Point-LIO

ROS2 Fork repo maintainer: LihanChen2004

Point-LIO: Robust High-Bandwidth Lidar-Inertial Odometry

1. Introduction

The framework and key points of the Point-LIO.

New features:

1. would not fly under degeneration.

2. high odometry output frequency, 4k-8kHz.

3. robust to IMU saturation and severe vibration, and other aggressive motions (75 rad/s in our test).

4. no motion distortion.

5. computationally efficient, robust, versatile on public datasets with general motions.

6. As an odometry, Point-LIO could be used in various autonomous tasks, such as trajectory planning, control, and perception, especially in cases involving very fast ego-motions (e.g., in the presence of severe vibration and high angular or linear velocity) or requiring high-rate odometry output and mapping (e.g., for high-rate feedback control and perception).

Important notes:

A. Please make sure the IMU and LiDAR are Synchronized, that's important.

B. Please obtain the saturation values of your used IMU (i.e., accelerator and gyroscope), and the units of the accelerator of your used IMU, then modify the .yaml file according to those settings, including values of 'satu_acc', 'satu_gyro', 'acc_norm'. That's improtant.

C. 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 because Point-LIO processes at the sampling time of each LiDAR point.

D. We recommend to set the extrinsic_est_en to false if the extrinsic is given. As for the extrinsic initiallization, please refer to our recent work: Robust and Online LiDAR-inertial Initialization.

E. If you want to use Point-LIO without imu, set the "imu_en" as false, and provide a predefined value of gavity in "gravity_init" as true as possible in the yaml file, and keep the "use_imu_as_input" as 0.

1.1. Developers:

The codes of this repo are contributed by: Dongjiao He (贺东娇) and Wei Xu (徐威)

1.2. Related paper

Our paper is published on Advanced Intelligent Systems(AIS). Point-LIO, DOI: 10.1002/aisy.202200459

1.3. Related video

Our accompany video is available on YouTube.

2. What can Point-LIO do?

2.1 Simultaneous LiDAR localization and mapping (SLAM) without motion distortion

2.2 Produce high odometry output frequence and high bandwidth

2.3 SLAM with aggressive motions even the IMU is saturated

3. Prerequisites

3.1 Ubuntu and ROS2

Ubuntu >= 20.04

The default from apt PCL and Eigen is enough for FAST-LIO to work normally.

ROS >= Foxy (Recommend to use ROS-Humble). ROS Installation

sudo apt-get install ros-$ROS_DISTRO-pcl-conversions

3.2 Eigen

Following the official Eigen installation, or directly install Eigen by:

sudo apt-get install libeigen3-dev

3.3 livox_ros_driver2

Follow livox_ros_driver Installation.

Remarks:

• Since the Point-LIO supports Livox serials LiDAR, so the livox_ros_driver must be installed and sourced before run any Point-LIO luanch file.
• How to source? The easiest way is add the line source $Livox_ros_driver2_dir$/install/setup.bash to the end of file ~/.bashrc, where $Livox_ros_driver2_dir$ is the directory of the livox ros driver workspace (should be the ws_livox directory if you completely followed the livox official document).

4. Build

Clone the repository and catkin_make:

    cd ~/$A_ROS_DIR$/src
    git clone https://github.com/LihanChen2004/Point-LIO.git
    cd ..
    rosdepc install -r --from-paths src --ignore-src --rosdistro $ROS_DISTRO -
    colcon build --symlink-install -DCMAKE_BUILD_TYPE=Release
    source install/setup.bash # use setup.zsh if use zsh

• Remember to source the livox_ros_driver2 before build (follow 3.3 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}

5. Directly run

5.1 For Mid360

Connect to your PC to Livox Mid360 LiDAR by following Livox-ros-driver2 installation, then

    cd ~/$Point_LIO_ROS_DIR$
    source install/setup.bash
    ros2 launch point_lio point_lio.launch.py

• For livox serials, Point-LIO only support the data collected by the msg_mid360_launch.py.launch since only its livox_ros_driver2/msg/CustomMsg data structure produces the timestamp of each LiDAR point which is very important for Point-LIO. rviz_mid360_launch.py can not produce it right now.

• If you want to change the frame rate, please modify the publish_freq parameter in the msg_mid360_launch.py.launch of Livox-ros-driver2

5.2 For Livox serials with external IMU

Need to setup some parameters befor run:

Edit config/mid360.yaml to set the below parameters:

1. LiDAR point cloud topic name: lid_topic

2. IMU topic name: imu_topic

3. Translational extrinsic: extrinsic_T

4. Rotational extrinsic: extrinsic_R (only support rotation matrix)

   • The extrinsic parameters in Point-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.

5. Saturation value of IMU's accelerator and gyroscope: satu_acc, satu_gyro

6. The norm of IMU's acceleration according to unit of acceleration messages: acc_norm

5.3 For Velodyne or Ouster (Velodyne as an example)

Step A: Setup before run

Edit config/veoldy16.yaml to set the below parameters:

1. LiDAR point cloud topic name: lid_topic

2. IMU topic name: imu_topic (both internal and external, 6-aixes or 9-axies are fine)

3. Set the parameter timestamp_unit based on the unit of time (Velodyne) or t (Ouster) field in PoindCloud2 rostopic

4. Line number (we tested 16, 32 and 64 line, but not tested 128 or above): scan_line

5. Translational extrinsic: extrinsic_T

6. Rotational extrinsic: extrinsic_R (only support rotation matrix)

   • The extrinsic parameters in Point-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. the IMU is the base frame).

7. Saturation value of IMU's accelerator and gyroscope: satu_acc, satu_gyro

8. The norm of IMU's acceleration according to unit of acceleration messages: acc_norm

Step B: Run below

    cd ~/$Point_LIO_ROS_DIR$
    source install/setup.bash
    ros2 launch point_lio point_lio.launch.py rviz:=True point_lio_cfg_dir:=/path/to/Point-LIO/config/velody16.yaml

Step C: Run LiDAR's ros driver or play rosbag.

5.4 PCD file save

Set pcd_save_enable in launch file to 1. All the scans (in global frame) will be accumulated and saved to the file Point-LIO/PCD/scans.pcd after the Point-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.

  txt 1 is all random 2 is X values 3 is Y values 4 is Z values 5 is intensity

6. Examples

The example datasets could be downloaded through onedrive. Pay attention that if you want to test on racing_drone.bag, [0.0, 9.810, 0.0] should be input in 'mapping/gravity_init' in avia.yaml, and set the 'start_in_aggressive_motion' as true in the yaml. Because this bag start from a high speed motion. And for PULSAR.bag, we change the measuring range of the gyroscope of the built-in IMU to 17.5 rad/s. Therefore, when you test on this bag, please change 'satu_gyro' to 17.5 in avia.yaml.

6.1. Example-1: SLAM on datasets with aggressive motions where IMU is saturated

6.2. Example-2: Application on FPV and PULSAR

PULSAR is a self-rotating UAV actuated by only one motor, PULSAR

7. Contact us

If you have any questions about this work, please feel free to contact me <hdj65822ATconnect.hku.hk> and Dr. Fu Zhang <fuzhangAThku.hk> via email.