FAST_LIO_LC

The tight integration of FAST-LIO with Radius-Search-based loop closure module.

FAST-LIO (Fast LiDAR-Inertial Odometry) is a computationally efficient and robust LiDAR-inertial odometry package. It fuses LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter. But it doesn't have a loop closure module to eliminate the accumulated drift.

Therefore, this project implements the pose graph optimization with a radius-search-based loop closure module which refers to FAST_LIO_SLAM. And the pose and map in the iterated extended Kalman filter of FAST-LIO will be updated according to the optimization which is a key difference with FAST_LIO_SLAM.

• FAST_LIO_LC
  • 1. Prerequisites
  • 2. Build
  • 3. Quick test
  • 3.1 For Velodyne 16
  • 4. Example results
  • Acknowledgements

1. Prerequisites

• Ubuntu 18.04 and ROS Melodic
• PCL >= 1.8 (default for Ubuntu 18.04)
• Eigen >= 3.3.4 (default for Ubuntu 18.04)
• GTSAM >= 4.0.0(tested on 4.0.0-alpha2)

2. Build

cd YOUR_WORKSPACE/src
git clone https://github.com/yanliang-wang/FAST_LIO_LC.git
cd ..
catkin_make

3. Quick test

3.1 For Velodyne 16

You can test this project with our data which includes /velodyne_points(10Hz) and /imu/data(400Hz).

roslaunch fast_lio mapping_velodyne.launch
roslaunch aloam_velodyne fastlio_velodyne_VLP_16.launch
rosbag play  T3F2-2021-08-02-15-00-12.bag  -r 2

If you want to test the original FAST LIO (i.e. without the loop closure module), you can set lc_enable in the mapping_velodyne.launch to false and run following commands.

roslaunch fast_lio mapping_velodyne.launch
rosbag play  T3F2-2021-08-02-15-00-12.bag  -r 2

4. Example results

video: Youtube link , Bilibili link

Acknowledgements

In this project, the LIO module refers to FAST-LIO and the pose graph optimization refers to FAST_LIO_SLAM.

Many thanks for their work.