Panda配置大部分资源

## 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