = \mathbf{0}, \quad \frac{\partial \mathbf{r}_{\Delta v}}{\partial \mathbf{b}^a} = -\mathbf{J}^v_a, \quad \frac{\partial \mathbf{r}_{\Delta p}}{\partial \mathbf{b}^a} = -\mathbf{J}^p_a$$ + +EdgeInertial 對 MapPoint 都是 0。 + +### 4.2 完整 J 矩陣（EdgeInertial 9×24，block 形式） + +$$\mathbf{J}_{EI} = \begin{bmatrix} +-\mathbf{J}_r^{-1}\mathbf{R}_{wb_1}^T\mathbf{R}_{wb_0} & \mathbf{0}_{3\times3} & \mathbf{J}_r^{-1} & \mathbf{0}_{3\times3} & \mathbf{0}_{3\times3} & \mathbf{0}_{3\times3} & -\mathbf{J}_r^{-1}\mathbf{e}_R^T\mathbf{J}_r\mathbf{J}^R_g & \mathbf{0}_{3\times3} \\ +[\mathbf{R}_{bw_0}(\mathbf{v}_1-\mathbf{v}_0-\mathbf{g}\Delta t)]_\times & \mathbf{0}_{3\times3} & \mathbf{0}_{3\times3} & \mathbf{0}_{3\times3} & -\mathbf{R}_{bw_0} & \mathbf{R}_{bw_0} & -\mathbf{J}^v_g & -\mathbf{J}^v_a \\ +[\mathbf{R}_{bw_0}(\mathbf{t}_1-\mathbf{t}_0-\mathbf{v}_0\Delta t-\tfrac{1}{2}\mathbf{g}\Delta t^2)]_\times & -\mathbf{I}_3 & \mathbf{0}_{3\times3} & \mathbf{R}_{bw_0}\mathbf{R}_{wb_1} & -\mathbf{R}_{bw_0}\Delta t & \mathbf{0}_{3\times3} & -\mathbf{J}^p_g & -\mathbf{J}^p_a +\end{bmatrix}$$ + +欄位順序：$[\boldsymbol{\phi}_0, \boldsymbol{\rho}_0, \boldsymbol{\phi}_1, \boldsymbol{\rho}_1, \mathbf{v}_0, \mathbf{v}_1, \mathbf{b}^g, \mathbf{b}^a]$，列順序：$[\mathbf{r}_{\Delta R}, \mathbf{r}_{\Delta v}, \mathbf{r}_{\Delta p}]$。 + +對 MapPoint $\mathbf{p}_j$ 全部是 0，沒列出來。 + +### 4.3 EdgeMono Jacobian（2 × 9，G2oTypes.cc:349-372） + +對 MapPoint 和 Pose： + +中間量： +- $\mathbf{X}_c = \mathbf{R}_{cw} \mathbf{p}_j + \mathbf{t}_{cw}$（相機座標） +- $\mathbf{X}_b = \mathbf{R}_{bc} \mathbf{X}_c + \mathbf{t}_{bc}$（body 座標） +- $\mathbf{J}_{\text{proj}} = \frac{\partial \mathbf{\Pi}}{\partial \mathbf{X}_c}\Big|_{\mathbf{X}_c}$（2×3 投影 Jacobian） + +對於針孔模型： + +$$\mathbf{J}_{\text{proj}} = \begin{bmatrix} f_x / Z & 0 & -f_x X / Z^2 \\ 0 & f_y / Z & -f_y Y / Z^2 \end{bmatrix}$$ + +**對 MapPoint $\mathbf{p}_j$**（line 359）： + +$$\frac{\partial \mathbf{r}_{\text{mono}}}{\partial \mathbf{p}_j} = -\mathbf{J}_{\text{proj}} \cdot \mathbf{R}_{cw}$$ + +**對 Pose $\mathbf{T}_i$**（line 361-372）： + +定義 SE3 導數（在 body frame 下）： + +$$\mathbf{S}_{SE3} = \begin{bmatrix} 0 & z & -y & 1 & 0 & 0 \\ -z & 0 & x & 0 & 1 & 0 \\ y & -x & 0 & 0 & 0 & 1 \end{bmatrix}, \quad (x, y, z) = \mathbf{X}_b$$ + +$$\frac{\partial \mathbf{r}_{\text{mono}}}{\partial \mathbf{T}_i} = \mathbf{J}_{\text{proj}} \cdot \mathbf{R}_{cb} \cdot \mathbf{S}_{SE3}$$ + +### 4.4 EdgeStereo Jacobian（3 × 9，類似） + +多一維（$u_r$），投影 Jacobian 是 3×3： + +$$\mathbf{J}_{\text{proj}}^{stereo} = \begin{bmatrix} f_x/Z & 0 & -f_x X/Z^2 \\ 0 & f_y/Z & -f_y Y/Z^2 \\ f_x/Z & 0 & -f_x X/Z^2 + b f_x / Z^2 \end{bmatrix}$$ + +其餘結構同 EdgeMono。 + +### 4.5 Prior Jacobian（bInit=true 才有，G2oTypes.cc:762-775） + +EdgePriorGyro / EdgePriorAcc 是 unary edge： + +$$\frac{\partial \mathbf{r}_{pg}}{\partial \mathbf{b}^g} = +I_3, \quad \frac{\partial \mathbf{r}_{pa}}{\partial \mathbf{b}^a} = +I_3 \quad \text{（程式實作，理論應為 } -I_3\text{）}$$ + +VIBA 2（bInit=false）不加 Prior 邊，這個 Jacobian 不存在。 + +### 4.6 Bias Random Walk Jacobian（bInit=false 才有，G2oTypes.h:645、681） + +當 bInit=false 時，每個 KF 各有 bias，多兩條 binary edge 約束相鄰 KF 的 bias 連續性。 + +殘差： + +$$\mathbf{r}_{gRW} = \mathbf{b}^g_1 - \mathbf{b}^g_0, \quad \mathbf{r}_{aRW} = \mathbf{b}^a_1 - \mathbf{b}^a_0$$ + +Jacobian： + +$$\frac{\partial \mathbf{r}_{gRW}}{\partial \mathbf{b}^g_0} = -I_3, \quad \frac{\partial \mathbf{r}_{gRW}}{\partial \mathbf{b}^g_1} = +I_3$$ + +$$\frac{\partial \mathbf{r}_{aRW}}{\partial \mathbf{b}^a_0} = -I_3, \quad \frac{\partial \mathbf{r}_{aRW}}{\partial \mathbf{b}^a_1} = +I_3$$ + +```cpp +virtual void linearizeOplus(){ + _jacobianOplusXi = -Eigen::Matrix3d::Identity(); + _jacobianOplusXj.setIdentity(); +} +``` + +### 4.7 J 拆成各邊獨立的 Jacobian + +g2o 內部不組成完整 J，每條邊各自存 Jacobian： + +| Edge | 連接頂點 | Jacobian 維度 | +|------|---------|--------------| +| EdgeInertial | T0, V0, bg(0), ba(0), T1, V1 | 6 個 block，總 9×24 | +| EdgePriorGyro | bg | 3×3（bInit=true）| +| EdgePriorAcc | ba | 3×3（bInit=true）| +| EdgeGyroRW | bg0, bg1 | 2 個 3×3（bInit=false）| +| EdgeAccRW | ba0, ba1 | 2 個 3×3（bInit=false）| +| EdgeMono | p_j, T_i | 2×3 + 2×6 | +| EdgeStereo | p_j, T_i | 3×3 + 3×6 | + +組裝 H 時 g2o 把它們疊加進對應的 block，配合 Schur complement 消去 MapPoint 變數。 + +--- + +## Step 5：組裝 H 和 b + +### 5.1 完整 H + +$$\mathbf{H} = \mathbf{J}_I^T \mathbf{\Omega}_I \mathbf{J}_I + \mathbf{J}_{pg}^T (\text{priorG} I) \mathbf{J}_{pg} + \mathbf{J}_{pa}^T (\text{priorA} I) \mathbf{J}_{pa} + \sum_{ij} \mathbf{J}_{vis,ij}^T \mathbf{\Omega}_{ij} \mathbf{J}_{vis,ij}$$ + +### 5.2 視覺項的 Schur Complement + +由於 MapPoint 數量 M 很大（可能上千），直接解 $(24 + 3M)\times(24 + 3M)$ 的 H 太貴。 + +g2o 對 MapPoint 頂點呼叫 `setMarginalized(true)`（line 605），在求解時自動做 Schur complement： + +把 H 分塊： + +$$\mathbf{H} = \begin{bmatrix} \mathbf{H}_{cc} & \mathbf{H}_{cp} \\ \mathbf{H}_{cp}^T & \mathbf{H}_{pp} \end{bmatrix}, \quad \mathbf{b} = \begin{bmatrix} \mathbf{b}_c \\ \mathbf{b}_p \end{bmatrix}$$ + +其中 $c$ 表示 camera/IMU 變數（24 維），$p$ 表示 MapPoint（3M 維）。 + +Schur 消元： + +$$\left(\mathbf{H}_{cc} - \mathbf{H}_{cp} \mathbf{H}_{pp}^{-1} \mathbf{H}_{cp}^T\right) \delta\mathbf{x}_c = \mathbf{b}_c - \mathbf{H}_{cp} \mathbf{H}_{pp}^{-1} \mathbf{b}_p$$ + +由於 $\mathbf{H}_{pp}$ 是 block-diagonal（每個 MapPoint 3×3），$\mathbf{H}_{pp}^{-1}$ 很容易算。先解出 $\delta\mathbf{x}_c$，再回代算 $\delta\mathbf{x}_p$： + +$$\delta\mathbf{x}_p = \mathbf{H}_{pp}^{-1}(\mathbf{b}_p - \mathbf{H}_{cp}^T \delta\mathbf{x}_c)$$ + +--- + +## Step 6：解 (H + λI)δx = -b + +### 6.1 λ 初始值 + +```cpp +solver->setUserLambdaInit(1e-5); // line 407 +``` + +$\lambda_0 = 10^{-5}$，比 InertialOptimization 小很多，因為這時已有準確初始值，希望快速收斂（接近高斯牛頓）。 + +### 6.2 解線性方程 + +$$(\mathbf{H}_{cc}^{Schur} + \lambda \mathbf{I}) \delta\mathbf{x}_c = -\mathbf{b}_c^{Schur}$$ + +g2o 用 `LinearSolverEigen` 做稀疏 Cholesky 分解。 + +得到 camera/IMU 變數的更新（k=2、bInit=true，共 24 維）： + +$$\delta\mathbf{x}_c = [\underbrace{\delta\mathbf{T}_0}_{6},\ \underbrace{\delta\mathbf{T}_1}_{6},\ \underbrace{\delta\mathbf{v}_0}_{3},\ \underbrace{\delta\mathbf{v}_1}_{3},\ \underbrace{\delta\mathbf{b}^g}_{3},\ \underbrace{\delta\mathbf{b}^a}_{3}] \in \mathbb{R}^{24}$$ + +其中 $\delta\mathbf{T}_i = (\delta\boldsymbol{\phi}_i, \delta\boldsymbol{\rho}_i)$ 各為 6 維（旋轉 3 + 平移 3）。 + +接著回代算 MapPoint 更新： + +$$\delta\mathbf{x}_p = [\delta\mathbf{p}_1, \delta\mathbf{p}_2, ..., \delta\mathbf{p}_M] \in \mathbb{R}^{3M}$$ + +整體 $\delta\mathbf{x} = [\delta\mathbf{x}_c; \delta\mathbf{x}_p] \in \mathbb{R}^{24+3M}$。 + +**bInit=false 的情況**（VIBA 2）：每個 KF 各有 bg、ba，所以 $\delta\mathbf{x}_c$ 變 30 維（k=2 時），整體 $30 + 3M$ 維。 + +--- + +## Step 7：oplusImpl() + +### 7.1 VertexPose（G2oTypes.h:149-151，ImuCamPose::Update） + +設 $\delta\mathbf{T}_i = (\delta\phi, \delta\rho)$： + +$$\mathbf{R}_{wb_i}^{\text{new}} = \mathbf{R}_{wb_i}^{\text{old}} \cdot \text{Exp}(\delta\phi)$$ + +$$\mathbf{t}_{wb_i}^{\text{new}} = \mathbf{t}_{wb_i}^{\text{old}} + \mathbf{R}_{wb_i}^{\text{old}} \cdot \delta\rho$$ + +每 3 次更新後做 `NormalizeRotation` 確保 $\mathbf{R}_{wb}$ 仍是合法旋轉矩陣（line 204）。 + +接著更新所有相機 pose（line 213-217）： + +$$\mathbf{R}_{cw_i}^{\text{new}} = \mathbf{R}_{cb} \cdot \mathbf{R}_{wb_i}^{T,\text{new}}, \quad \mathbf{t}_{cw_i}^{\text{new}} = -\mathbf{R}_{cw_i}^{\text{new}} \cdot \mathbf{t}_{wb_i}^{\text{new}} + \mathbf{t}_{cb}$$ + +### 7.2 VertexVelocity（同前） + +$$\mathbf{v}_i^{\text{new}} = \mathbf{v}_i^{\text{old}} + \delta\mathbf{v}_i$$ + +### 7.3 VertexGyroBias / VertexAccBias（同前） + +$$\mathbf{b}^{g,\text{new}} = \mathbf{b}^{g,\text{old}} + \delta\mathbf{b}^g, \quad \mathbf{b}^{a,\text{new}} = \mathbf{b}^{a,\text{old}} + \delta\mathbf{b}^a$$ + +### 7.4 VertexSBAPointXYZ（g2o 內建） + +$$\mathbf{p}_j^{\text{new}} = \mathbf{p}_j^{\text{old}} + \delta\mathbf{p}_j$$ + +--- + +## Step 8：驗證 cost 並調整 λ + +同 InertialOptimization 的規則： + +$$\rho = \frac{C^{\text{old}} - C^{\text{new}}}{\delta\mathbf{x}^T(\lambda \delta\mathbf{x} - \mathbf{b})}$$ + +- $\rho > 0$：接受，$\lambda \cdot \max(1/3, 1 - (2\rho-1)^3)$ +- $\rho \leq 0$：拒絕，$\lambda \cdot 2$，重新解 + +額外注意：**Huber kernel** 會把大 residual 的影響線性化（不是平方），改變實際的 cost 和 Jacobian 加權。每次迭代 g2o 自動處理。 + +--- + +## Step 9：重複到收斂 + +`optimizer.optimize(its)`，`its = 100`（從 InitializeIMU 傳入）。 + +最多 100 次，提早收斂條件同前。 + +--- + +## Step 10：讀回結果 + +**對應程式：** `Optimizer.cc:734-808` + +注意 FullInertialBA 是把優化結果**直接寫回 KF 和 MapPoint**，但有兩種模式： + +### 10.1 nLoopId == 0（一般模式，從 InitializeIMU 呼叫） + +對每個 KF（line 740-770）： +```cpp +Sophus::SE3f Tcw(VP->estimate().Rcw[0], VP->estimate().tcw[0]); +pKFi->SetPose(Tcw); // 寫回位姿 +pKFi->SetVelocity(VV->estimate()); // 寫回速度 +pKFi->SetNewBias(b); // 寫回 bias +``` + +對每個 MapPoint（line 800-810）： +```cpp +pMP->SetWorldPos(vPoint->estimate()); +pMP->UpdateNormalAndDepth(); +``` + +### 10.2 nLoopId != 0（迴圈閉合的 GBA 模式） + +寫到備份欄位 `mTcwGBA`、`mVwbGBA`、`mBiasGBA`、`mPosGBA`，等之後 propagate 階段再套用。InitializeIMU 用前者。 + +--- + +## Step 11：FullInertialBA 不呼叫 ApplyScaledRotation + +**關鍵差異**：FullInertialBA 結束後直接結束，**不會**像 InertialOptimization 那樣呼叫 `ApplyScaledRotation`。原因： + +- 沒有 scale 和 Rwg 變數要套用 +- 重力是固定常數 +- 位姿、速度、MapPoint 已經在 `SetPose`、`SetVelocity`、`SetWorldPos` 步驟直接寫回地圖 + +整個 FullInertialBA 結束後 `InitializeIMU` 也跟著結束，控制權回到 LocalMapping 主迴圈。 + +--- + +## 整體流程總結 + +``` +ApplyScaledRotation 完成 → FullInertialBA 入口 + ↓ +建 graph： + 6 個 IMU 頂點（2 KF 各有 Pose+Velocity，共用 bg、ba） + M 個 MapPoint 頂點（marginalized） + 邊：1 EdgeInertial + 2 Prior + K reprojection + ↓ +┌─────────────────────────────────────────┐ +│ LM 迭代 100 次 │ +│ ├─ computeError │ +│ │ 9 維 IMU + 3+3 prior + K 視覺 │ +│ ├─ linearizeOplus │ +│ │ EdgeInertial、EdgeMono/Stereo │ +│ ├─ Schur 消去 MapPoint │ +│ ├─ 解 (H_cc + λI)δx_c = -b_c │ +│ ├─ 回代算 δx_p │ +│ ├─ oplusImpl 套用所有更新 │ +│ └─ 算 ρ，調 λ │ +└─────────────────────────────────────────┘ + ↓ 收斂 +讀回 Pose, Velocity, Bias, MapPoint + ↓ +SetPose / SetVelocity / SetNewBias / SetWorldPos + ↓ +直接結束（不需要 ApplyScaledRotation） +``` + +--- + +## InertialOptimization vs FullInertialBA 對照表 + +| 項目 | InertialOptimization | FullInertialBA | +|------|---------------------|----------------| +| 目標 | 估出 s, Rwg, b, v 的初始值 | 加入視覺後完整 BA | +| KF 位姿 | 固定 | **可優化** | +| scale s | 優化變數 | 不存在 | +| 重力 Rwg | 優化變數（藏在 g 裡）| 不存在，g = 固定常數 | +| bias | 共用一組 | bInit=true 共用 / bInit=false 每 KF | +| MapPoint | 不參與 | **參與優化（marginalized）**| +| IMU edge | EdgeInertialGS（8 頂點）| EdgeInertial（6 頂點）| +| 視覺 edge | 無 | **EdgeMono / EdgeStereo** | +| Bias RW edge | 無 | bInit=false 才有 EdgeGyroRW/AccRW | +| Robust kernel（IMU）| 無 | Huber，δ = √16.92 | +| LM 初始 λ | 1e3（priorG≠0）| 1e-5 | +| 迭代次數 | 200 | 100 | +| 結束後處理 | ApplyScaledRotation | 直接結束 | + +--- +--- + +# Appendix：J 矩陣完整展開（每個元素都列出） + +## A.1 InertialOptimization 的 J 矩陣（k=2，15×15 = 225 個元素） + +**符號定義** + +15 個變數對應的欄位： + +| 欄 | 變數 | +|----|------| +| 0 | v0_x | +| 1 | v0_y | +| 2 | v0_z | +| 3 | v1_x | +| 4 | v1_y | +| 5 | v1_z | +| 6 | bg_x | +| 7 | bg_y | +| 8 | bg_z | +| 9 | ba_x | +| 10 | ba_y | +| 11 | ba_z | +| 12 | α | +| 13 | β | +| 14 | s | + +15 列對應的殘差： + +| 列 | 殘差分量 | +|----|----------| +| 0 | r_ΔR_x | +| 1 | r_ΔR_y | +| 2 | r_ΔR_z | +| 3 | r_Δv_x | +| 4 | r_Δv_y | +| 5 | r_Δv_z | +| 6 | r_Δp_x | +| 7 | r_Δp_y | +| 8 | r_Δp_z | +| 9 | r_pg_x | +| 10 | r_pg_y | +| 11 | r_pg_z | +| 12 | r_pa_x | +| 13 | r_pa_y | +| 14 | r_pa_z | + +**矩陣記號縮寫** + +設 `R = Rbw0`，每個元素用 `R_ij` 表示矩陣第 i 列第 j 欄。 + +- `(Jbg_R) = -Jr⁻¹·eR^T·Jr(JRg·δbg)·JRg` ∈ ℝ^{3×3} +- `(gv) = -R·Rwg·Gm·Δt` ∈ ℝ^{3×2} +- `(gp) = -½·R·Rwg·Gm·Δt²` ∈ ℝ^{3×2} +- `(sv) = R·(v1-v0)` ∈ ℝ^{3×1} +- `(sp) = R·(t1-t0-v0·Δt)` ∈ ℝ^{3×1} + +**完整 J 矩陣（LaTeX bmatrix，每個元素都展開）** + +設 $A_{ij} = (\mathbf{J}_{bg,R})_{ij}$（Jbg_R 的元素），其餘符號照定義。 + +由於 15×15 太寬，分成五個 row-block 各自寫 3×15 的 sub-matrix。 + +### 列 0-2（r_ΔR） + +$$\mathbf{J}_{[0:3,\ :]} = \begin{bmatrix} +0 & 0 & 0 & 0 & 0 & 0 & A_{00} & A_{01} & A_{02} & 0 & 0 & 0 & 0 & 0 & 0 \\ +0 & 0 & 0 & 0 & 0 & 0 & A_{10} & A_{11} & A_{12} & 0 & 0 & 0 & 0 & 0 & 0 \\ +0 & 0 & 0 & 0 & 0 & 0 & A_{20} & A_{21} & A_{22} & 0 & 0 & 0 & 0 & 0 & 0 +\end{bmatrix}$$ + +### 列 3-5（r_Δv） + +$$\mathbf{J}_{[3:6,\ :]} = \begin{bmatrix} +-sR_{00} & -sR_{01} & -sR_{02} & sR_{00} & sR_{01} & sR_{02} & -JV^g_{00} & -JV^g_{01} & -JV^g_{02} & -JV^a_{00} & -JV^a_{01} & -JV^a_{02} & gv_{00} & gv_{01} & sv_0 \\ +-sR_{10} & -sR_{11} & -sR_{12} & sR_{10} & sR_{11} & sR_{12} & -JV^g_{10} & -JV^g_{11} & -JV^g_{12} & -JV^a_{10} & -JV^a_{11} & -JV^a_{12} & gv_{10} & gv_{11} & sv_1 \\ +-sR_{20} & -sR_{21} & -sR_{22} & sR_{20} & sR_{21} & sR_{22} & -JV^g_{20} & -JV^g_{21} & -JV^g_{22} & -JV^a_{20} & -JV^a_{21} & -JV^a_{22} & gv_{20} & gv_{21} & sv_2 +\end{bmatrix}$$ + +### 列 6-8（r_Δp） + +$$\mathbf{J}_{[6:9,\ :]} = \begin{bmatrix} +-sR_{00}\Delta t & -sR_{01}\Delta t & -sR_{02}\Delta t & 0 & 0 & 0 & -JP^g_{00} & -JP^g_{01} & -JP^g_{02} & -JP^a_{00} & -JP^a_{01} & -JP^a_{02} & gp_{00} & gp_{01} & sp_0 \\ +-sR_{10}\Delta t & -sR_{11}\Delta t & -sR_{12}\Delta t & 0 & 0 & 0 & -JP^g_{10} & -JP^g_{11} & -JP^g_{12} & -JP^a_{10} & -JP^a_{11} & -JP^a_{12} & gp_{10} & gp_{11} & sp_1 \\ +-sR_{20}\Delta t & -sR_{21}\Delta t & -sR_{22}\Delta t & 0 & 0 & 0 & -JP^g_{20} & -JP^g_{21} & -JP^g_{22} & -JP^a_{20} & -JP^a_{21} & -JP^a_{22} & gp_{20} & gp_{21} & sp_2 +\end{bmatrix}$$ + +### 列 9-11（r_pg） + +$$\mathbf{J}_{[9:12,\ :]} = \begin{bmatrix} +0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ +0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ +0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 +\end{bmatrix}$$ + +### 列 12-14（r_pa） + +$$\mathbf{J}_{[12:15,\ :]} = \begin{bmatrix} +0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ +0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ +0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 +\end{bmatrix}$$ + +### 完整 15×15 J 矩陣（block 形式） + +把上面五個 sub-matrix 縱向疊起來就是完整的 15×15 J： + +$$\mathbf{J} = \begin{bmatrix} +\mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{A} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 2} & \mathbf{0}_{3\times 1} \\ +-s\mathbf{R} & s\mathbf{R} & -\mathbf{J}^v_g & -\mathbf{J}^v_a & \mathbf{gv} & \mathbf{sv} \\ +-s\mathbf{R}\Delta t & \mathbf{0}_{3\times 3} & -\mathbf{J}^p_g & -\mathbf{J}^p_a & \mathbf{gp} & \mathbf{sp} \\ +\mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{I}_3 & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 2} & \mathbf{0}_{3\times 1} \\ +\mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{I}_3 & \mathbf{0}_{3\times 2} & \mathbf{0}_{3\times 1} +\end{bmatrix}_{15 \times 15}$$ + +**統計**：3+3+3+3+3 列 × 15 欄 = 225 個元素，全部列出。 + +**零元素的位置**（共 130 個）： +- r_ΔR：除了 bg 的 9 個元素，其他 36 個都是 0 +- r_Δv：全部 45 個都非零 +- r_Δp：v1 的 9 個元素是 0，共 9 個 0 +- r_pg：除了 bg 對角線的 3 個 1，其他 42 個都是 0 +- r_pa：除了 ba 對角線的 3 個 1，其他 42 個都是 0 + +非零元素總數：9 (r_ΔR) + 45 (r_Δv) + 36 (r_Δp) + 3 (r_pg) + 3 (r_pa) = **96 個非零元素** + +--- + +**設定**：k=2（2 個 KF），M=1（1 個 MapPoint），1 個視覺觀測（KF₀ 觀測 p₁）。 + +**變數順序**（共 27 維）： + +| 欄 | 變數 | +|----|------| +| 0 | φ0_x（T0 旋轉 x） | +| 1 | φ0_y | +| 2 | φ0_z | +| 3 | ρ0_x（T0 平移 x） | +| 4 | ρ0_y | +| 5 | ρ0_z | +| 6 | φ1_x | +| 7 | φ1_y | +| 8 | φ1_z | +| 9 | ρ1_x | +| 10 | ρ1_y | +| 11 | ρ1_z | +| 12 | v0_x | +| 13 | v0_y | +| 14 | v0_z | +| 15 | v1_x | +| 16 | v1_y | +| 17 | v1_z | +| 18 | bg_x | +| 19 | bg_y | +| 20 | bg_z | +| 21 | ba_x | +| 22 | ba_y | +| 23 | ba_z | +| 24 | p1_x | +| 25 | p1_y | +| 26 | p1_z | + +**殘差列**（共 17 列）： + +| 列 | 殘差分量 | +|----|----------| +| 0-2 | r_ΔR | +| 3-5 | r_Δv | +| 6-8 | r_Δp | +| 9-11 | r_pg | +| 12-14 | r_pa | +| 15-16 | r_uv（視覺，2 維） | + +**矩陣記號縮寫** + +- `R = Rbw0 = Rwb0^T` +- `R₂ = Rbw0·Rwb1` +- `(JR_T0)_ij = -Jr⁻¹·Rwb1^T·Rwb0` 第 i 列第 j 欄 +- `(Jr⁻¹)_ij`：InverseRightJacobian 矩陣元素 +- `(Jbg_R)_ij = (-Jr⁻¹·eR^T·Jr(JRg·δbg)·JRg)_ij` +- `(Jv_T0)_ij = [R(v1-v0-g·Δt)]×_ij`（hat operator） +- `(Jp_T0)_ij = [R(t1-t0-v0·Δt-½g·Δt²)]×_ij` +- `JVg_ij, JVa_ij, JPg_ij, JPa_ij`：bias Jacobian 元素 +- `(Jpose)_ij = (Jproj·Rcb·S_SE3)_ij`（2×6，對 T0） +- `(Jpoint)_ij = (-Jproj·Rcw0)_ij`（2×3，對 p1） + +由於完整 17×27 = 459 個元素太寬，每個 row-block 拆成 LaTeX bmatrix。 + +**符號縮寫**（每個都是 3×3 或 2×6 等矩陣的元素）： + +- $B_{ij} = (\mathbf{JR}_{T0})_{ij} = (-\mathbf{J}_r^{-1} \mathbf{R}_{wb_1}^T \mathbf{R}_{wb_0})_{ij}$ +- $C_{ij} = (\mathbf{J}_r^{-1})_{ij}$ +- $D_{ij} = (\mathbf{Jv}_{T0})_{ij} = ([\mathbf{R}(\mathbf{v}_1 - \mathbf{v}_0 - \mathbf{g}\Delta t)]_\times)_{ij}$ +- $E_{ij} = (\mathbf{Jp}_{T0})_{ij} = ([\mathbf{R}(\mathbf{t}_1 - \mathbf{t}_0 - \mathbf{v}_0\Delta t - \tfrac{1}{2}\mathbf{g}\Delta t^2)]_\times)_{ij}$ +- $A_{ij} = (\mathbf{J}_{bg,R})_{ij}$ +- $R_{ij} = (\mathbf{R}_{bw_0})_{ij}$ +- $R^2_{ij} = (\mathbf{R}_{bw_0} \mathbf{R}_{wb_1})_{ij}$ +- $P_{ij} = (\mathbf{J}_{pose})_{ij}$（2×6，Jproj·Rcb·S_SE3） +- $Q_{ij} = (\mathbf{J}_{point})_{ij}$（2×3，-Jproj·Rcw0） + +### A.2.1 列 0-2（r_ΔR） + +$$\mathbf{J}_{[0:3,\ :]} = \begin{bmatrix} +B_{00} & B_{01} & B_{02} & 0 & 0 & 0 & C_{00} & C_{01} & C_{02} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & A_{00} & A_{01} & A_{02} & 0 & 0 & 0 & 0 & 0 & 0 \\ +B_{10} & B_{11} & B_{12} & 0 & 0 & 0 & C_{10} & C_{11} & C_{12} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & A_{10} & A_{11} & A_{12} & 0 & 0 & 0 & 0 & 0 & 0 \\ +B_{20} & B_{21} & B_{22} & 0 & 0 & 0 & C_{20} & C_{21} & C_{22} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & A_{20} & A_{21} & A_{22} & 0 & 0 & 0 & 0 & 0 & 0 +\end{bmatrix}$$ + +### A.2.2 列 3-5（r_Δv） + +$$\mathbf{J}_{[3:6,\ :]} = \begin{bmatrix} +D_{00} & D_{01} & D_{02} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -R_{00} & -R_{01} & -R_{02} & R_{00} & R_{01} & R_{02} & -JV^g_{00} & -JV^g_{01} & -JV^g_{02} & -JV^a_{00} & -JV^a_{01} & -JV^a_{02} & 0 & 0 & 0 \\ +D_{10} & D_{11} & D_{12} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -R_{10} & -R_{11} & -R_{12} & R_{10} & R_{11} & R_{12} & -JV^g_{10} & -JV^g_{11} & -JV^g_{12} & -JV^a_{10} & -JV^a_{11} & -JV^a_{12} & 0 & 0 & 0 \\ +D_{20} & D_{21} & D_{22} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -R_{20} & -R_{21} & -R_{22} & R_{20} & R_{21} & R_{22} & -JV^g_{20} & -JV^g_{21} & -JV^g_{22} & -JV^a_{20} & -JV^a_{21} & -JV^a_{22} & 0 & 0 & 0 +\end{bmatrix}$$ + +### A.2.3 列 6-8（r_Δp） + +$$\mathbf{J}_{[6:9,\ :]} = \begin{bmatrix} +E_{00} & E_{01} & E_{02} & -1 & 0 & 0 & 0 & 0 & 0 & R^2_{00} & R^2_{01} & R^2_{02} & -R_{00}\Delta t & -R_{01}\Delta t & -R_{02}\Delta t & 0 & 0 & 0 & -JP^g_{00} & -JP^g_{01} & -JP^g_{02} & -JP^a_{00} & -JP^a_{01} & -JP^a_{02} & 0 & 0 & 0 \\ +E_{10} & E_{11} & E_{12} & 0 & -1 & 0 & 0 & 0 & 0 & R^2_{10} & R^2_{11} & R^2_{12} & -R_{10}\Delta t & -R_{11}\Delta t & -R_{12}\Delta t & 0 & 0 & 0 & -JP^g_{10} & -JP^g_{11} & -JP^g_{12} & -JP^a_{10} & -JP^a_{11} & -JP^a_{12} & 0 & 0 & 0 \\ +E_{20} & E_{21} & E_{22} & 0 & 0 & -1 & 0 & 0 & 0 & R^2_{20} & R^2_{21} & R^2_{22} & -R_{20}\Delta t & -R_{21}\Delta t & -R_{22}\Delta t & 0 & 0 & 0 & -JP^g_{20} & -JP^g_{21} & -JP^g_{22} & -JP^a_{20} & -JP^a_{21} & -JP^a_{22} & 0 & 0 & 0 +\end{bmatrix}$$ + +### A.2.4 列 9-11（r_pg） + +$$\mathbf{J}_{[9:12,\ :]} = \begin{bmatrix} +0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ +0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ +0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 +\end{bmatrix}$$ + +### A.2.5 列 12-14（r_pa） + +$$\mathbf{J}_{[12:15,\ :]} = \begin{bmatrix} +0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ +0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ +0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 +\end{bmatrix}$$ + +### A.2.6 列 15-16（r_uv，視覺） + +$$\mathbf{J}_{[15:17,\ :]} = \begin{bmatrix} +P_{00} & P_{01} & P_{02} & P_{03} & P_{04} & P_{05} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & Q_{00} & Q_{01} & Q_{02} \\ +P_{10} & P_{11} & P_{12} & P_{13} & P_{14} & P_{15} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & Q_{10} & Q_{11} & Q_{12} +\end{bmatrix}$$ + +### A.2.7 完整 17×27 J 矩陣（block 形式） + +$$\mathbf{J} = \begin{bmatrix} +\mathbf{B} & \mathbf{0}_{3\times 3} & \mathbf{C} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{A} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} \\ +\mathbf{D} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & -\mathbf{R} & \mathbf{R} & -\mathbf{J}^v_g & -\mathbf{J}^v_a & \mathbf{0}_{3\times 3} \\ +\mathbf{E} & -\mathbf{I}_3 & \mathbf{0}_{3\times 3} & \mathbf{R}^2 & -\mathbf{R}\Delta t & \mathbf{0}_{3\times 3} & -\mathbf{J}^p_g & -\mathbf{J}^p_a & \mathbf{0}_{3\times 3} \\ +\mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{I}_3 & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} \\ +\mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{0}_{3\times 3} & \mathbf{I}_3 & \mathbf{0}_{3\times 3} \\ +\mathbf{P}_L & \mathbf{P}_R & \mathbf{0}_{2\times 3} & \mathbf{0}_{2\times 3} & \mathbf{0}_{2\times 3} & \mathbf{0}_{2\times 3} & \mathbf{0}_{2\times 3} & \mathbf{0}_{2\times 3} & \mathbf{Q} +\end{bmatrix}_{17 \times 27}$$ + +其中 $\mathbf{P}_L$、$\mathbf{P}_R$ 分別是 $\mathbf{J}_{pose}$ 的左 3 欄和右 3 欄。 + +### A.2.7 統計 + +- 總元素數：17 × 27 = **459 個** +- r_ΔR：A 區 18 個（9 非零）+ B 區 36 個（9 非零）+ C 區 9 個（0）= 9+9 = 18 個非零 +- r_Δv：A 區 18 個（9 非零）+ B 區 36 個（36 非零）+ C 區 9 個（0）= 9+36 = 45 個非零 +- r_Δp：A 區 18 個（12 非零，含 -I3 三個 -1）+ B 區 36 個（27 非零）+ C 區 9 個（0）= 12+27 = 39 個非零 +- r_pg：3 個非零（對角線） +- r_pa：3 個非零（對角線） +- r_uv：A 區 12 個非零 + C 區 6 個非零 = 18 個非零 + +**總非零元素：18+45+39+3+3+18 = 126 個** + +**零元素：459 - 126 = 333 個**（佔 72.5%） + +這就是為什麼需要稀疏求解器（g2o `LinearSolverEigen` 配 `BlockSolverX`）和 Schur complement 來高效求解。 + +### A.2.8 Schur complement 後的 reduced H + +把 MapPoint 變數（p1）消去後，剩下 24 個變數的 reduced system： + +``` + T0(6) T1(6) v0(3) v1(3) bg(3) ba(3) + ┌ ┐ +H_cc^Schur =│ 24 × 24 dense (or near-dense) │ + └ ┘ +``` + +MapPoint 被消去後，IMU 變數之間透過視覺項變得相關（fill-in），這個矩陣大致是滿的，但維度只有 24，可以用 Cholesky 直接解。 + diff --git a/study/imu_initialization.md b/study/imu_initialization.md new file mode 100644 index 00000000000..b1592785b91 --- /dev/null +++ b/study/imu_initialization.md @@ -0,0 +1,271 @@ +# IMU 初始化數學（ORB-SLAM3 Section V.B） + +論文來源：J21-ORB_SLAM3.pdf，Section V.B "IMU Initialization" + +--- + +## 時間軸 + +### t = 0s +Tracking 開始，持續插入 KF，IMU 資料持續預積分。 + +--- + +### t ≈ 2s　｜　KF 數量 >= 10　→　**初始化** + +- Step 1 完成：純視覺地圖建立（up-to-scale） +- `InitializeIMU()` 第一次呼叫（`priorG = 1e2`） + - `InertialOptimization()`：估出 $s, \mathbf{R}_{wg}, \mathbf{b}, \bar{\mathbf{v}}$ + - `ApplyScaledRotation()`：scale 和重力對齊套用到地圖 + - `FullInertialBA()`：Visual-Inertial BA +- `isImuInitialized() = true` + +--- + +### t > 5s　→　**VIBA 1** + +- `InitializeIMU()` 第二次呼叫（`priorG = 1.f`，放鬆 gyro prior） + - `InertialOptimization()` + `FullInertialBA()` +- bias 估計開始收斂 + +--- + +### t > 15s　→　**VIBA 2** + +- `InitializeIMU()` 第三次呼叫（`priorG = 0.f, priorA = 0.f`，完全移除 prior） + - `InertialOptimization()` + `FullInertialBA()` +- 地圖進入成熟狀態 + +--- + +### t = 25 / 35 / 45s　→　**Scale Refinement**（僅 Monocular） + +- 條件：KF 數量 <= 200 +- `ScaleRefinement()`：每隔 10 秒修正一次 scale +- 注意：程式碼寫了 55 / 65 / 75s 的觸發條件，但因外層有 `mTinit < 50.0f` 擋住，實際上永遠不會執行到 + +--- + +### t >= 50s + +- 外層條件 `mTinit < 50.0f`（`LocalMapping.cc:235`）不再成立 +- 整個初始化流程結束，不再進入 + +--- + +## 整體概念 + +IMU 初始化的目標是為以下惰性變數找到好的初始值，再交給後續的 Visual-Inertial BA： + +- **scale** $s$：Monocular 的地圖差一個未知尺度 +- **重力方向** $\mathbf{R}_{wg}$：世界座標系對齊重力的旋轉 +- **bias** $\mathbf{b} = (\mathbf{b}^a, \mathbf{b}^g) \in \mathbb{R}^6$：加速度計 + 陀螺儀的系統性偏差 +- **速度** $\bar{\mathbf{v}}_{0:k}$：各 KF 的初始速度估計 + +--- + +## Step 1 — Vision-Only MAP Estimation + +先跑純視覺 SLAM 約 2 秒，建出一張 **up-to-scale** 的地圖（k = 10 個 KF）。 + +得到的位姿為： + +$$\bar{\mathbf{T}}_{0:k} = [\mathbf{R},\ \bar{\mathbf{p}}]$$ + +其中 $\bar{\mathbf{p}}$ 是差一個 scale 的位置（bar 表示 up-to-scale）。 + +**程式位置**：`LocalMapping.cc:184`，`InitializeIMU()` 觸發之前，LocalMapping 主迴圈在 IMU 尚未初始化時每次都跑 `Optimizer::LocalBundleAdjustment()`，持續約 2 秒所累積的結果就是 Step 1 的輸出。`InitializeIMU()` 進來時直接把這個視覺地圖當作已知輸入。 + +--- + +## Step 2 — Inertial-Only MAP Estimation + +**對應函式**：`Optimizer::InertialOptimization()`（`Optimizer.cc:3042`） + +只用 IMU preintegration 資料，不加視覺 reprojection error。KF 的位姿在這個階段**全部固定不動**（`VP->setFixed(true)`），只優化慣性變數。 + +### 優化變數 → g2o 頂點對應 + +$$\mathcal{Y}_k = \{s,\ \mathbf{R}_{wg},\ \mathbf{b},\ \bar{\mathbf{v}}_{0:k}\}$$ + +| 論文變數 | g2o 頂點 | 說明 | +|----------|----------|------| +| $s$ | `VertexScale` | Stereo 時 `setFixed(true)`，scale 已知 | +| $\mathbf{R}_{wg}$ | `VertexGDir` | 重力方向，在 SO(3) 流形上更新 | +| $\mathbf{b}^g$ | `VertexGyroBias` | 陀螺儀 bias，所有 KF 共用一個 | +| $\mathbf{b}^a$ | `VertexAccBias` | 加速度計 bias，所有 KF 共用一個 | +| $\bar{\mathbf{v}}_i$ | `VertexVelocity`（每個 KF 一個）| 速度 | + +### 優化問題（論文公式 8） + +$$\mathcal{Y}_k^* = \arg\min_{\mathcal{Y}_k} \left( \|\mathbf{b}\|^2_{\Sigma_b} + \sum_{i=1}^{k} \|\mathbf{r}_{\mathcal{I}_{i-1,i}}\|^2_{\Sigma^{-1}_{\mathcal{I}_{i-1,i}}} \right)$$ + +### Cost 項 → g2o 邊對應 + +**第一項**：bias prior（讓 bias 偏向零） + +$$\|\mathbf{b}\|^2_{\Sigma_b}$$ + +``` +EdgePriorGyro → information = priorG * I₃ +EdgePriorAcc → information = priorA * I₃ +``` + +prior 強度越大，bias 被拉越靠近零。VIBA 2 時 `priorG = priorA = 0`，代表完全移除此項。 + +**第二項**：IMU preintegration residual（`EdgeInertialGS`） + +$$\|\mathbf{r}_{\mathcal{I}_{i-1,i}}\|^2_{\Sigma^{-1}_{\mathcal{I}_{i-1,i}}}$$ + +這個 edge 連接 8 個頂點： + +``` +EdgeInertialGS(T_prev, V_prev, bg, ba, T_curr, V_curr, Rwg, scale) +``` + +`GS` = Gravity + Scale，代表這條 edge 把重力方向和 scale 也納入優化。這是 Step 2 專屬的 edge 型別。 + +### IMU Preintegration Residual（論文公式 2） + +$$\mathbf{r}_{\mathcal{I}_{i,i+1}} = [\mathbf{r}_{\Delta\mathbf{R}},\ \mathbf{r}_{\Delta\mathbf{v}},\ \mathbf{r}_{\Delta\mathbf{p}}]$$ + +$$\mathbf{r}_{\Delta\mathbf{R}} = \text{Log}\left(\Delta\mathbf{R}_{i,i+1}^T \mathbf{R}_i^T \mathbf{R}_{i+1}\right)$$ + +$$\mathbf{r}_{\Delta\mathbf{v}} = \mathbf{R}_i^T\left(\mathbf{v}_{i+1} - \mathbf{v}_i - \mathbf{g}\Delta t\right) - \Delta\mathbf{v}_{i,i+1}$$ + +$$\mathbf{r}_{\Delta\mathbf{p}} = \mathbf{R}_i^T\left(\mathbf{p}_{i+1} - \mathbf{p}_i - \mathbf{v}_i\Delta t - \frac{1}{2}\mathbf{g}\Delta t^2\right) - \Delta\mathbf{p}_{i,i+1}$$ + +其中 $\Delta\mathbf{R}, \Delta\mathbf{v}, \Delta\mathbf{p}$ 是 `ImuTypes.cc` 的 `IntegrateNewMeasurement()` 累積出的預積分量。 + +### 重力方向的更新（論文公式 9） + +$\mathbf{R}_{wg}$ 在 SO(3) 流形上更新，但只允許繞非重力軸旋轉： + +$$\mathbf{R}_{wg}^{\text{new}} = \mathbf{R}_{wg}^{\text{old}} \cdot \text{Exp}(\delta\alpha_g,\ \delta\beta_g,\ 0)$$ + +z 軸分量固定為 0，防止重力大小被改動。對應 `VertexGDir` 的 `oplusImpl()`。 + +### Scale 的更新（論文公式 10） + +$$s^{\text{new}} = s^{\text{old}} \cdot \exp(\delta s)$$ + +指數參數化強制 scale 為正數。對應 `VertexScale` 的 `oplusImpl()`。 + +--- + +## Step 3 — Visual-Inertial MAP Estimation + +**對應函式**：`Optimizer::FullInertialBA()`（`Optimizer.cc:392`） + +有了好的初始值後，把視覺 reprojection error 加回來，做完整的 Visual-Inertial BA。 + +### 和 Step 2 的關鍵差異 + +| | Step 2 `InertialOptimization` | Step 3 `FullInertialBA` | +|-|-------------------------------|--------------------------| +| KF 位姿 | **固定不動** | **可優化** | +| scale $s$ | 優化變數（Mono）| 不存在，已套用到地圖 | +| 重力 $\mathbf{R}_{wg}$ | 優化變數 | 不存在，已套用到地圖 | +| bias | 所有 KF 共用一組 | 每個 KF 各自一組（`bInit=false` 時）| +| IMU edge | `EdgeInertialGS`（含 scale、Rwg）| `EdgeInertial`（不含 scale、Rwg）| +| bias 連續性 | 無 | `EdgeGyroRW` + `EdgeAccRW` | +| 視覺項 | 無 | 有 reprojection error | + +### 優化問題（論文公式 4） + +$$\min_{\mathcal{S}_k,\ \mathcal{X}} \left( \sum_{i=1}^{k} \|\mathbf{r}_{\mathcal{I}_{i-1,i}}\|^2_{\Sigma^{-1}} + \sum_{j=0}^{l-1} \sum_{i \in \mathcal{K}^j} \rho_{\text{Hub}}\left(\|\mathbf{r}_{ij}\|_{\Sigma^{-1}_{ij}}\right) \right)$$ + +### Cost 項 → g2o 邊對應 + +**IMU 項**（`EdgeInertial`）： + +``` +EdgeInertial(T_prev, V_prev, bg_prev, ba_prev, T_curr, V_curr) +``` + +注意這裡沒有 `Rwg` 和 `scale`，因為這兩個已在 Step 2 後套用到地圖，不再是變數。 + +**Bias random walk**（論文圖 2(a) 的紫色邊）： + +``` +EdgeGyroRW(bg_prev, bg_curr) → bias 在相鄰 KF 間應平滑變化 +EdgeAccRW(ba_prev, ba_curr) +``` + +**視覺項**：`EdgeMono` / `EdgeStereo`（binary edge，連接 VertexSBAPointXYZ + VertexPose；Huber kernel，閾值 $\sqrt{5.991}$ mono、$\sqrt{7.815}$ stereo） + +--- + +## 整體流程總結 + +``` +LocalMapping::Run() +│ +└─ LocalMapping::InitializeIMU() + │ + ├─ [Step 1 已由 LocalBundleAdjustment 完成] 純視覺地圖，up-to-scale + │ + ├─ Step 2: Optimizer::InertialOptimization() + │ ├─ 頂點：VertexScale, VertexGDir, VertexGyroBias, VertexAccBias, VertexVelocity×N + │ ├─ 邊：EdgeInertialGS×N（含 scale + Rwg） + │ └─ 邊：EdgePriorGyro + EdgePriorAcc（prior） + │ → 輸出：s, Rwg, bg, ba, v 的初始估計 + │ + ├─ ApplyScaledRotation() ← 把 s 和 Rwg 套用到整張地圖，之後不再優化這兩個 + │ + └─ Step 3: Optimizer::FullInertialBA() + ├─ 頂點：VertexPose×N, VertexVelocity×N + ├─ 頂點：VertexGyroBias×1, VertexAccBias×1（bInit=true，初始化/VIBA1） + ├─ 頂點：VertexGyroBias×N, VertexAccBias×N（bInit=false，VIBA2） + ├─ 邊：EdgeInertial×N（不含 scale + Rwg） + ├─ 邊：EdgeGyroRW + EdgeAccRW（bInit=false 時才加） + ├─ 邊：EdgeMono / EdgeStereo reprojection error（Huber kernel） + └─ 邊：EdgePriorGyro + EdgePriorAcc（bInit=true 時才加） +``` + +--- + +## 三階段差異 + +### 初始化（t ≈ 2s） + +**目標**：第一次估出 scale、重力方向、bias、速度的粗略初始值。 + +- `InertialOptimization`：scale $s$ 和重力方向 $\mathbf{R}_{wg}$ 從頭估起，都是優化變數 +- `ApplyScaledRotation()`：把估出的 $s$ 和 $\mathbf{R}_{wg}$ 套用到整張地圖（**三個階段都有執行**） +- `FullInertialBA`（`bInit=true`，因為 priorA ≠ 0）：所有 KF 共用一組 bias，加大 prior（priorG=1e2, priorA=1e5/1e10）把 bias 拉向零，沒有 EdgeGyroRW/AccRW +- `isImuInitialized()` 設為 true + +--- + +### VIBA 1（t > 5s） + +**目標**：放鬆 gyro prior，讓 bias 開始收斂到合理值。 + +- `InertialOptimization`：`mRwg` 起始為 I、`mScale` 起始為 1.0（重力和 scale 已在初始化時套用到地圖，從 neutral 出發），但 `VertexGDir` 和 `VertexScale`（mono）仍是**可優化**的變數，並非固定 +- `ApplyScaledRotation()`：同樣執行，把本次 InertialOptimization 的結果套用回地圖 +- `FullInertialBA`（`bInit=true`，因為 priorA=1e5 ≠ 0）：仍然共用 bias、仍然有 prior，但 priorG 從 1e2 大幅降到 1.f，讓 gyro bias 自由移動；priorA 維持 1e5 +- 和初始化的差別：只有 priorG 改變，gyro 約束大幅放鬆 + +--- + +### VIBA 2（t > 15s） + +**目標**：完全移除 prior，bias 改用 random walk 模型，讓地圖自由優化到最佳解。 + +- `InertialOptimization`：同樣執行，priorG=priorA=0 使得 prior 邊的 information=0，實際貢獻為零 +- `ApplyScaledRotation()`：同樣執行 +- `FullInertialBA`（`bInit=false`，因為 priorA=0）：prior 完全移除，bias 改為每個 KF 各自一組，並加入 EdgeGyroRW/EdgeAccRW 約束 bias 的連續性 +- 和 VIBA 1 的差別：`bInit` 從 true 變成 false，bias 模型從「所有 KF 共用常數」升級為「random walk」 +- 執行後地圖進入成熟狀態（mature） + +--- + +## 三階段 Prior 強度 + +| 階段 | 時間 | priorG | priorA | 目的 | +|------|------|--------|--------|------| +| 初始化 | t ≈ 0s | 1e2 | 1e5（stereo）/ 1e10（mono）| 大 prior 穩住解，防止 bias 亂飛 | +| VIBA 1 | t > 5s | 1.f | 1e5 | 放鬆 gyro prior，讓 bias 自由收斂 | +| VIBA 2 | t > 15s | 0.f | 0.f | 完全移除 prior，地圖自己決定 | +| Scale Refinement | t = 25/35/.../75s | — | — | 僅 Mono，純 scale 優化 | diff --git a/study/inertial_optimization_math.md b/study/inertial_optimization_math.md new file mode 100644 index 00000000000..9380f9f7088 --- /dev/null +++ b/study/inertial_optimization_math.md @@ -0,0 +1,167 @@ +# InertialOptimization() 數學步驟 + +對應程式：`Optimizer::InertialOptimization()`（`Optimizer.cc:3042`） +Edge 實作：`EdgeInertialGS::computeError()`（`G2oTypes.cc:617`） + +--- + +## 1. 優化變數（頂點） + +| 頂點 | 維度 | 說明 | +|------|------|------| +| `VertexPose` × N | 6 | KF 位姿 $\mathbf{T}_i = (\mathbf{R}_{wb_i}, \mathbf{t}_{wb_i})$，**固定不優化** | +| `VertexVelocity` × N | 3 | 各 KF 速度 $\mathbf{v}_i$（world frame） | +| `VertexGyroBias` | 3 | 陀螺儀 bias $\mathbf{b}^g$，所有 KF 共用 | +| `VertexAccBias` | 3 | 加速度計 bias $\mathbf{b}^a$，所有 KF 共用 | +| `VertexGDir` | 2 | 重力方向 $\mathbf{R}_{wg}$（2 自由度，z 軸固定） | +| `VertexScale` | 1 | 尺度 $s$（Stereo 時固定為 1） | + +--- + +## 2. 總 Cost Function + +$$\min \left( \underbrace{\|\mathbf{b}^g\|^2_{\text{priorG} \cdot I} + \|\mathbf{b}^a\|^2_{\text{priorA} \cdot I}}_{\text{bias prior}} + \sum_{i=1}^{N} \underbrace{\|\mathbf{r}_{\mathcal{I}_{i-1,i}}\|^2_{\Sigma^{-1}_i}}_{\text{IMU residual}} \right)$$ + +--- + +## 3. Bias Prior（`EdgePriorGyro` + `EdgePriorAcc`） + +### Residual + +$$\mathbf{r}_{\text{prior},g} = \mathbf{0} - \mathbf{b}^g$$ + +$$\mathbf{r}_{\text{prior},a} = \mathbf{0} - \mathbf{b}^a$$ + +目標值是零向量，代表「假設 bias 應該接近零」。Cost 取平方後符號不影響結果，但程式碼（`G2oTypes.h:779`）的實際計算是 `bprior - estimate`。 + +### Information Matrix + +$$\Omega_{\text{prior}} = \text{priorG} \cdot I_3 \quad \text{（或 priorA）}$$ + +`priorG` / `priorA` 越大 → bias 被拉得越靠近零。VIBA 2 時設為 0，代表拿掉此項。 + +### Jacobian（`G2oTypes.cc:762`） + +理論上 $\mathbf{r} = \mathbf{0} - \mathbf{b}$，所以： + +$$\frac{\partial \mathbf{r}}{\partial \mathbf{b}} = -I_3$$ + +但程式碼實際寫 `+I₃`。符號錯誤不影響 Hessian（$J^T\Omega J = \Omega$ 無論正負），只影響 gradient 方向。因為 prior 是弱約束，`EdgeInertialGS` 主導整體優化，實務上不影響收斂。 + +--- + +## 4. IMU Preintegration Residual（`EdgeInertialGS`） + +每對相鄰 KF $(i-1, i)$ 建一條邊，連接 8 個頂點： + +$$(\mathbf{T}_{i-1},\ \mathbf{v}_{i-1},\ \mathbf{b}^g,\ \mathbf{b}^a,\ \mathbf{T}_i,\ \mathbf{v}_i,\ \mathbf{R}_{wg},\ s)$$ + +### 4.1 預積分量的 bias 修正（`ImuTypes.cc:283`） + +預積分量是在某個線性化點 $\bar{\mathbf{b}}$ 計算的。當 bias 變為 $\mathbf{b}$，用一階 Taylor 展開修正，不需重積分： + +$$\Delta\mathbf{R}(\mathbf{b}) = \bar{\Delta\mathbf{R}} \cdot \text{Exp}\left(\mathbf{J}^R_g \cdot \delta\mathbf{b}^g\right)$$ + +$$\Delta\mathbf{v}(\mathbf{b}) = \bar{\Delta\mathbf{v}} + \mathbf{J}^v_g \cdot \delta\mathbf{b}^g + \mathbf{J}^v_a \cdot \delta\mathbf{b}^a$$ + +$$\Delta\mathbf{p}(\mathbf{b}) = \bar{\Delta\mathbf{p}} + \mathbf{J}^p_g \cdot \delta\mathbf{b}^g + \mathbf{J}^p_a \cdot \delta\mathbf{b}^a$$ + +其中 $\delta\mathbf{b} = \mathbf{b} - \bar{\mathbf{b}}$，$\mathbf{J}^R_g, \mathbf{J}^v_g, \mathbf{J}^v_a, \mathbf{J}^p_g, \mathbf{J}^p_a$ 是預積分過程中計算好的 Jacobian。 + +### 4.2 重力向量 + +$$\mathbf{g} = \mathbf{R}_{wg} \cdot \mathbf{g}_I, \quad \mathbf{g}_I = [0,\ 0,\ -9.81]^T$$ + +$\mathbf{R}_{wg}$ 把 IMU 定義的重力方向轉到世界座標系。 + +### 4.3 Residual（`G2oTypes.cc:617`） + +**旋轉殘差**（3 維）： + +$$\mathbf{r}_{\Delta R} = \text{Log}\!\left(\Delta\mathbf{R}(\mathbf{b})^T \cdot \mathbf{R}_{wb_1}^T \cdot \mathbf{R}_{wb_2}\right)$$ + +量測值 $\Delta\mathbf{R}$ 應等於 $\mathbf{R}_{wb_1}^T \mathbf{R}_{wb_2}$，差值取 Log 映射回向量空間。 + +**速度殘差**（3 維）： + +$$\mathbf{r}_{\Delta v} = \mathbf{R}_{wb_1}^T \left(s(\mathbf{v}_2 - \mathbf{v}_1) - \mathbf{g}\cdot\Delta t\right) - \Delta\mathbf{v}(\mathbf{b})$$ + +$s$ 乘在速度差上，因為地圖是 up-to-scale，速度也帶有同一個 scale。 + +**位置殘差**（3 維）： + +$$\mathbf{r}_{\Delta p} = \mathbf{R}_{wb_1}^T \left(s(\mathbf{t}_{wb_2} - \mathbf{t}_{wb_1} - \mathbf{v}_1\Delta t) - \frac{1}{2}\mathbf{g}\Delta t^2\right) - \Delta\mathbf{p}(\mathbf{b})$$ + +合併成 9 維 residual： + +$$\mathbf{r}_{\mathcal{I}} = [\mathbf{r}_{\Delta R},\ \mathbf{r}_{\Delta v},\ \mathbf{r}_{\Delta p}]^T \in \mathbb{R}^9$$ + +### 4.4 Information Matrix + +$$\Omega_i = \Sigma_i^{-1}, \quad \Sigma_i = \mathbf{C}_{[0:9,\ 0:9]}$$ + +$\mathbf{C}$ 是預積分過程中累積的 $9\times9$ covariance（旋轉、速度、位置），在 `IntegrateNewMeasurement()` 裡遞推計算。 + +--- + +## 5. 頂點更新規則 + +### VertexVelocity / VertexGyroBias / VertexAccBias + +加法更新（歐氏空間）： + +$$\mathbf{v}^{\text{new}} = \mathbf{v}^{\text{old}} + \delta\mathbf{v}$$ + +### VertexGDir（`G2oTypes.h:264`） + +在 SO(3) 流形上更新，z 軸固定為 0（論文公式 9）： + +$$\mathbf{R}_{wg}^{\text{new}} = \mathbf{R}_{wg}^{\text{old}} \cdot \text{Exp}(\delta\alpha,\ \delta\beta,\ 0)$$ + +只有兩個自由度，防止重力「縮放」（旋轉量的 z 分量鎖死）。 + +### VertexScale（`G2oTypes.h:314`） + +指數更新，強制 scale 為正數（論文公式 10）： + +$$s^{\text{new}} = s^{\text{old}} \cdot \exp(\delta s)$$ + +--- + +## 6. 優化流程 + +``` +1. 建立頂點：VertexPose（fixed）, VertexVelocity, VertexGyroBias, + VertexAccBias, VertexGDir, VertexScale + +2. 建立邊： + - EdgePriorGyro（bias^g → 0） + - EdgePriorAcc（bias^a → 0） + - EdgeInertialGS × N（每對相鄰 KF 一條） + +3. Levenberg-Marquardt 迭代 200 次 + 每次迭代： + a. computeError()：計算所有邊的 residual + b. linearizeOplus()：計算所有邊對各頂點的 Jacobian + c. 組裝 H = J^T Ω J，b = J^T Ω r + d. 解 (H + λI)δx = -b + e. 用各頂點的 oplusImpl() 更新估計值 + +4. 讀回優化結果：s, Rwg, bg, ba, v_i +5. 對每個 KF：若 bias 變化量 norm > 0.01 才呼叫 Reintegrate() 重新計算預積分 + 否則只呼叫 SetNewBias() 更新 bias 值（Optimizer.cc:3213） +``` + +--- + +## 7. 和 FullInertialBA 的差異 + +| | `InertialOptimization` | `FullInertialBA` | +|-|------------------------|-----------------| +| KF 位姿 | 固定 | 可優化 | +| scale $s$ | 優化變數 | 不存在（已套用到地圖）| +| 重力 $\mathbf{R}_{wg}$ | 優化變數 | 不存在（已套用到地圖）| +| bias | 所有 KF 共用一組 | 每個 KF 各一組 | +| IMU edge | `EdgeInertialGS`（含 $s$, $\mathbf{R}_{wg}$）| `EdgeInertial`（不含）| +| bias 連續性 | 無 | `EdgeGyroRW` + `EdgeAccRW` | +| 視覺項 | 無 | reprojection error | diff --git a/study/trajectory_pure_visual.md b/study/trajectory_pure_visual.md new file mode 100644 index 00000000000..69e52f652ce --- /dev/null +++ b/study/trajectory_pure_visual.md @@ -0,0 +1,394 @@ +# ORB-SLAM3 軌跡研究（純視覺） + +## 1. 研究範圍與模式 + +在 ORB-SLAM3 中，純視覺模式對應 `System::eSensor` 的： + +- `MONOCULAR=0` +- `STEREO=1` +- `RGBD=2` + +參考：`include/System.h:87-94` + +--- + +## 2. 軌跡的核心資料結構（Tracking 端） + +`Tracking` 不會每幀直接輸出完整世界座標軌跡，而是先保存「相對於 reference keyframe 的 frame pose」： + +- `mlRelativeFramePoses`：每幀相對其 reference KF 的 SE3 變換 `Tcr` +- `mlpReferences`：每幀對應的 reference keyframe 指標 +- `mlFrameTimes`：每幀的時間戳 +- `mlbLost`：每幀是否為 lost 狀態（用於輸出時跳過丟失幀） + +參考：`include/Tracking.h:154-159` + +這四個 list 是最後 `System::SaveTrajectory*()` 重建完整軌跡的來源。 + +--- + +## 3. Tracking 狀態機 + +`Track()` 的行為取決於 `eTrackingState mState`，定義在 `include/Tracking.h:126-133`： + +| 狀態 | 值 | 含義 | +| :----------------- | :-: | :--------------------------------------- | +| `SYSTEM_NOT_READY` | -1 | 系統尚未就緒 | +| `NO_IMAGES_YET` | 0 | 尚未收到任何影像 | +| `NOT_INITIALIZED` | 1 | 收到影像但尚未完成初始化 | +| `OK` | 2 | 正常追蹤中 | +| `RECENTLY_LOST` | 3 | 剛丟失，嘗試重定位（純視覺 3 秒內超時） | +| `LOST` | 4 | 完全丟失，需要建新地圖 | + +狀態轉移（純視覺）： + +``` +NO_IMAGES_YET → NOT_INITIALIZED → OK +OK → RECENTLY_LOST（追蹤失敗且 map KF > 10） +OK → LOST（追蹤失敗且 map KF ≤ 10） +RECENTLY_LOST → OK（重定位成功） +RECENTLY_LOST → LOST（超過 3 秒仍無法重定位） +LOST → NO_IMAGES_YET（建立新地圖 / 重設） +``` + +參考：`src/Tracking.cc:2142-2237` + +--- + +## 4. 前端每幀 pose 估計（純視覺） + +主入口：`Tracking::Track()` (`src/Tracking.cc:1963`) + +### 4.1 選擇前端路徑的條件 + +在 `mState == OK` 的正常追蹤下： + +```cpp +if ((!mbVelocity && !pCurrentMap->isImuInitialized()) || + mCurrentFrame.mnId < mnLastRelocFrameId + 2) +{ + bOK = TrackReferenceKeyFrame(); // tracking_method = 0 (RefKF) +} +else +{ + bOK = TrackWithMotionModel(); // tracking_method = 1 (MM) + if (!bOK) + bOK = TrackReferenceKeyFrame(); // fallback to RefKF +} +``` + +也就是說： +- **沒有速度估計**（首幀 or 上幀丟失後恢復）→ `TrackReferenceKeyFrame` +- **剛做完重定位的前 2 幀** → `TrackReferenceKeyFrame` +- **其他情況** → 先試 `TrackWithMotionModel`，失敗才 fallback + +參考：`src/Tracking.cc:2130-2148` + +### 4.2 `TrackReferenceKeyFrame()`（BoW 匹配） + +步驟： + +1. 對當前幀計算 BoW 向量：`mCurrentFrame.ComputeBoW()` +2. 用 `ORBmatcher(0.7, true)` 做 `SearchByBoW(mpReferenceKF, mCurrentFrame, ...)` + - 匹配門檻：**BoW matches < 15 → 直接失敗** +3. 初始 pose 設為 `mLastFrame.GetPose()` +4. 呼叫 `Optimizer::PoseOptimization(&mCurrentFrame)` +5. 剔除 outlier，統計 `nmatchesMap`（有 >0 次觀測的 inlier 數量） +6. **成功條件：`nmatchesMap >= 10`**（純視覺）；IMU 模式則一律 return true + +參考：`src/Tracking.cc:2973-3034` + +### 4.3 `TrackWithMotionModel()`（恆速模型） + +步驟： + +1. **`UpdateLastFrame()`**： + - 用 `mlRelativeFramePoses.back()` × reference KF 的最新 pose 更新 `mLastFrame` 的姿態 + - 在 localization-only 模式下，還會為 stereo/RGBD 的 last frame 創建臨時 MapPoint（按深度排序，插入深度 < `mThDepth` 的點，至少保留 100 個近點） + - 參考：`src/Tracking.cc:3036-3108` + +2. **初值預測**（純視覺分支）： + ```cpp + mCurrentFrame.SetPose(mVelocity * mLastFrame.GetPose()); + ``` + +3. **投影匹配**：用 `ORBmatcher(0.9, true)` 做 `SearchByProjection` + - 搜索半徑 `th`：stereo=7，其他=15 + - 若匹配數 < 20 → 加倍半徑重試（`2*th`） + - **仍然 < 20 → 失敗**（純視覺返回 false） + +4. 呼叫 `Optimizer::PoseOptimization(&mCurrentFrame)` +5. 剔除 outlier，統計 `nmatchesMap` +6. **成功條件：`nmatchesMap >= 10`** + +參考：`src/Tracking.cc:3111-3208` + +### 4.4 `mVelocity` 的計算 + +每幀追蹤成功（`bOK` 或 `RECENTLY_LOST`）後： + +```cpp +Sophus::SE3f LastTwc = mLastFrame.GetPose().inverse(); +mVelocity = mCurrentFrame.GetPose() * LastTwc; // Tcl = Tc_curr * Tw_last +mbVelocity = true; +``` + +即 `mVelocity` = 當前幀 camera-to-world 相對於上一幀的增量，用於下一幀的恆速模型預測。 + +參考：`src/Tracking.cc:2415-2422` + +--- + +## 5. TrackLocalMap（Local Map 精煉） + +`TrackLocalMap()` 是前端追蹤的第二階段，用更多的 local map 點來 refine pose。 + +### 5.1 建立 Local Map + +1. **`UpdateLocalKeyFrames()`**： + - 當前幀的每個 MapPoint 投票給觀測過它的 KF → 建立 `keyframeCounter` + - 所有投票到的 KF 加入 `mvpLocalKeyFrames`，同時追蹤得票最高的 `pKFmax` 設為 `mpReferenceKF` + - 再加入這些 KF 的 covisibility 鄰居（最多 10 個中取 1 個）、子 KF、父 KF + - 上限 **80 個 local keyframes** + - 參考：`src/Tracking.cc:3740-3900` + +2. **`UpdateLocalPoints()`**： + - 收集 `mvpLocalKeyFrames` 中所有 KF 的 MapPoint → `mvpLocalMapPoints` + - 用 `mnTrackReferenceForFrame` 避免重複加入 + - 參考：`src/Tracking.cc:3710-3738` + +### 5.2 `SearchLocalPoints()` + +1. 已匹配的點標記為已見，不重複搜索 +2. 對 `mvpLocalMapPoints` 中未匹配的點做 frustum 檢查（`isInFrustum(pMP, 0.5)`） +3. 用 `ORBmatcher(0.8)` 做 `SearchByProjection` + - 搜索半徑 `th` 因情況而異： + - RGBD → th=3 + - 剛重定位 → th=5 + - LOST/RECENTLY_LOST → th=15 + - 預設 → th=1 + +參考：`src/Tracking.cc:3648-3708` + +### 5.3 Pose 優化與成功判定 + +純視覺條件下（IMU 未初始化），呼叫： + +```cpp +Optimizer::PoseOptimization(&mCurrentFrame); // opt_type = 0 +``` + +優化後統計 `mnMatchesInliers`（非 outlier 且 Observations > 0 的 MapPoint 數量），根據不同情境判斷追蹤是否成功： + +| 條件 | 門檻 | +| :--------------------------------------- | :--------------- | +| 重定位後 `mMaxFrames` 幀內 | ≥ 50 inliers | +| `RECENTLY_LOST` 狀態恢復 | ≥ 10 inliers | +| 一般純視覺（非 mono/非 IMU） | **≥ 30 inliers** | +| IMU_MONOCULAR（已初始化） | ≥ 15 inliers | +| IMU_STEREO / IMU_RGBD | ≥ 15 inliers | + +參考：`src/Tracking.cc:3235-3410` + +--- + +## 6. 純視覺 pose 優化本體 + +`Optimizer::PoseOptimization()` (`src/Optimizer.cc:814`) + +### 6.1 求解器設定 + +- **線性求解器**：`LinearSolverDense` +- **非線性優化演算法**：Levenberg-Marquardt（`OptimizationAlgorithmLevenberg`） +- **Vertex**：唯一的 `VertexSE3Expmap`（id=0），6 DoF frame pose + +### 6.2 邊（觀測）類型 + +| 觀測類型 | 邊類型 | 維度 | +| :----------------------------------- | :--------------------------------------- | :----- | +| Mono（`mvuRight[i] < 0`） | `EdgeSE3ProjectXYZOnlyPose` | 2D | +| Stereo（`mvuRight[i] >= 0`） | `EdgeStereoSE3ProjectXYZOnlyPose` | 3D | +| 右相機（fisheye 雙目） | `EdgeSE3ProjectXYZOnlyPoseToBody` | 2D | + +注意：RGBD 的 `mvuRight` 被填為虛擬右相機 u 值（由深度計算），所以走 **stereo 邊**。 + +### 6.3 Robust Kernel 與 χ² 門檻 + +- **Huber Kernel**： + - mono：`δ = √5.991`（對應 95% χ²(2)） + - stereo：`δ = √7.815`（對應 95% χ²(3)） +- **Information matrix**：`I × invSigma2[octave]`（根據特徵點的金字塔層級加權） + +### 6.4 迭代策略（4 輪） + +```cpp +const float chi2Mono[4] = {5.991, 5.991, 5.991, 5.991}; +const float chi2Stereo[4] = {7.815, 7.815, 7.815, 7.815}; +const int its[4] = {10, 10, 10, 10}; // 共 40 次迭代 +``` + +每輪結束後： +1. 計算每條邊的 χ² error +2. 超過門檻 → 標記為 outlier，設 `level=1`（下輪不參與優化） +3. 未超過 → 標記為 inlier，設 `level=0` +4. **第 3 輪（it==2）後移除 Robust Kernel**（讓最後一輪用純 L2） +5. 若總邊數 < 10 → 提前終止 + +**最低門檻**：初始對應數 `nInitialCorrespondences < 3` → 直接返回 0 + +### 6.5 回傳值 + +`return nInitialCorrespondences - nBad`：即最終 inlier 數量。同時已將優化後的 pose 寫回 `pFrame->SetPose()`。 + +參考：`src/Optimizer.cc:828-1114` + +--- + +## 7. 重定位（Relocalization） + +當純視覺追蹤進入 `RECENTLY_LOST` 狀態時會嘗試重定位： + +### 7.1 候選 KF 檢索 + +- 對當前幀計算 BoW，用 `mpKeyFrameDB->DetectRelocalizationCandidates()` 查詢候選 KF +- 候選為空 → 直接失敗 + +### 7.2 MLPnP RANSAC + +對每個候選 KF： +1. `ORBmatcher(0.75)` 做 `SearchByBoW`，匹配 < 15 → 丟棄 +2. 建立 `MLPnPsolver`，RANSAC 參數：`(0.99, 10, 300, 6, 0.5, 5.991)` + - 每次跑 5 次 RANSAC 迭代 +3. 若得到候選 pose → `PoseOptimization` + - inlier < 10 → 繼續下一個候選 + - inlier < 50 → 用 `SearchByProjection` 在較寬窗口追加匹配，再次優化 + - inlier ≥ 50 → **重定位成功** + +### 7.3 成功後的影響 + +- 設定 `mnLastRelocFrameId = mCurrentFrame.mnId` +- 之後的 `mMaxFrames` 幀內，TrackLocalMap 的成功門檻提高到 50 inliers +- 前 2 幀強制用 `TrackReferenceKeyFrame`（不走 motion model） + +參考：`src/Tracking.cc:3905-4060` + +--- + +## 8. 軌跡保存（reconstruction） + +### 8.1 frame 端先存相對 pose + +`Track()` 結尾，當 `mState==OK || mState==RECENTLY_LOST` 時： + +```cpp +if (mCurrentFrame.isSet()) { + Sophus::SE3f Tcr_ = mCurrentFrame.GetPose() * mCurrentFrame.mpReferenceKF->GetPoseInverse(); + mlRelativeFramePoses.push_back(Tcr_); + mlpReferences.push_back(mCurrentFrame.mpReferenceKF); + mlFrameTimes.push_back(mCurrentFrame.mTimeStamp); + mlbLost.push_back(mState == LOST); +} else { + // pose 無效 → 複製上一筆（避免 list 長度不一致） + mlRelativeFramePoses.push_back(mlRelativeFramePoses.back()); + mlpReferences.push_back(mlpReferences.back()); + mlFrameTimes.push_back(mlFrameTimes.back()); + mlbLost.push_back(mState == LOST); +} +``` + +參考：`src/Tracking.cc:2545-2568` + +### 8.2 `SaveTrajectoryTUM()` 輸出 + +1. 取第一個 KF 的 `Twc` 作為原點 offset `Two` +2. 遍歷所有 frame： + - `*lbL == true`（lost）→ 跳過不輸出 + - 取 reference KF `pKF`；若 `pKF->isBad()` → 沿 spanning tree 的 `mTcp` 往 parent 爬 + - 計算 `Trw = (累積的 Tcp) * pKF->GetPose() * Two` + - `Tcw = Tcr * Trw` + - 輸出 `Twc = Tcw.inverse()` 的平移 + 四元數（`x y z qx qy qz qw`） +3. 格式：`timestamp tx ty tz qx qy qz qw` + +參考：`src/System.cc:602-651` + +### 8.3 `SaveTrajectoryEuRoC()` 輸出 + +與 TUM 格式的差異： +- 在多地圖（Atlas）中只輸出 **最大的 map** +- 原點 offset：純視覺用 `vpKFs[0]->GetPoseInverse()`；VI 用 `vpKFs[0]->GetImuPose()` +- 若 reference KF 不屬於該 map → 跳過 +- 純視覺分支輸出 `Twc`（camera）： + ```cpp + Sophus::SE3f Twc = ((*lit) * Trw).inverse(); + ``` +- 時間戳以**奈秒**格式輸出（`1e9 * (*lT)`） + +參考：`src/System.cc:715-783`（純視覺分支在 `779-783`） + +--- + +## 9. Viewer 中即時軌跡/相機顯示 + +### 9.1 當前相機 pose 傳遞 + +Tracking 每幀追蹤完畢後： + +```cpp +if (mCurrentFrame.isSet()) + mpMapDrawer->SetCurrentCameraPose(mCurrentFrame.GetPose()); +``` + +`MapDrawer::SetCurrentCameraPose()` 內部將 `Tcw` 取逆存為 `mCameraPose = Tcw.inverse()`（即 `Twc`）。 + +參考：`src/Tracking.cc:2403-2407`, `src/MapDrawer.cc:441-445` + +### 9.2 `DrawCurrentCamera()` + +用 OpenGL 繪製一個梯形線框表示相機視錐體： +- 尺寸由 `Viewer.CameraSize` 控制（`w, h=0.75w, z=0.6w`） +- 顏色：綠色 `(0,1,0)` +- 在 `Twc` 的座標系下用 `glMultMatrix` 繪製 + +參考：`src/MapDrawer.cc:400-440` + +### 9.3 `DrawMapPoints()` + +- **一般地圖點**：黑色 `(0,0,0)` +- **Reference map points**（local map 裡的點）：紅色 `(1,0,0)` +- 點大小由 `Viewer.PointSize` 控制 + +### 9.4 `DrawKeyFrames()` + +- 每個 KF 以 `GetPoseInverse()` 取得 `Twc`，在該座標系繪製視錐體線框 +- 第一個 KF（無 parent）：紅色、加粗 5 倍線寬 +- 其他 KF：藍色 +- **Covisibility graph**（綠線）：連接 covisibility weight ≥ 100 的 KF 對 +- **Spanning tree**（綠線）：連接每個 KF 與其 parent +- **Loop edges**（綠線）：連接 loop closure 偵測到的 KF 對 + +參考：`src/MapDrawer.cc:170-400` + +--- + +## 10. 快速判讀：哪些幀是「純視覺」 + +看 `tracking_log.csv` 時可用以下準則： + +- `imu_initialized = no` +- `tracking_method` 不是 `IMU_MM`（值=2） +- `opt_type = 0`（TrackLocalMap 用 `PoseOptimization`） + +`tracking_method` 定義（`include/Tracking.h:367-389`）： + +| 值 | 含義 | +| :-: | :----------- | +| 0 | RefKF | +| 1 | MotionModel | +| 2 | IMU_MM | +| 3 | Relocalization | + +--- + +## 11. 一句話總結 + +純視覺軌跡 = 前端以視覺匹配（BoW 或投影）給初值 → `PoseOptimization`（4 輪 × 10 次 Levenberg-Marquardt + Huber outlier 篩選）refine → 每幀只存相對 reference KF 的 `Tcr` → 最後由 `System::SaveTrajectory*()` 沿 spanning tree 回推成完整世界軌跡 `Twc`。 diff --git a/study/trajectory_study_index.md b/study/trajectory_study_index.md new file mode 100644 index 00000000000..6747f9ef4f1 --- /dev/null +++ b/study/trajectory_study_index.md @@ -0,0 +1,91 @@ +# ORB-SLAM3 Trajectory Study Index + +## Purpose + +This index helps you read the two trajectory notes in a structured order: + +- `trajectory_pure_visual.md` +- `trajectory_visual_inertial.md` + +--- + +## Recommended Reading Order + +1. Read this file first (global map of the code paths). +2. Read `trajectory_pure_visual.md` to lock down the baseline front-end and export logic. +3. Read `trajectory_visual_inertial.md` to understand what changes after IMU is introduced. +4. Return to `src/Tracking.cc` and trace one frame end-to-end. + +--- + +## Big Picture (One Frame) + +```text +Sensor Input + -> Tracking::Track() + -> initial pose guess + - visual: RefKF / MotionModel + - VI: PredictStateIMU (when initialized and outside reset window) + -> TrackLocalMap() pose refinement + - visual: PoseOptimization + - VI: PoseOptimization or PoseInertialOptimization* (depends on state) + -> store relative pose to reference KF + - mlRelativeFramePoses / mlpReferences / mlFrameTimes / mlbLost + -> System::SaveTrajectory*() + -> reconstruct absolute trajectory from reference KFs + -> output Twc (visual) or Twb (VI branch in EuRoC export) +``` + +--- + +## Side-by-Side Comparison + +| Topic | Pure Visual | Visual-Inertial | +|---|---|---| +| Initial pose guess | RefKF / MotionModel | Can switch to `PredictStateIMU()` | +| Local pose refinement | `PoseOptimization` | `PoseOptimization` or `PoseInertialOptimizationLastFrame/LastKeyFrame` | +| State variables in optimization | pose only | pose + velocity + gyro bias + acc bias | +| Trajectory container | same 4 lists in `Tracking` | same 4 lists in `Tracking` | +| EuRoC export pose type | `Twc` branch | `Twb` branch | + +--- + +## Code Entry Points You Should Revisit + +- `src/Tracking.cc` + - `Track()` + - `TrackReferenceKeyFrame()` + - `TrackWithMotionModel()` + - `PredictStateIMU()` + - `TrackLocalMap()` + - `UpdateFrameIMU()` + +- `src/Optimizer.cc` + - `PoseOptimization()` + - `PoseInertialOptimizationLastKeyFrame()` + - `PoseInertialOptimizationLastFrame()` + +- `src/System.cc` + - `SaveTrajectoryTUM()` + - `SaveTrajectoryEuRoC()` + +- `src/LocalMapping.cc` + - `InitializeIMU()` + - `LocalInertialBA` / `LocalBundleAdjustment` branch + +--- + +## Practical Validation Workflow + +1. Run one pure visual sequence and one VI sequence. +2. Inspect `tracking_log.csv`: + - `tracking_method` + - `imu_initialized` + - (optionally re-enable) `opt_type`, `imu_predicted` +3. Compare generated trajectory files and confirm expected branch behavior. + +--- + +## Suggested Next Study Step + +Create a small per-frame tracer table (frame_id -> tracking_method -> opt_type -> output pose type) for one short sequence. That will make branch switching behavior obvious. diff --git a/study/trajectory_visual_inertial.md b/study/trajectory_visual_inertial.md new file mode 100644 index 00000000000..3ea70ea931a --- /dev/null +++ b/study/trajectory_visual_inertial.md @@ -0,0 +1,461 @@ +# ORB-SLAM3 軌跡研究（視覺 + 慣性） + +## 1. 研究範圍與模式 + +視覺慣性模式對應 `System::eSensor`： + +- `IMU_MONOCULAR=3` +- `IMU_STEREO=4` +- `IMU_RGBD=5` + +參考：`include/System.h:87-94` + +--- + +## 2. 整體骨架（先有共同流程，再進入 VI 分支） + +與純視覺相同，軌跡最終仍由這組資料結構重建： + +- `mlRelativeFramePoses`：每幀相對 reference KF 的 `Tcr` +- `mlpReferences`：每幀的 reference keyframe 指標 +- `mlFrameTimes`：每幀時間戳 +- `mlbLost`：每幀是否為 lost + +參考：`include/Tracking.h:154-159` + +也就是說：VI 並不是換一套軌跡保存容器，而是改變「每幀 pose 是怎麼估計出來」。 + +--- + +## 3. VI 前端估計：何時用 IMU + +主入口仍是 `Tracking::Track()` (`src/Tracking.cc:1963`)。 + +### 3.1 IMU 預積分準備 + +每幀進入追蹤前，若 sensor 為 VI 且非剛建立新地圖，會做 `PreintegrateIMU()`： + +```cpp +if ((mSensor == System::IMU_MONOCULAR || mSensor == System::IMU_STEREO || mSensor == System::IMU_RGBD) + && !mbCreatedMap) +{ + PreintegrateIMU(); +} +``` + +`PreintegrateIMU()` 做兩件事： +1. 從 `mlQueueImuData` 取出上一幀到當前幀之間的所有 IMU 量測 +2. 把它們積入 `mpImuPreintegratedFromLastKF`（KF→當前幀）和 `mCurrentFrame.mpImuPreintegratedFrame`（上一幀→當前幀） +3. 積分完後呼叫 `mCurrentFrame.setIntegrated()` + +參考：`src/Tracking.cc:2046-2055`, `src/Tracking.cc:1825-1903` + +### 3.2 Motion model 分支中的 IMU 條件 + +在 `TrackWithMotionModel()`： + +```cpp +if (mpAtlas->isImuInitialized() && (mCurrentFrame.mnId > mnLastRelocFrameId + mnFramesToResetIMU)) +{ + PredictStateIMU(); + return true; // 直接成功，不做視覺投影匹配 +} +``` + +若滿足： +- map 已 `isImuInitialized()` +- frame id 已超過重定位後 reset 視窗（`mnFramesToResetIMU`） + +就直接用 IMU 預測 pose 並返回 true（`tracking_method = IMU_MM = 2`），不走後面那段純視覺投影匹配初始化。 + +參考：`src/Tracking.cc:3119-3126` + +### 3.3 `PredictStateIMU()` 實際做什麼 + +用上次狀態 + 重力 + IMU 預積分，預測 `(Rwb, twb, Vwb)`，再轉成 frame 的 `Tcw`。 + +**map 已更新時**（`mbMapUpdated && mpLastKeyFrame`）→ 參照 last **keyframe**： + +```cpp +const Eigen::Vector3f twb1 = mpLastKeyFrame->GetImuPosition(); +const Eigen::Matrix3f Rwb1 = mpLastKeyFrame->GetImuRotation(); +const Eigen::Vector3f Vwb1 = mpLastKeyFrame->GetVelocity(); +const Eigen::Vector3f Gz(0, 0, -IMU::GRAVITY_VALUE); +const float t12 = mpImuPreintegratedFromLastKF->dT; + +Rwb2 = NormalizeRotation(Rwb1 * ΔR(bias)); +twb2 = twb1 + Vwb1*t12 + 0.5*t12²*Gz + Rwb1 * Δp(bias); +Vwb2 = Vwb1 + t12*Gz + Rwb1 * Δv(bias); +``` + +**map 未更新時**（`!mbMapUpdated`）→ 參照 last **frame**： +- 使用 `mCurrentFrame.mpImuPreintegratedFrame`（只覆蓋上一幀到當前幀） +- 公式同上但參考物改為 `mLastFrame` + +最後呼叫 `mCurrentFrame.SetImuPoseVelocity(Rwb2, twb2, Vwb2)` 把 body state 轉成 `Tcw`： + +```cpp +Sophus::SE3f Twb(Rwb, twb); +mTcw = mImuCalib.mTcb * Twb.inverse(); // Tcb * Tbw = Tcw +``` + +參考：`src/Tracking.cc:1915-1949`, `src/Frame.cc:483-492` + +### 3.4 RECENTLY_LOST 下的 IMU 行為 + +當 `mState == RECENTLY_LOST` 時： +- **VI 模式**：若 IMU 已初始化 → 用 `PredictStateIMU()` 繼續預測（不做重定位） + - 若超過 `time_recently_lost` 秒仍無法恢復 → 轉為 LOST + - 若 IMU 未初始化 → 直接 `bOK = false` +- **純視覺模式**：嘗試 `Relocalization()`（3 秒超時） + +這意味著 VI 模式可以在短暫丟失時純靠 IMU 慣性維持軌跡連續性。 + +參考：`src/Tracking.cc:2190-2220` + +--- + +## 4. VI 下的 pose refine（關鍵） + +`TrackLocalMap()` 會依條件切 3 種優化： + +### 4.1 選擇邏輯 + +```cpp +if (!mpAtlas->isImuInitialized()) +{ + Optimizer::PoseOptimization(&mCurrentFrame); // opt_type = 0 +} +else +{ + if (mCurrentFrame.mnId <= mnLastRelocFrameId + mnFramesToResetIMU) + { + Optimizer::PoseOptimization(&mCurrentFrame); // opt_type = 0（重定位 reset 視窗內） + } + else + { + if (!mbMapUpdated) + inliers = Optimizer::PoseInertialOptimizationLastFrame(&mCurrentFrame); // opt_type = 1 + else + inliers = Optimizer::PoseInertialOptimizationLastKeyFrame(&mCurrentFrame); // opt_type = 2 + } +} +``` + +| opt_type | 函式 | 觸發條件 | +| :------: | :--------------------------------------- | :------------------------------------- | +| 0 | `PoseOptimization` | IMU 未初始化，或重定位 reset 視窗內 | +| 1 | `PoseInertialOptimizationLastFrame` | IMU 已初始化 + 視窗外 + map 未更新 | +| 2 | `PoseInertialOptimizationLastKeyFrame` | IMU 已初始化 + 視窗外 + map 已更新 | + +參考：`src/Tracking.cc:3235-3261` + +### 4.2 成功判定（VI 模式） + +| 感測器模式 | 門檻 | +| :------------------- | :---------------------------------------------------- | +| IMU_MONOCULAR（已初始化） | < 15 inliers → 失敗；< 50（未初始化）→ 失敗 | +| IMU_STEREO / IMU_RGBD | < 15 inliers → 失敗 | +| 重定位後 mMaxFrames 幀內 | < 50 inliers → 失敗 | +| RECENTLY_LOST 恢復 | ≥ 10 inliers → 成功 | + +參考：`src/Tracking.cc:3370-3410` + +--- + +## 5. VI 優化圖模型長什麼樣 + +### 5.1 `PoseInertialOptimizationLastKeyFrame` + +參考：`src/Optimizer.cc:4491-4735` + +**求解器**：`BlockSolverX` + `LinearSolverDense` + **Gauss-Newton**（非 Levenberg-Marquardt） + +**Vertex（8 個）**： + +| id | 類型 | 優化/固定 | 說明 | +| :-: | :---------------- | :-------- | :------------------ | +| 0 | `VertexPose` | 優化 | 當前 frame pose | +| 1 | `VertexVelocity` | 優化 | 當前 frame 速度 | +| 2 | `VertexGyroBias` | 優化 | 當前 frame 陀螺偏置 | +| 3 | `VertexAccBias` | 優化 | 當前 frame 加速偏置 | +| 4 | `VertexPose` | **固定** | 上一個 KF pose | +| 5 | `VertexVelocity` | **固定** | 上一個 KF 速度 | +| 6 | `VertexGyroBias` | **固定** | 上一個 KF 陀螺偏置 | +| 7 | `VertexAccBias` | **固定** | 上一個 KF 加速偏置 | + +**Edge（慣性部分）**： + +| 邊類型 | 連接 | Information | +| :----------------- | :------------------------------------- | :----------------------------------- | +| `EdgeInertial` | VPk→VVk→VGk→VAk→VP→VV | 預積分噪聲協方差的逆 | +| `EdgeGyroRW` | VGk→VG | `C[9:12,9:12]⁻¹`（陀螺隨機遊走） | +| `EdgeAccRW` | VAk→VA | `C[12:15,12:15]⁻¹`（加速隨機遊走） | + +**Edge（視覺部分）**： +- `EdgeMonoOnlyPose`（mono 觀測，2D） +- `EdgeStereoOnlyPose`（stereo 觀測，3D） +- Huber robust kernel：mono `δ=√5.991`，stereo `δ=√7.815` +- Information matrix 考慮了 `mpCamera->uncertainty2(obs)` 的校正 + +**迭代策略**：4 輪 × 10 次，χ² 門檻逐輪收緊： + +```cpp +float chi2Mono[4] = {12, 7.5, 5.991, 5.991}; +float chi2Stereo[4] = {15.6, 9.8, 7.815, 7.815}; +``` + +且距離 < 10m 的 close point 門檻放寬 1.5 倍（`chi2close = 1.5 * chi2Mono[it]`）。 + +第 3 輪（it==2）後移除 Robust Kernel。 + +### 5.2 `PoseInertialOptimizationLastFrame` + +參考：`src/Optimizer.cc:5036-5108` + +與 LastKeyFrame 版本的**關鍵差異**： + +1. **Previous vertex 不固定**：上一個 **frame**（非 KF）的 pose/velocity/bias 也被優化（id=4~7，`setFixed(false)`） +2. **使用 frame-to-frame 預積分**：`pFrame->mpImuPreintegratedFrame`（而非 KF→frame） +3. **額外加入先驗邊**： + ```cpp + EdgePriorPoseImu* ep = new EdgePriorPoseImu(pFp->mpcpi); + ``` + - 連接上一個 frame 的 4 個 vertex + - 用 Huber kernel `δ=5` + - `mpcpi` 是 previous frame 保存的 constrained prior info（`ConstrainedPoseImu`），包含已優化的 state 與 information matrix + +4. **χ² 門檻**：mono 不逐輪收緊（4 輪都是 `5.991`），stereo 逐輪收緊（`{15.6, 9.8, 7.815, 7.815}`） + +這讓連續幀下的 VI 解更穩定，因為有上一幀的先驗約束。 + +### 5.3 兩個 VI 優化的回傳值 + +回傳 `nInitialCorrespondences - nBad`（inlier 數量），同時已更新 frame 的： +- pose（`VP->estimate()`） +- velocity（`VV->estimate()`） +- gyro bias（`VG->estimate()`） +- acc bias（`VA->estimate()`） + +若 inlier < 30 且非 `bRecInit`，會用放寬門檻（mono=18, stereo=24）再做一次 outlier 判定。 + +--- + +## 6. IMU 初始化對軌跡的影響（LocalMapping） + +`LocalMapping` 會負責 IMU 初始化與慣性 BA 階段切換。 + +### 6.1 初始化觸發條件 + +```cpp +if (!mpCurrentKeyFrame->GetMap()->isImuInitialized() && mbInertial) +{ + if (mbMonocular) + InitializeIMU(1e2, 1e10, true); // mono：先驗極弱 + else + InitializeIMU(1e2, 1e5, true); // stereo/RGBD：先驗較強 +} +``` + +參考：`src/LocalMapping.cc:214-221` + +### 6.2 `InitializeIMU()` 流程 + +1. **前置條件**：KF 數量 ≥ `nMinKF`（10），且時間跨度 ≥ `minTime`（mono=2s, stereo=1s） +2. **估計重力方向 `Rwg`**： + - 累積所有 KF 的 `R_prev * ΔV` → 得到重力方向 `dirG` + - 用 `gI=(0,0,-1)` 與 `dirG` 的叉積算旋轉 `Rwg = SO3::exp(vzg)` +3. **估計初始速度**：`v_k = (p_{k+1} - p_k) / ΔT` +4. **`InertialOptimization`**：聯合優化 `Rwg`、scale `s`、gyro bias `bg`、acc bias `ba` +5. **若 `scale < 0.1` → 初始化失敗，直接返回** +6. **套用尺度與旋轉**： + ```cpp + Sophus::SE3f Twg(mRwg.transpose(), Vector3f::Zero()); + mpAtlas->GetCurrentMap()->ApplyScaledRotation(Twg, mScale, true); + mpTracker->UpdateFrameIMU(mScale, vpKF[0]->GetImuBias(), mpCurrentKeyFrame); + ``` +7. **標記完成**：`mpAtlas->SetImuInitialized()` +8. **FullInertialBA**：做完整的慣性 BA（100 次迭代） + +參考：`src/LocalMapping.cc:1268-1405` + +### 6.3 局部 BA 的分支 + +IMU 初始化後，`LocalMapping` 的局部 BA 從 `LocalBundleAdjustment` 切換為 `LocalInertialBA`： + +```cpp +if (mbInertial && mpCurrentKeyFrame->GetMap()->isImuInitialized()) +{ + Optimizer::LocalInertialBA(mpCurrentKeyFrame, &mbAbortBA, pMap, ...); +} +else +{ + Optimizer::LocalBundleAdjustment(mpCurrentKeyFrame, &mbAbortBA, pMap, ...); +} +``` + +參考：`src/LocalMapping.cc:156-177` + +--- + +## 7. `UpdateFrameIMU()` 為什麼和軌跡研究有關 + +`UpdateFrameIMU(s, b, pCurrentKeyFrame)` 在 IMU 初始化/重估尺度後被呼叫，它做三件事： + +### 7.1 歷史 frame 相對位姿重標定 + +```cpp +for (auto lit = mlRelativeFramePoses.begin(); ...; lit++) +{ + if (*lbL) continue; // 跳過 lost 幀 + KeyFrame* pKF = *lRit; + while (pKF->isBad()) pKF = pKF->GetParent(); + if (pKF->GetMap() == pMap) + (*lit).translation() *= s; // 只縮放平移部分 +} +``` + +這代表：VI 初始化/重估尺度後，歷史 frame 相對位姿會被重標定，而不是只改當前幀。 + +### 7.2 更新 bias + +```cpp +mLastBias = b; +mLastFrame.SetNewBias(mLastBias); +mCurrentFrame.SetNewBias(mLastBias); +``` + +### 7.3 重設當前/上一幀的 IMU pose/velocity + +- 若 `mLastFrame` 就是 last KF 同一幀 → 直接用 KF 的 IMU state +- 否則 → 從 last KF 的 state 透過預積分推算 +- `mCurrentFrame` 也用相同方式從其 `mpLastKeyFrame` 推算 + +參考：`src/Tracking.cc:4268-4332` + +--- + +## 8. frame pose 與 body pose 的關係 + +`Frame::SetImuPoseVelocity(Rwb, twb, Vwb)` 會做： + +```cpp +mVw = Vwb; +Sophus::SE3f Twb(Rwb, twb); +Sophus::SE3f Tbw = Twb.inverse(); +mTcw = mImuCalib.mTcb * Tbw; // Tcb * Tbw = Tcw +``` + +參考：`src/Frame.cc:483-492` + +因此 tracking 內主狀態仍是 `Tcw`（相機位姿），但它可由 IMU 狀態透過 extrinsic `Tcb` 推導而來。反過來，要從 `Tcw` 回推 body pose： + +```cpp +Twb = (Tcb * Tcw)^-1 = Twc * Tbc +``` + +--- + +## 9. 軌跡輸出：VI 與純視覺的關鍵差異 + +### 9.1 共同點 + +輸出時都會用： + +- `mlRelativeFramePoses` + reference KF + spanning tree 回推 + +參考：`src/System.cc:715-767`, `820-872` + +### 9.2 `SaveTrajectoryEuRoC()` 差異 + +**原點 offset**： +```cpp +if (mSensor == IMU_MONOCULAR || mSensor == IMU_STEREO || mSensor == IMU_RGBD) + Twb = vpKFs[0]->GetImuPose(); // body frame 原點 +else + Twb = vpKFs[0]->GetPoseInverse(); // camera frame 原點 +``` + +**VI 模式輸出 `Twb`（body）**： +```cpp +Sophus::SE3f Twb = (pKF->mImuCalib.mTbc * (*lit) * Trw).inverse(); +// Tbc * Tcr * Trw = Tbw → 取逆 = Twb +f << 1e9*(*lT) << " " << twb << " " << q << endl; +``` + +**純視覺輸出 `Twc`（camera）**： +```cpp +Sophus::SE3f Twc = ((*lit) * Trw).inverse(); +// Tcr * Trw = Tcw → 取逆 = Twc +``` + +參考：`src/System.cc:770-783` + +也就是說，研究 VI 軌跡時要先確認你看的檔案是 body 還是 camera 參考座標。 + +### 9.3 `SaveTrajectoryTUM()` 的情況 + +`SaveTrajectoryTUM()` **不區分 VI/純視覺**，一律輸出 `Twc`（camera pose），不做 body 轉換。所以若想比較 body 軌跡，應使用 EuRoC 格式。 + +參考：`src/System.cc:602-651` + +--- + +## 10. 怎麼判斷某一幀主要走 VI 還是純視覺 + +看 `tracking_log.csv`（欄位定義在 `include/Tracking.h:356-419`）： + +1. `imu_initialized = yes` 才可能走慣性分支 +2. `tracking_method`： + - `0` = RefKF（純視覺） + - `1` = MotionModel（純視覺恆速模型） + - `2` = IMU_MM（IMU 預測，**不做視覺投影匹配初始化**） + - `3` = Relocalization +3. `opt_type`： + - `0` = `PoseOptimization`（純視覺優化） + - `1` = `PoseInertialOptimizationLastFrame`（VI，參照上一 frame） + - `2` = `PoseInertialOptimizationLastKeyFrame`（VI，參照上一 KF） +4. `imu_predicted`：是否有用 IMU 做初始 pose 預測 + +典型 VI 幀的 log 特徵：`tracking_method=2, imu_initialized=yes, opt_type=1 或 2, imu_predicted=true` + +--- + +## 11. VI 模式下的特殊機制 + +### 11.1 Bad IMU 偵測 + +若 `LocalMapping` 偵測到 IMU 異常（`mbBadImu = true`），`Track()` 開頭會直接重設地圖： + +```cpp +if (mpLocalMapper->mbBadImu) { + mpSystem->ResetActiveMap(); + return; +} +``` + +參考：`src/Tracking.cc:1980-1984` + +### 11.2 時間戳跳躍處理 + +若當前幀時間戳比上一幀大超過 1 秒： +- IMU 已初始化且完成 InertialBA2 → `CreateMapInAtlas()`（建新地圖） +- IMU 已初始化但未完成 → `ResetActiveMap()` +- IMU 未初始化 → `ResetActiveMap()` + +參考：`src/Tracking.cc:2010-2040` + +### 11.3 重定位後的 IMU reset 視窗 + +重定位成功後的 `mnFramesToResetIMU` 幀內： +- 前端仍使用純視覺 `PoseOptimization`（不走 VI 優化） +- 前 2 幀強制用 `TrackReferenceKeyFrame` +- 視窗結束時呼叫 `ResetFrameIMU()` 重置 IMU 狀態 + +參考：`src/Tracking.cc:2370-2395` + +--- + +## 12. 一句話總結 + +VI 軌跡不是「視覺與 IMU 二選一」，而是以視覺觀測為量測主體，IMU 透過預積分與偏置狀態把時間連續性和動力學先驗注入同一個 pose 優化裡；初始化時還會回溯校正歷史 frame 的尺度。 diff --git a/voxel/pixel_change.py b/voxel/pixel_change.py new file mode 100644 index 00000000000..5901deb5040 --- /dev/null +++ b/voxel/pixel_change.py @@ -0,0 +1,92 @@ +import open3d as o3d +import numpy as np +import os +from pathlib import Path + + # 必須要有這一行！ +SCRIPT_DIR = Path(__file__).parent.resolve() +# ========================================== +# 檔案路徑設定 (對應你的電腦環境) +# ========================================== +INPUT_PLY = str(SCRIPT_DIR / "stereo_imu_tum_room1_mappoints.txt") # 這是你從 ORB-SLAM3 產生的點雲檔案 +OUTPUT_TXT = str(SCRIPT_DIR / "stereo_imu_tum_room1_mappoints_fix.txt") + +print(f"Reading from: {INPUT_PLY}") +print(f"Writing to: {OUTPUT_TXT}") + + +# Voxel 的大小 (方塊大小) +# 如果跑出來方塊太小或太大，可以調整這個數字 (例如 0.05 或 0.1) +VOXEL_SIZE = 0.05 + +# 測試模式：True = 所有點一律設為白色 (255,255,255)，忽略原始灰階值 +FORCE_WHITE = True + +def main(): + if not os.path.exists(INPUT_PLY): + print(f"找不到點雲檔案：{INPUT_PLY}") + return + + print(f"正在讀取點雲：{INPUT_PLY} ...") + + points_list = [] + colors_list = [] + with open(INPUT_PLY, 'r') as f: + for line in f: + line = line.strip() + if not line: + continue + vals = [float(v) for v in line.replace(',', ' ').split()] + if len(vals) < 3: + continue + points_list.append(vals[:3]) + if len(vals) >= 6: + # XYZ + RGB (0-255) + colors_list.append([vals[3]/255.0, vals[4]/255.0, vals[5]/255.0]) + elif len(vals) == 4: + # XYZ + Grayscale (0-255) + g = 1.0 if FORCE_WHITE else vals[3] / 255.0 + colors_list.append([g, g, g]) + else: + colors_list.append([1.0, 1.0, 1.0]) + + if not points_list: + print("找不到任何有效點，請確認檔案格式。") + return + + pcd = o3d.geometry.PointCloud() + pcd.points = o3d.utility.Vector3dVector(np.array(points_list)) + pcd.colors = o3d.utility.Vector3dVector(np.array(colors_list)) + print(f"原始點雲共有 {len(pcd.points)} 個點。") + + # ========================================== + # 核心：將點雲「體素化 (Voxel Downsampling)」 + # ========================================== + print(f"正在進行 Voxel 轉換 (Voxel Size = {VOXEL_SIZE})...") + voxel_pcd = pcd.voxel_down_sample(voxel_size=VOXEL_SIZE) + print(f"體素化完成！剩下 {len(voxel_pcd.points)} 個 Voxel。") + + points = np.asarray(voxel_pcd.points) + + # ORB-SLAM2 原本沒有存顏色，所以我們預設給它白色 (255, 255, 255) + # 如果未來你有帶顏色的點雲，這段程式碼也會自動去抓顏色 + if len(voxel_pcd.colors) > 0: + colors = np.asarray(voxel_pcd.colors) * 255.0 + else: + colors = np.ones_like(points) * 255.0 + + # ========================================== + # 寫出符合你 C++ 程式碼讀取的 TXT 格式 + # ========================================== + print(f"正在寫入 TXT 檔案：{OUTPUT_TXT} ...") + with open(OUTPUT_TXT, 'w') as f: + for i in range(len(points)): + x, y, z = points[i] + r, g, b = colors[i] + # 你的 C++ 需要的格式：X Y Z R G B (用空白隔開) + f.write(f"{x:.6f} {y:.6f} {z:.6f} {int(r)} {int(g)} {int(b)}\n") + + print("現在可以去跑你的 C++ OpenGL 程式了！") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/voxel/voxel.cpp b/voxel/voxel.cpp new file mode 100644 index 00000000000..42b45843e3e --- /dev/null +++ b/voxel/voxel.cpp @@ -0,0 +1,538 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#define scalefactor 0.05 +#define VOXEL_SIZE 0.05f +#define FORCE_WHITE true + + +using namespace std; + + + + +struct XYZ_RGB_t { + unsigned char R, G, B; + float X = 0, Y = 0, Z = 0; +}; +vector volume_t; +int old_rot_x = 0; +int old_rot_y = 0; + +int rot_x = 0; +int rot_y = 0; + +int record_x = 0; +int record_y = 0; + +float x_shift = 0; +float y_shift = 0; +float z_shift = 0; + +long long getSystemTime() { + struct timeb t; + ftime(&t); + return 1000 * t.time + t.millitm; +} + +void Display() +{ + glEnable(GL_DEPTH_TEST); + + glClearColor(0, 0, 0, 1.0); + glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT); + glPolygonMode(GL_BACK, GL_LINE); + + glMatrixMode(GL_MODELVIEW); + glLoadIdentity(); + + gluLookAt(0, 0, 20, 0, 0, 0, 0, 1, 0); + + glTranslatef(x_shift, y_shift, z_shift); + glRotatef((float)rot_y + (float)record_y, 1.0, 0.0, 0.0); + glRotatef((float)rot_x + (float)record_x, 0.0, 1.0, 0.0); + + float scale = scalefactor; + float x, y, z;// + float R, G, B;//color + for (int n = 0; n < volume_t.size(); n++) { //data num + glBegin(GL_QUADS); + R = volume_t[n].R / 255.0, G = volume_t[n].G / 255.0, B = volume_t[n].B / 255.0; + //printf("%.2f %.2f %.2f \n", R,G,B); + glColor3f(R, G, B);//normalize + x = volume_t[n].X; + y = -volume_t[n].Y; + z = -volume_t[n].Z; + + // Voxel //方塊有6個面，每個面有四個點 + glVertex3f(x + (-0.5 * scale), y + (0.5 * scale), z + (0.5 * scale));/*點1*/ glVertex3f(x + (-0.5 * scale), y + (-0.5 * scale), z + (0.5 * scale));/*點2*/ glVertex3f(x + (0.5 * scale), y + (-0.5 * scale), z + (0.5 * scale));/*點3*/ + glVertex3f(x + (0.5 * scale), y + (0.5 * scale), z + (0.5 * scale));/*點4*/ + + glVertex3f(x + (0.5 * scale), y + (0.5 * scale), z + (-0.5 * scale)); glVertex3f(x + (0.5 * scale), y + (-0.5 * scale), z + (-0.5 * scale)); glVertex3f(x + (-0.5 * scale), y + (-0.5 * scale), z + (-0.5 * scale)); + glVertex3f(x + (-0.5 * scale), y + (0.5 * scale), z + (-0.5 * scale)); + + glVertex3f(x + (0.5 * scale), y + (0.5 * scale), z + (0.5 * scale)); glVertex3f(x + (0.5 * scale), y + (-0.5 * scale), z + (0.5 * scale)); glVertex3f(x + (0.5 * scale), y + (-0.5 * scale), z + (-0.5 * scale)); + glVertex3f(x + (0.5 * scale), y + (0.5 * scale), z + (-0.5 * scale)); + + glVertex3f(x + (-0.5 * scale), y + (0.5 * scale), z + (-0.5 * scale)); glVertex3f(x + (-0.5 * scale), y + (-0.5 * scale), z + (-0.5 * scale)); glVertex3f(x + (-0.5 * scale), y + (-0.5 * scale), z + (0.5 * scale)); + glVertex3f(x + (-0.5 * scale), y + (0.5 * scale), z + (0.5 * scale)); + + glVertex3f(x + (-0.5 * scale), y + (0.5 * scale), z + (-0.5 * scale)); glVertex3f(x + (-0.5 * scale), y + (0.5 * scale), z + (0.5 * scale)); glVertex3f(x + (0.5 * scale), y + (0.5 * scale), z + (0.5 * scale)); + glVertex3f(x + (0.5 * scale), y + (0.5 * scale), z + (-0.5 * scale)); + + glVertex3f(x + (-0.5 * scale), y + (-0.5 * scale), z + (0.5 * scale)); glVertex3f(x + (-0.5 * scale), y + (-0.5 * scale), z + (-0.5 * scale)); glVertex3f(x + (0.5 * scale), y + (-0.5 * scale), z + (-0.5 * scale)); + glVertex3f(x + (0.5 * scale), y + (-0.5 * scale), z + (0.5 * scale)); + + glEnd(); + + + + } + + glutSwapBuffers(); +} + + +void Keyboard(unsigned char key, int x, int y) +{ + printf("你所按按鍵的碼是%x\t此時視窗內的滑鼠座標是(%d,%d)\n", key, x, y); + switch (key) + { + case 'w': + y_shift += 1.0f; + break; + case 's': + y_shift -= 1.0f; + break; + case 'd': + x_shift += 1.0f; + break; + case 'a': + x_shift -= 1.0f; + break; + case 'q': + z_shift += 1.0f; + break; + case 'e': + z_shift -= 1.0f; + break; + case 'z': + break; + case 27: + + exit(0); + break; + } + glutPostRedisplay(); +} + +void Mouse(int button, int state, int x, int y) +{ + if (state) { + record_x += x - old_rot_x; + record_y += y - old_rot_y; + + rot_x = 0; + rot_y = 0; + } + else { + old_rot_x = x; + old_rot_y = y; + } +} + +void MotionMouse(int x, int y) +{ + rot_x = x - old_rot_x; + rot_y = y - old_rot_y; + glutPostRedisplay(); +} + +void WindowSize(int w, int h) +{ + printf("目前視窗大小為%dX%d\n", w, h); + glViewport(0, 0, w, h); //當視窗長寬改變時，畫面也跟著變 + glMatrixMode(GL_PROJECTION); + glLoadIdentity(); + float rate = (float)w / (float)h; + gluPerspective(45, rate, 1.0, 74000.0); + glMatrixMode(GL_MODELVIEW); + glLoadIdentity(); +} + +struct Point2i { + int x, y; +}; + + + +int main(int argc, char* argv[]) +{ + if (argc < 2) { + cerr << "用法: " << argv[0] << " <點雲檔案路徑>" << endl; + cerr << "例如: " << argv[0] << " output_points_int_LR.txt" << endl; + return 1; + } + string input_path = argv[1]; + + + + // --- 讀取原始點雲 --- + vector raw_points; + XYZ_RGB_t data; + ifstream infile2(input_path); + if (!infile2.is_open()) { + cerr << "[Error] 無法開啟檔案: " << input_path << endl; + return 1; + } + string line; + bool has_rgb = false; + bool has_gray = false; + bool format_detected = false; + while (getline(infile2, line)) { + if (line.empty()) continue; + replace(line.begin(), line.end(), ',', ' '); + istringstream iss(line); + float v[6]; + int count = 0; + while (count < 6 && iss >> v[count]) count++; + if (count < 3) continue; + if (!format_detected) { + has_rgb = (count >= 6); + has_gray = (count == 4); + format_detected = true; + if (has_rgb) + cout << "偵測到格式: XYZ + RGB" << endl; + else if (has_gray) + cout << "偵測到格式: XYZ + 灰階" << endl; + else + cout << "偵測到格式: 僅 XYZ，顏色預設灰色" << endl; + } + data.X = v[0]; data.Y = v[1]; data.Z = v[2]; + if (has_rgb) { + data.R = (unsigned char)v[3]; data.G = (unsigned char)v[4]; data.B = (unsigned char)v[5]; + } else if (has_gray) { + unsigned char g = FORCE_WHITE ? 255 : (unsigned char)v[3]; + data.R = g; data.G = g; data.B = g; + } else { + data.R = 200; data.G = 200; data.B = 200; + } + raw_points.push_back(data); + } + infile2.close(); + cout << "讀取完成，共 " << raw_points.size() << " 個原始點。" << endl; + + // --- Voxel Downsampling --- + cout << "正在進行 Voxel 降採樣 (VOXEL_SIZE = " << VOXEL_SIZE << ")..." << endl; + struct VoxelKey { + int ix, iy, iz; + bool operator==(const VoxelKey& o) const { + return ix == o.ix && iy == o.iy && iz == o.iz; + } + }; + struct VoxelKeyHash { + size_t operator()(const VoxelKey& k) const { + size_t h = 0; + h ^= hash()(k.ix) + 0x9e3779b9 + (h << 6) + (h >> 2); + h ^= hash()(k.iy) + 0x9e3779b9 + (h << 6) + (h >> 2); + h ^= hash()(k.iz) + 0x9e3779b9 + (h << 6) + (h >> 2); + return h; + } + }; + struct VoxelAccum { + double sx=0, sy=0, sz=0, sr=0, sg=0, sb=0; + int count=0; + }; + unordered_map voxel_map; + for (auto& pt : raw_points) { + VoxelKey key; + key.ix = (int)floor(pt.X / VOXEL_SIZE); + key.iy = (int)floor(pt.Y / VOXEL_SIZE); + key.iz = (int)floor(pt.Z / VOXEL_SIZE); + auto& acc = voxel_map[key]; + acc.sx += pt.X; acc.sy += pt.Y; acc.sz += pt.Z; + acc.sr += pt.R; acc.sg += pt.G; acc.sb += pt.B; + acc.count++; + } + for (auto& kv : voxel_map) { + auto& acc = kv.second; + XYZ_RGB_t p; + p.X = (float)(acc.sx / acc.count); + p.Y = (float)(acc.sy / acc.count); + p.Z = (float)(acc.sz / acc.count); + p.R = (unsigned char)(acc.sr / acc.count); + p.G = (unsigned char)(acc.sg / acc.count); + p.B = (unsigned char)(acc.sb / acc.count); + volume_t.push_back(p); + } + cout << "降採樣完成，剩下 " << volume_t.size() << " 個 Voxel。" << endl; + + + + + + + + + + + glutInit(&argc, argv);// + glutInitDisplayMode(GLUT_RGB | GLUT_DOUBLE | GLUT_DEPTH);//single is 2D + + glutInitWindowPosition(250, 50); + glutInitWindowSize(800, 800);//size window + glutCreateWindow("OpenGL cube");// + glutReshapeFunc(WindowSize); + glutKeyboardFunc(Keyboard); + glutMouseFunc(Mouse); + glutMotionFunc(MotionMouse); + + glutDisplayFunc(Display); + glutMainLoop(); + return 0; +} + + +//#include // 請確保已安裝 FreeGLUT +//#include +//#include +//#include +//#include +// +//using namespace std; +// +//// -------------------------------------------------------- +//// 參數設定 +//// -------------------------------------------------------- +//// 如果點雲太稀疏或太大，可以調整這個縮放因子 +//#define SCALE_FACTOR 1.0 +//// 每個 Voxel (立方體) 的大小 +//#define VOXEL_SIZE 10.0f +// +//// 輸入檔案路徑 (請修改為您的檔案路徑) +//const string INPUT_TXT_PATH = "D:/code/2D_to_3D_coordinates/2D_to_3D_coordinates/output_points_int.txt"; +// +//// -------------------------------------------------------- +//// 資料結構 +//// -------------------------------------------------------- +//struct VoxelPoint { +// int X, Y, Z; // 3D 座標 (mm) +// unsigned char R, G, B; // 顏色 (0-255) +//}; +// +//// 全域變數：儲存所有點雲資料 +//vector voxelData; +// +//// 視角控制變數 +//int old_rot_x = 0; +//int old_rot_y = 0; +//int rot_x = 0; +//int rot_y = 0; +//int record_x = 0; +//int record_y = 0; +// +//float x_shift = 0; +//float y_shift = 0; +//float z_shift = 0; // 負責縮放/前後移動 +// +//// -------------------------------------------------------- +//// 繪圖函式 (Display) +//// -------------------------------------------------------- +//void Display() +//{ +// // 開啟深度測試，確保前後遮擋關係正確 +// glEnable(GL_DEPTH_TEST); +// +// // 清除顏色緩衝與深度緩衝 +// glClearColor(1.0f, 1.0f, 1.0f, 1.0f); // 背景設為深灰色，比較好看清楚點雲 +// glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT); +// +// glMatrixMode(GL_MODELVIEW); +// glLoadIdentity(); +// +// // 設定攝影機位置 (眼睛位置, 看向哪裡, 頭頂朝向) +// // 預設將相機拉遠一點 (Z=-2000)，以免一開始就在物體內部 +// gluLookAt(0, 0, 0, 0, 0, 1000, 0, 1, 0); +// +// // 應用使用者的移動與旋轉 +// glTranslatef(x_shift, y_shift, z_shift); // 平移 +// glRotatef((float)rot_y + (float)record_y, 1.0, 0.0, 0.0); // 繞 X 軸旋轉 +// glRotatef((float)rot_x + (float)record_x, 0.0, 1.0, 0.0); // 繞 Y 軸旋轉 +// +// // 開始繪製 Voxel +// float s = VOXEL_SIZE * SCALE_FACTOR / 2.0f; // 半徑 +// //float s = 0.5f * SCALE_FACTOR; +// // 為了效能，使用 GL_QUADS 繪製所有方塊 +// glBegin(GL_QUADS); +// +// for (const auto& p : voxelData) { +// // 設定顏色 +// glColor3ub(p.R, p.G, p.B); +// +// // 座標轉換 +// // 注意：OpenGL 的 Y 軸通常朝上，而影像座標 Y 朝下 +// // 這裡加負號 (-p.Y) 是為了讓畫面轉正 +// float x = -p.X * SCALE_FACTOR; +// float y = -p.Y * SCALE_FACTOR; +// float z = p.Z * SCALE_FACTOR; +// +// // 繪製立方體的六個面 +// // 前面 +// glVertex3f(x - s, y + s, z + s); glVertex3f(x - s, y - s, z + s); +// glVertex3f(x + s, y - s, z + s); glVertex3f(x + s, y + s, z + s); +// // 後面 +// glVertex3f(x + s, y + s, z - s); glVertex3f(x + s, y - s, z - s); +// glVertex3f(x - s, y - s, z - s); glVertex3f(x - s, y + s, z - s); +// // 左面 +// glVertex3f(x - s, y + s, z - s); glVertex3f(x - s, y - s, z - s); +// glVertex3f(x - s, y - s, z + s); glVertex3f(x - s, y + s, z + s); +// // 右面 +// glVertex3f(x + s, y + s, z + s); glVertex3f(x + s, y - s, z + s); +// glVertex3f(x + s, y - s, z - s); glVertex3f(x + s, y + s, z - s); +// // 上面 +// glVertex3f(x - s, y + s, z - s); glVertex3f(x - s, y + s, z + s); +// glVertex3f(x + s, y + s, z + s); glVertex3f(x + s, y + s, z - s); +// // 下面 +// glVertex3f(x - s, y - s, z + s); glVertex3f(x - s, y - s, z - s); +// glVertex3f(x + s, y - s, z - s); glVertex3f(x + s, y - s, z + s); +// } +// glEnd(); +// +// glutSwapBuffers(); +//} +// +//// -------------------------------------------------------- +//// 鍵盤控制 (Keyboard) +//// W/S: 上下移動, A/D: 左右移動, Q/E: 前後縮放 +//// -------------------------------------------------------- +//void Keyboard(unsigned char key, int x, int y) +//{ +// float speed = 50.0f * SCALE_FACTOR; // 移動速度 +// +// switch (key) +// { +// case 'w': y_shift += speed; break; +// case 's': y_shift -= speed; break; +// case 'd': x_shift += speed; break; +// case 'a': x_shift -= speed; break; +// case 'q': z_shift += speed; break; // 拉近 (或推遠，視座標系而定) +// case 'e': z_shift -= speed; break; // 推遠 +// case 27: exit(0); break; // ESC 鍵離開 +// } +// glutPostRedisplay(); +//} +// +//// -------------------------------------------------------- +//// 滑鼠點擊 (Mouse Click) +//// -------------------------------------------------------- +//void Mouse(int button, int state, int x, int y) +//{ +// if (state == GLUT_DOWN) { +// old_rot_x = x; +// old_rot_y = y; +// } +// else if (state == GLUT_UP) { +// record_x += x - old_rot_x; +// record_y += y - old_rot_y; +// rot_x = 0; +// rot_y = 0; +// } +//} +// +//// -------------------------------------------------------- +//// 滑鼠拖曳 (Mouse Motion) -> 旋轉視角 +//// -------------------------------------------------------- +//void MotionMouse(int x, int y) +//{ +// rot_x = x - old_rot_x; +// rot_y = y - old_rot_y; +// glutPostRedisplay(); +//} +// +//// -------------------------------------------------------- +//// 視窗大小改變 (Window Reshape) +//// -------------------------------------------------------- +//void WindowSize(int w, int h) +//{ +// if (h == 0) h = 1; +// glViewport(0, 0, w, h); +// +// glMatrixMode(GL_PROJECTION); +// glLoadIdentity(); +// +// float aspect = (float)w / (float)h; +// // 設定透視投影：視角 45 度, 最近 1.0, 最遠 100000.0 (確保看的到遠處的點) +// gluPerspective(45, aspect, 1.0, 100000.0); +// +// glMatrixMode(GL_MODELVIEW); +// glLoadIdentity(); +//} +// +//// -------------------------------------------------------- +//// 主程式 (Main) +//// -------------------------------------------------------- +//int main(int argc, char* argv[]) +//{ +// // 1. 讀取 TXT 檔案 +// cout << "[Info] 正在讀取檔案: " << INPUT_TXT_PATH << " ..." << endl; +// +// ifstream fin(INPUT_TXT_PATH); +// if (!fin.is_open()) { +// cerr << "[Error] 無法開啟檔案！請檢查路徑是否正確。" << endl; +// system("pause"); +// return -1; +// } +// +// int x, y, z, r, g, b; +// // 假設檔案格式為: X Y Z R G B (中間用空白分隔) +// while (fin >> x >> y >> z >> r >> g >> b) { +// VoxelPoint p; +// p.X = x; +// p.Y = y; +// p.Z = z; +// p.R = (unsigned char)r; +// p.G = (unsigned char)g; +// p.B = (unsigned char)b; +// voxelData.push_back(p); +// } +// fin.close(); +// +// cout << "[Success] 讀取完成！共 " << voxelData.size() << " 個點。" << endl; +// cout << "---------------------------------------------" << endl; +// cout << "操作說明：" << endl; +// cout << " [滑鼠左鍵拖曳] 旋轉視角" << endl; +// cout << " [W / S] 上下移動" << endl; +// cout << " [A / D] 左右移動" << endl; +// cout << " [Q / E] 前後移動 (Zoom)" << endl; +// cout << " [ESC] 離開程式" << endl; +// +// // 2. 初始化 OpenGL / GLUT +// glutInit(&argc, argv); +// glutInitDisplayMode(GLUT_RGB | GLUT_DOUBLE | GLUT_DEPTH); +// glutInitWindowPosition(100, 100); +// glutInitWindowSize(1024, 768); +// glutCreateWindow("3D Voxel Viewer"); +// +// // 3. 註冊回呼函式 +// glutReshapeFunc(WindowSize); +// glutDisplayFunc(Display); +// glutKeyboardFunc(Keyboard); +// glutMouseFunc(Mouse); +// glutMotionFunc(MotionMouse); +// +// // 4. 開始主迴圈 +// glutMainLoop(); +// +// return 0; +//} \ No newline at end of file