- This repository is a SLAM implementation combining FAST-LIO2 with pose graph optimization and loop closing based on LIO-SAM paper
- Loop-detection is based on radius search and ICP is used to calc matching
- Note: similar repositories already exist
- FAST_LIO_LC: FAST-LIO2 SC-A-LOAM based SLAM
- FAST_LIO_SLAM: FAST-LIO2 ScanContext based SLAM
- FAST_LIO_SAM: FAST-LIO2 LIO-SAM
- Note2: main code (PGO) is modularized and hence can be combined with any other LIO / LO
- This repo is to learn GTSAM myself!
- and as GTSAM tutorial for beginners - GTSAM 튜토리얼 한글 포스팅
KITTI seq 05 top view - (left): FAST-LIO2 (right): FAST-LIO-SAM
KITTI seq 05 side view - (top): FAST-LIO2 (bottom): FAST-LIO-SAM
KITTI seq 05 trajectories - (blue): FAST-LIO2 (green): FAST-LIO-SAM
- For better loop-detection and transform calculation, FAST-LIO-SAM-QN is also coded and opened.
- ROS (it comes with
Eigen
andPCL
) - GTSAM
wget -O gtsam.zip https://github.com/borglab/gtsam/archive/refs/tags/4.1.1.zip unzip gtsam.zip cd gtsam-4.1.1/ mkdir build && cd build cmake -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF -DGTSAM_USE_SYSTEM_EIGEN=ON .. sudo make install -j16
- Get the code and build
cd ~/your_workspace/src git clone https://github.com/engcang/FAST-LIO-SAM --recursive cd .. catkin build -DCMAKE_BUILD_TYPE=Release . devel/setup.bash
- Then run (change config files in third_party/
FAST_LIO
)roslaunch fast_lio_sam run.launch lidar:=ouster roslaunch fast_lio_sam run.launch lidar:=velodyne roslaunch fast_lio_sam run.launch lidar:=livox
- odom_pcd_cb
- pub realtime pose in corrected frame
- keyframe detection -> if keyframe, add to pose graph save to keyframe queue
- pose graph optimization with iSAM2
- loop_timer_func
- process a saved keyframe
- detect loop -> if loop, add to pose graph
- process a saved keyframe
- vis_timer_func
- visualize all (Note: global map is only visualized once uncheck/check the mapped_pcd in rviz to save comp.)