SLAM:
- ikd-Tree: A state-of-art dynamic KD-Tree for 3D kNN search.
- R2LIVE: A high-precision LiDAR-inertial-Vision fusion work using FAST-LIO as LiDAR-inertial front-end.
- LI_Init: A robust, real-time LiDAR-IMU extrinsic initialization and synchronization package..
- FAST-LIO-LOCALIZATION: The integration of FAST-LIO with Re-localization function module.
Control and Plan:
- IKFOM: A Toolbox for fast and high-precision on-manifold Kalman filter.
- UAV Avoiding Dynamic Obstacles: One of the implementation of FAST-LIO in robot's planning.
- UGV Demo: Model Predictive Control for Trajectory Tracking on Differentiable Manifolds.
- Bubble Planner: Planning High-speed Smooth Quadrotor Trajectories using Receding Corridors.
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:
- Fast iterated Kalman filter for odometry optimization;
- Automaticaly initialized at most steady environments;
- Parallel KD-Tree Search to decrease the computation;
Related video: FAST-LIO2, FAST-LIO1
Pipeline:
New Features:
- Incremental mapping using ikd-Tree, achieve faster speed and over 100Hz LiDAR rate.
- Direct odometry (scan to map) on Raw LiDAR points (feature extraction can be disabled), achieving better accuracy.
- Since no requirements for feature extraction, FAST-LIO2 can support many types of LiDAR including spinning (Velodyne, Ouster) and solid-state (Livox Avia, Horizon, MID-70) LiDARs, and can be easily extended to support more LiDARs.
- Support external IMU.
- Support ARM-based platforms including Khadas VIM3, Nivida TX2, Raspberry Pi 4B(8G RAM).
Related papers:
FAST-LIO2: Fast Direct LiDAR-inertial Odometry
FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter
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, Borong Yuan
Ubuntu >= 16.04
For Ubuntu 18.04 or higher, the default PCL and Eigen is enough for FAST-LIO to work normally.
ROS >= Melodic. ROS Installation
PCL >= 1.8, Follow PCL Installation.
Eigen >= 3.3.4, Follow Eigen Installation.
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 thews_livox
directory if you completely followed the livox official document).
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}
Noted:
A. Please make sure the IMU and LiDAR are Synchronized, that's important.
B. The warning message "Failed to find match for field 'time'." means the timestamps of each LiDAR points are missed in the rosbag file. That is important for the forward propagation and backwark propagation.
C. We recommend to set the extrinsic_est_en to false if the extrinsic is give. As for the extrinsic initiallization, please refer to our recent work: Robust and Online LiDAR-inertial Initialization.
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
- For livox serials, FAST-LIO only support the data collected by the
livox_lidar_msg.launch
since only itslivox_ros_driver/CustomMsg
data structure produces the timestamp of each LiDAR point which is very important for the motion undistortion.livox_lidar.launch
can not produce it right now. - If you want to change the frame rate, please modify the publish_freq parameter in the