li_slam_ros2

This package is a combination of lidarslam_ros2 and the LIO-SAM IMU composite method.

See LIO-SAM for IMU composites, otherwise see lidarslam_ros2.

• Walking dataset(casual_walk.bag)

Yellow path: path without loop closure, Green path: modified path, Red: map

Reference(From the LIO-SAM paper)
https://github.com/TixiaoShan/LIO-SAM/blob/master/config/doc/paper.pdf

• Campus dataset (large) demo(big_loop.bag)

Yellow path: path without loop closure, Red: map (the 10x10 grids in size of 10m × 10m)

Green path: modified path with loop closure, Red: map

requirement to build

You need ndt_omp_ros2 and gtsam for scan-matcher

clone

cd ~/ros2_ws/src
git clone --recursive https://github.com/rsasaki0109/li_slam_ros2

gtsam install

sudo add-apt-repository ppa:borglab/gtsam-release-4.1
sudo apt update
sudo apt install libgtsam-dev libgtsam-unstable-dev

build

cd ~/ros2_ws
rosdep update
rosdep install --from-paths src --ignore-src -yr
colcon build --cmake-args -DCMAKE_BUILD_TYPE=Release

Walking dataset demo(casual_walk.bag)

The optimization pipeline in Lidar Inertial SLAM were taken from LIO-SAM.

(Note: See the LIO-SAM repository for detailed settings regarding IMU.
The other thing to note is that the speed will diverge if the voxel_grid_size is large.

demo data(ROS1) in LIO-SAM
https://github.com/TixiaoShan/LIO-SAM
To use ros1 rosbag , use rosbags.
The Velodyne VLP-16 was used in this data.

rviz2 -d src/li_slam_ros2/scanmatcher/rviz/lio.rviz 

ros2 launch scanmatcher lio.launch.py

ros2 bag play walking_dataset/

Green arrow: pose, Yellow path: path, Green path: path by imu

Yellow path: path without loop closure, Green path: modified path, Red: map

rosgraph

pose_graph.g2o and map.pcd are saved in loop closing or using the following service call.

ros2 service call /map_save std_srvs/Empty

Campus dataset (large) demo(big_loop.bag)

rviz2 -d src/li_slam_ros2/scanmatcher/rviz/lio_bigloop.rviz 

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