1. [ikd-Tree](https://github.com/hku-mars/ikd-Tree): A state-of-art dynamic KD-Tree for 3D kNN search.
2. [IKFOM](https://github.com/hku-mars/IKFoM): A Toolbox for fast and high-precision on-manifold Kalman filter.
3. [UAV Avoiding Dynamic Obstacles](https://github.com/hku-mars/dyn_small_obs_avoidance): One of the implementation of FAST-LIO in robot's planning.
4. [R2LIVE](https://github.com/hku-mars/dyn_small_obs_avoidance): A high-precision LiDAR-inertial-Vision fusion work using FAST-LIO as LiDAR-inertial front-end.
**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;
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.
[Wei Xu 徐威](https://github.com/XW-HKU)，[Yixi Cai 蔡逸熙](https://github.com/Ecstasy-EC)，[Dongjiao He 贺东娇](https://github.com/Joanna-HE)，[Fangcheng Zhu 朱方程](https://github.com/zfc-zfc)，[Jiarong Lin 林家荣](https://github.com/ziv-lin)，[Zheng Liu 刘政](https://github.com/Zale-Liu), [Borong Yuan](https://github.com/borongyuan)
- 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).
- If you want to change the frame rate, please modify the **publish_freq** parameter in the [livox_lidar_msg.launch](https://github.com/Livox-SDK/livox_ros_driver/blob/master/livox_ros_driver/launch/livox_lidar_msg.launch) of [Livox-ros-driver](https://github.com/Livox-SDK/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.
Download [avia_indoor_quick_shake_example1](https://drive.google.com/file/d/1SWmrwlUD5FlyA-bTr1rakIYx1GxS4xNl/view?usp=sharing) or [avia_indoor_quick_shake_example2](https://drive.google.com/file/d/1wD485CIbzZlNs4z8e20Dv2Q1q-7Gv_AT/view?usp=sharing) and then
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](https://github.com/Livox-SDK/livox_mapping), [LINS](https://github.com/ChaoqinRobotics/LINS---LiDAR-inertial-SLAM) and [Loam_Livox](https://github.com/hku-mars/loam_livox).
