This repository is a modified LiDAR-inertial odometry system. The system is developed based on the open-source odometry framework FAST-LIO to get the odometry information. And the feature extract moudle is implemented based on LIO-SAM .

• Feature extract moudle is implemented based on lio-sam, this moudle support multiple lidar types(such as velodyne,ouster,robosense, livox etc.);
• laser mapping moudle is implemented base on fast-lio 1.0, Use Eigen matrix instead of IKFom;
• use ikdtree manage the map;
• the new laser mapping moudle support multiple lidar types: both traditional spinning lidar (velodyne, ouster, robsense etc.) and solid-state lidar(livox);
• add online extrinsic calib as fast-lio2

Demo video

• add ivox
• add extrinsic parameter calibration
• compare with FAST-LIO2
• add test video

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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 to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. Our package address many key issues:

1. Fast iterated Kalman filter for odometry optimization;
2. Automaticaly initialized at most steady environments;
3. Parallel KD-Tree Search to decrease the computation;
4. Robust feature extraction;

Developers

Wei Xu 徐威: Laser mapping and pose optimization;

Zheng Liu 刘政: Features extraction.

To know more about the details, please refer to our related paper:)

Our related paper: our related papers are now available on arxiv:

FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter

Our related video: https://youtu.be/iYCY6T79oNU

Ubuntu >= 18.04 (Ubuntu 16.04 is not supported)

ROS >= Melodic. ROS Installation

PCL >= 1.8, Follow PCL Installation.

Eigen >= 3.3.4, Follow Eigen Installation.

OpenCV >= 3.2, Follow openCV Installation.

Follow livox_ros_driver Installation.

Clone the repository and catkin_make:

    cd ~/catkin_ws/src
    git clone https://github.com/XW-HKU/fast_lio.git
    cd ..
    catkin_make
    source devel/setup.bash

Remarks:

• If you want to use a custom build of PCL, add the following line to ~/.bashrc export PCL_ROOT={CUSTOM_PCL_PATH}

Connect to your PC to Livox Avia LiDAR by following Livox-ros-driver installation, then

    ....
    roslaunch fast_lio mapping_avia.launch
    roslaunch livox_ros_driver livox_lidar_msg.launch
    

Remarks:

• If you want to change the frame rate, please modify the publish_freq parameter in the livox_lidar_msg.launch of Livox-ros-driver before make the livox_ros_driver pakage.

Connect to your PC to Livox Avia LiDAR following Livox-ros-driver installation, then

    ....
    roslaunch fast_lio mapping_avia_outdoor.launch
    roslaunch livox_ros_driver livox_lidar_msg.launch
    

Download avia_indoor_quick_shake_example1 or avia_indoor_quick_shake_example2 and then

roslaunch fast_lio mapping_avia.launch
rosbag play YOUR_DOWNLOADED.bag

Download avia_hku_main building_mapping and then

roslaunch fast_lio mapping_avia_outdoor.launch
rosbag play YOUR_DOWNLOADED.bag

In order to validate the robustness and computational efficiency of FAST-LIO in actual mobile robots, we build a small-scale quadrotor which can carry a Livox Avia LiDAR with 70 degree FoV and a DJI Manifold 2-C onboard computer with a 1.8 GHz Intel i7-8550U CPU and 8 G RAM, as shown in below.

Thanks for LOAM(J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time), Livox_Mapping and Loam_Livox.