## Related Works and Extended Application
**SLAM:**
1. [ikd-Tree](https://github.com/hku-mars/ikd-Tree): A state-of-art dynamic KD-Tree for 3D kNN search.
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.
**Control and Plan:**
1. [IKFOM](https://github.com/hku-mars/IKFoM): A Toolbox for fast and high-precision on-manifold Kalman filter.
2. [UAV Avoiding Dynamic Obstacles](https://github.com/hku-mars/dyn_small_obs_avoidance): One of the implementation of FAST-LIO in robot's planning.
3. [UGV Demo](https://www.youtube.com/watch?v=wikgrQbE6Cs): Model Predictive Control for Trajectory Tracking on Differentiable Manifolds.
4. [Bubble Planner](https://arxiv.org/abs/2202.12177): Planning High-speed Smooth Quadrotor Trajectories using Receding Corridors.
<!-- 10. [**FAST-LIVO**](https://github.com/hku-mars/FAST-LIVO): Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry. -->
## 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)
<!--  -->
<!-- [](https://youtu.be/2OvjGnxszf8) -->
<div align="left">
<img src="doc/real_experiment2.gif" width=49.6% />
<img src="doc/ulhkwh_fastlio.gif" width = 49.6% >
</div>
**Related video:** [FAST-LIO2](https://youtu.be/2OvjGnxszf8), [FAST-LIO1](https://youtu.be/iYCY6T79oNU)
**Pipeline:**
<div align="center">
<img src="doc/overview_fastlio2.svg" width=99% />
</div>
**New Features:**
1. Incremental mapping using [ikd-Tree](https://github.com/hku-mars/ikd-Tree), achieve faster speed and over 100Hz LiDAR rate.
2. Direct odometry (scan to map) on Raw LiDAR points (feature extraction can be disabled), achieving better accuracy.
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.
4. Support external IMU.
5. Support ARM-based platforms including Khadas VIM3, Nivida TX2, Raspberry Pi 4B(8G RAM).
**Related papers**:
[FAST-LIO2: Fast Direct LiDAR-inertial Odometry](doc/Fast_LIO_2.pdf)
[FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter](https://arxiv.org/abs/2010.08196)
**Contributors**
[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)
<!-- <div align="center">
<img src="doc/results/HKU_HW.png" width = 49% >
<img src="doc/results/HKU_MB_001.png" width = 49% >
</div> -->
## 1. Prerequisites
### 1.1 **Ubuntu** and **ROS**
**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](http://wiki.ros.org/ROS/Installation)
### 1.2. **PCL && Eigen**
PCL >= 1.8, Follow [PCL Installation](http://www.pointclouds.org/downloads/linux.html).
Eigen >= 3.3.4, Follow [Eigen Installation](http://eigen.tuxfamily.org/index.php?title=Main_Page).
### 1.3. **livox_ros_driver**
Follow [livox_ros_driver Installation](https://github.com/Livox-SDK/livox_ros_driver).
*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 $Livox_ros_driver_dir$/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).
## 2. Build
If you want to use docker conatiner to run fastlio2, please install the docker on you machine.
Follow [Docker Installation](https://docs.docker.com/engine/install/ubuntu/).
### 2.1 Docker Container
User can create a new script with anyname by the following command in linux:
```
touch <your_custom_name>.sh
```
Place the following code inside the ``` <your_custom_name>.sh ``` script.
```
#!/bin/bash
mkdir docker_ws
# Script to run ROS Kinetic with GUI support in Docker
# Allow X server to be accessed from the local machine
xhost +local:
# Container name
CONTAINER_NAME="fastlio2"
# Run the Docker container
docker run -itd \
--name=$CONTAINER_NAME \
--user mars_ugv \
--network host \
--ipc=host \
-v /home/$USER/docker_ws:/home/mars_ugv/docker_ws \
--privileged \
--env="QT_X11_NO_MITSHM=1" \
--volume="/etc/localtime:/etc/localtime:ro" \
-v /dev/bus/usb:/dev/bus/usb \
--device=/dev/dri \
--group-add video \
-v /tmp/.X11-unix:/tmp/.X11-unix \
--env="DISPLAY=$DISPLAY" \
kenny0407/marslab_fastlio2:latest \
/bin/bash
```
execute the following command to grant execute permissions to the script, making it runnable:
```
sudo chmod +x <your_custom_name>.sh
```
execute the following command to download the image and create the container.
```
./<your_custom_name>.sh
```
*Script explanation:*
- The docker run command provided below creates a container with a tag, using an image from Docker Hub. The download duration for this image can differ depending on the user's network speed.
- This command also establishes a new workspace called ``` docker_ws ```, which serves as a shared folder between the Docker container and the host machine. This means that if users wish to run the rosbag example, they need to download the rosbag file and place it in the ``` docker_ws ``` directory on their host machine.
- Subsequently, a folder with the same name inside the Docker container will receive this file. Users can then easily play the file within Docker.
- In this example, we've shared the network of the host machine with the Docker container. Consequently, if users execute the ``` rostopic list ``` command, they will observe identical output whether they run it on the host machine or inside the Docker container."
### 2.2 Build from source
Clone the repository and catkin_make:
```
cd ~/$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
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 R
Panda配置大部分资源 （1311个子文件） 
libDepthUtils.a 14KB mongoose.c 128KB ctrl-gen.c 56KB device.c 53KB stream.c 47KB frame.c 14KB diag.c 14KB frame-mjpeg.c 8KB example.c 7KB ctrl.c 6KB init.c 5KB test.c 4KB usb_reset.c 2KB misc.c 2KB UVCCamera.cfg 7KB Astra.cfg 3KB RangeSensorLayer.cfg 850B line_hsv.cfg 593B .clang-format 2KB .clang-format 2KB FindPCAP.cmake 3KB FindOpenCVPkg.cmake 2KB FindJpegPkg.cmake 2KB FindLibUSB.cmake 1KB version.cmake 990B libuvcConfig.cmake 473B doxygen.conf 97KB config 281B config 275B config 273B config 268B config 257B ikd_Tree.cpp 67KB ikd_Tree.cpp 64KB sl_lidar_driver.cpp 59KB nvilidar_driver_serialport.cpp 50KB nvilidar_driver_udp.cpp 49KB laserMapping.cpp 45KB astra_driver.cpp 40KB handler_capsules.cpp 39KB command_impl.cpp 35KB device_manager.cpp 30KB net_socket.cpp 26KB general_command_handler.cpp 26KB parse_lidar_state_info.cpp 26KB net_socket.cpp 25KB net_socket.cpp 24KB sc_mini.cpp 24KB preprocess.cpp 23KB astra_device.cpp 23KB lddc.cpp 22KB camera_driver.cpp 20KB node.cpp 19KB adapter_manager.cpp 18KB build_request.cpp 18KB FastCRChw.cpp 16KB uvc_cam.cpp 16KB range_sensor_layer.cpp 16KB pub_handler.cpp 16KB livox_lidar_upgrader.cpp 16KB livox_lidar_callback.cpp 15KB parse_cfg_file.cpp 14KB nvilidar_serial_unix.cpp 14KB FastCRCsw.cpp 13KB livox_lidar_sdk.cpp 13KB logger_manager.cpp 13KB file_manager.cpp 11KB net_serial.cpp 11KB hap_command_handler.cpp 11KB base_communication.cpp 10KB rs_driver_viewer.cpp 10KB mid360_command_handler.cpp 10KB nvilidar_process.cpp 10KB nvilidar_serial_win.cpp 9KB camera.cpp 9KB net_serial.cpp 9KB pointcloud_to_laserscan_nodelet.cpp 9KB sl_async_transceiver.cpp 9KB astra_device_manager.cpp 9KB nvilidar_driver_net_config.cpp 8KB stereo.cpp 8KB net_serial.cpp 8KB main.cpp 8KB livox_ros_driver2.cpp 8KB logger_handler.cpp 7KB sl_lidarprotocol_codec.cpp 7KB params_check.cpp 6KB base_control.cpp 6KB handler_hqnode.cpp 6KB lds_lidar.cpp 6KB nvilidar_node.cpp 6KB rplidar_driver.cpp 6KB synchro.cpp 6KB network_util.cpp 6KB dataunpacker.cpp 6KB lds.cpp 6KB laserscan_to_pointcloud_nodelet.cpp 6KB synchro.cpp 6KB network_util.cpp 6KB handler_normalnode.cpp 5KB共 1311 条
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