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

You need ndt_omp_ros2 and gtsam for scan-matcher

clone

cd ~/ros2_ws/src
git clone --recursive https://github.com/rsasaki0109/lidarslam_ros2

gtsam install

sudo apt-get install libtbb-dev
mkdir ~/workspace && cd ~/workspace
git clone https://github.com/borglab/gtsam
cd gtsam
mkdir -p build && cd build
cmake \
  -DCMAKE_BUILD_TYPE=Release \
  -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF \
  -DGTSAM_USE_SYSTEM_EIGEN=ON \
  -DGTSAM_BUILD_EXAMPLES_ALWAYS=OFF \
  ..
make -j4 check
sudo make install -j4

build

cd ~/ros2_ws
colcon build

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
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 -s rosbag_v2 casual_walk.bag 

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

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

ros2 launch scanmatcher lio_bigloop.launch.py

ros2 bag play -s rosbag_v2 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

• Eigen
• PCL(BSD3)
• g2o(BSD2 except a part)
• ndt_omp (BSD2)
• gtsam(BSD2)