Noted: Ubuntu 16.04 and lower is not supported

Related Works

1. ikd-Tree: A state-of-art dynamic KD-Tree for 3D kNN search.
2. IKFOM: A Toolbox for fast and high-precision on-manifold Kalman filter.
3. UAV Avoiding Dynamic Obstacles: One of the implementation of FAST-LIO in robot's planning.
4. R2LIVE: A high-precision LiDAR-inertial-Vision fusion work using FAST-LIO as LiDAR-inertial front-end.

FAST-LIO

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;

FAST-LIO 2.0 (2021-07-05 Update)

Related video:

FAST-LIO2

FAST-LIO1

New features:

1. Incremental mapping using ikd-Tree, achieve faster speed and over 100Hz LiDAR rate.
2. Direct odometry on Raw LiDAR points (feature extraction can be closed), achieving better accuracy.
3. Since no need for feature extraction, FAST-LIO2 can support different LiDAR Types including spinning (Velodyne, Ouster) and solid-state (Avia, horizon) LiDARs, and can be easily extended to support more LiDAR.
4. Support external IMU.
5. Support ARM-based platforms including Khadas VIM3, Nivida TX2, Raspberry 4B with 8G RAM.

Contributors

Wei Xu 徐威，Yixi Cai 蔡逸熙，Dongjiao He 贺东娇，Fangcheng Zhu 朱方程，Jiarong Lin 林家荣，Zheng Liu 刘政

Related papers:

FAST-LIO2: Fast Direct LiDAR-inertial Odometry (Currently Unavailable)

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

1. Prerequisites

1.1 Ubuntu and ROS

Ubuntu >= 18.04 (Ubuntu 16.04 is not supported)

For Ubuntu 18.04 or higher, the default PCL and Eigen is enough for FAST-LIO to work normally.

ROS >= Melodic. ROS Installation

1.2. PCL && Eigen && openCV

PCL >= 1.8, Follow PCL Installation.

Eigen >= 3.3.4, Follow Eigen Installation.

1.3. livox_ros_driver

Follow livox_ros_driver Installation.

Remarks:

• Since the FAST-LIO must support Livox serials LiDAR firstly, so the livox_ros_driver must be installed and sourced before run any FAST-LIO luanch file.
• How to source? The easiest way is add the line source $Licox_ros_driver_dir$/devel/setup.bash to the end of file ~/.bashrc, where $Licox_ros_driver_dir$ is the directory of the livox ros driver workspace (should be the ws_livox directory if you completely followed the livox official document).

2. Build

Clone the repository and catkin_make:

    cd ~/$A_ROS_DIR$/src
    git clone https://github.com/hku-mars/FAST_LIO.git
    cd FAST_LIO
    git submodule update --init
    cd ../..
    catkin_make
    source devel/setup.bash

• Remember to source the livox_ros_driver before build (follow 1.3 livox_ros_driver)
• If you want to use a custom build of PCL, add the following line to ~/.bashrc export PCL_ROOT={CUSTOM_PCL_PATH}

3. Directly run

3.1 For Avia

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

    cd ~/$FAST_LIO_ROS_DIR$
    source devel/setup.bash
    roslaunch fast_lio mapping_avia.launch
    roslaunch livox_ros_driver livox_lidar_msg.launch

• 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.

3.2 For Velodyne or Ouster (using Velodyne as an example)

Step A: Setup before run

Edit FAST-LIO/config/velodyne.yaml to set the below parameters:

1. lidar point cloud topic name: lid_topic
2. IMU topic name: imu_topic
3. Line number (we tested 16 and 32 line, but not tested 64 or above): scan_line
4. Translational extrinsic: extrinsic_T
5. Rotational extrinsic: extrinsic_R (only support rotation matrix)

• The extrinsic parameters in FAST-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. the IMU is the base frame).

Step B: Run below

    cd ~/$FAST_LIO_ROS_DIR$
    source devel/setup.bash
    roslaunch fast_lio mapping_velodyne.launch

Step C: Run LiDAR's ros driver or play rosbag. Remarks:

• We will produce some velodyne datasets which is already transfered to Rosbags, please wait for a while.

4. Rosbag Example

4.1 Indoor rosbag (Livox Avia LiDAR)

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

4.2 Outdoor rosbag (Livox Avia LiDAR)

Download avia_hku_main building_mapping and then

roslaunch fast_lio mapping_avia.launch
rosbag play YOUR_DOWNLOADED.bag

5.Implementation on UAV

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.

The main structure of this UAV is 3d printed (Aluminum or PLA), the .stl file will be open-sourced in the future.

6.Acknowledgments

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