Creating a 3D map using 3D-LiDAR (Livox: MID-360) "Implementation of FAST-LIO"

System Architecture

• Fast iterative FILTERS for odometry optimization.
• Automatic initialization in the most stable environment
• Parallel KD-tree search to reduce computation

Features

• Incremental mapping with ikd-Tree enables faster LiDAR rates above 100Hz
• Direct odometry (scan and map) on raw LiDAR points (feature extraction can be disabled) for improved accuracy
• With no feature extraction requirement, 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 many more LiDARs.
• External IMU support
• Also supports ARM-based platforms such as Khadas VIM3, Nivida TX2, and Raspberry Pi 4B (8G RAM)

• Hardware Configuration

• Livox 3D-LiDAR: MID-360

• Intel NUC: NUC11PAHi3

• environment

• ROS (Noetic)

set up

1. Install and set up Livox_SDK2.

• Livox-SDK2 (GitHub)

2. Install and set up Livox_ros_driver2.

• livox_ros_driver2 (GitHub)

• If you have installed livox_ros_driver2 separately, add the following to your ~/.bashrc.
  source (installation directory) / devel / setup.bash

3. Download the FAST-LIO package published on github.

$git clone https://github.com/hku-mars/FAST_LIO ~/FAST-LIO/src/FAST-LIO
$cd ~/FAST_LIO/src/FAST-LIO
$git submodule update --init

If you run catkin_make as is, an error will occur, so fix the following:

Setting parameters

• Launch file (mapping_mid360.launch)

<launch>
  <!-- Launch file for Livox MID360 LiDAR -->

    <arg name="rviz" default="true" />

    <rosparam command="load" file="$(find fast_lio)/config/mid360.yaml" />

    <param name="feature_extract_enable" type="bool" value="0"/>
    <param name="point_filter_num" type="int" value="3"/>
    <param name="max_iteration" type="int" value="3" />
    <param name="filter_size_surf" type="double" value="0.5" />
    <param name="filter_size_map" type="double" value="0.5" />
    <param name="cube_side_length" type="double" value="1000" />
    <param name="runtime_pos_log_enable" type="bool" value="0" />

    <node pkg="fast_lio" type="fastlio_mapping" name="laserMapping" output="screen" />

    <group if="$(arg rviz)">
    <node launch-prefix="nice" pkg="rviz" type="rviz" name="rviz" args="-d $(find fast_lio)/rviz_cfg/loam_livox.rviz" />
    </group>

   </launch>

• The feature_extract_enable parameter controls whether feature extraction is enabled.
  When this parameter is enabled, fast-lio will extract feature points from LiDAR scans and use those feature points to build maps and localize.
  Feature points help identify structures and objects in the environment, which helps improve the accuracy of SLAM algorithms.
  On the other hand, if this parameter is disabled, fast-lio will not perform feature extraction and will instead use the raw LiDAR scan data for mapping and localization.
  In this case, not using feature points may improve processing speed, but may affect the complexity of the environment and accuracy.

• config file (mid360.yaml)

common:
        lid_topic:  "/livox/lidar"
        imu_topic:  "/livox/imu"
        time_sync_en: false                 # ONLY turn on when external time synchronization is really not possible
        time_offset_lidar_to_imu: 0.0 # Time offset between lidar and IMU calibrated by other algorithms, e.g. LI-Init (can be found in README).
        # This param will take effect no matter what time_sync_en is. So if the time offset is not known exactly, please set as 0.0

    preprocess:
        lidar_type: 1                # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR, 
        scan_line: 4
        blind: 0.2

    mapping:
        acc_cov: 0.1
        gyr_cov: 0.1
        b_acc_cov: 0.0001
        b_gyr_cov: 0.0001
        fov_degree:    360
        det_range:      50.0
        extrinsic_est_en:  false      # true: enable the online estimation of IMU-LiDAR extrinsic
        extrinsic_T: [ -0.011, -0.02329, 0.04412 ]
        extrinsic_R: [ 1, 0, 0,
        　        0, 1, 0,
        0, 0, 1]

    publish:
        path_en:  true
        scan_publish_en:  true       # false: close all the point cloud output
        dense_publish_en: true       # false: low down the points number in a global-frame point clouds scan.
        scan_bodyframe_pub_en: true  # true: output the point cloud scans in IMU-body-frame

    pcd_save:
        pcd_save_en: true
        interval: -1           # how many LiDAR frames saved in each pcd file; 
        # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

• Main parameters

• lid_topic: LiDAR point cloud topic name
• imu_topic: IMU topic name
• time_sync_en: Time synchronization with external IMU (false if using the IMU in LiDAR)
• lidar_type: LiDAR type 1=Livox, 2=Velodyne, 3=Ouster However, if you select Livox, use livox customized pointcloud data
• scan_line: Number of light sources: 4 for MID-360
• blind: close-range blind, set to 0.2m
• fov_degree: Horizontal FOV
• det_range: Detection distance Set to 50m in this example
• extrinsic_est_en: In the case of an external IMU, whether the origin of the LiDAR and the IMU can be adjusted (false if using the IMU built into the LiDAR)
• path_en: Whether to display the movement path
• scan_publish_en: Whether or not to display point clouds
• dense_publish_en: Reduce the number of points in a global-frame point cloud scan
• scan_bodyframe_pub_en: Output point cloud scan in IMU-body-frame
• pcd_save_en: Can save to pcd?

How to run

$ roslaunch livox_ros_driver2 msg_MID360.launch
$ roslaunch fast_lio mapping_mid360.launch

Execution result

After editing with CloudCompare (overall view: data viewed from above)

After editing with CloudCompare (overall view with ceiling cut out: data viewed from above)

Reference

• FAST-LIO
• CloudCompare
• Creating a 3D map using 3D LiDAR (Livox: MID-360) | LIVOX Column

Disclaimer

The system introduced on this page has been confirmed for demonstration purposes only. Therefore, we cannot be held responsible for any problems that may arise from using the contents of this page. Also, if you intend to use it for commercial purposes, please check with the source code creator.