2. [R2LIVE](https://github.com/hku-mars/r2live): A high-precision LiDAR-inertial-Vision fusion work using FAST-LIO as LiDAR-inertial front-end.
3. [LI_Init](https://github.com/hku-mars/LiDAR_IMU_Init): A robust, real-time LiDAR-IMU extrinsic initialization and synchronization package..
4. [FAST-LIO-LOCALIZATION](https://github.com/HViktorTsoi/FAST_LIO_LOCALIZATION): The integration of FAST-LIO with **Re-localization** function module.
5. [FAST-LIVO](https://github.com/hku-mars/FAST-LIVO) | [FAST-LIVO2](https://github.com/hku-mars/FAST-LIVO2): A state-of-art LiDAR-inertial-visual odometry (LIVO) system with high computational efficiency, robustness, and pixel-level accuracy.
**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 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.
[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 $Livox_ros_driver_dir$/devel/setup.bash ``` to the end of file ``` ~/.bashrc ```, where ``` $Livox_ros_driver_dir$ ``` is the directory of the livox ros driver workspace (should be the ``` ws_livox ``` directory if you completely followed the livox official document).
execute the following command to grant execute permissions to the script, making it runnable:
```
sudo chmod +x <your_custom_name>.sh
```
execute the following command to download the image and create the container.
```
./<your_custom_name>.sh
```
*Script explanation:*
- The docker run command provided below creates a container with a tag, using an image from Docker Hub. The download duration for this image can differ depending on the user's network speed.
- This command also establishes a new workspace called ``` docker_ws ```, which serves as a shared folder between the Docker container and the host machine. This means that if users wish to run the rosbag example, they need to download the rosbag file and place it in the ``` docker_ws ``` directory on their host machine.
- Subsequently, a folder with the same name inside the Docker container will receive this file. Users can then easily play the file within Docker.
- In this example, we've shared the network of the host machine with the Docker container. Consequently, if users execute the ``` rostopic list ``` command, they will observe identical output whether they run it on the host machine or inside the Docker container."
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 Real-time LiDAR-inertial Initialization**](https://github.com/hku-mars/LiDAR_IMU_Init).
- For livox serials, FAST-LIO only support the data collected by the ``` livox_lidar_msg.launch ``` since only its ``` livox_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 [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.
4. 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). They can be found in the official manual.
- FAST-LIO produces a very simple software time sync for livox LiDAR, set parameter ```time_sync_en``` to ture to turn on. But turn on **ONLY IF external time synchronization is really not possible**, since the software time sync cannot make sure accuracy.
- 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).
Set ``` pcd_save_enable ``` in launchfile to ``` 1 ```. All the scans (in global frame) will be accumulated and saved to the file ``` FAST_LIO/PCD/scans.pcd ``` after the FAST-LIO is terminated. ```pcl_viewer scans.pcd``` can visualize the point clouds.
*Tips for pcl_viewer:*
- change what to visualize/color by pressing keyboard 1,2,3,4,5 when pcl_viewer is running.
We produce [Rosbag Files](https://drive.google.com/drive/folders/1blQJuAB4S80NwZmpM6oALyHWvBljPSOE?usp=sharing) and [a python script](https://drive.google.com/file/d/1QC9IRBv2_-cgo_AEvL62E1ml1IL9ht6J/view?usp=sharing) to generate Rosbag files: ```python3 sensordata_to_rosbag_fastlio.py bin_file_dir bag_name.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](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).
