Official implementation of our paper "GRIL-Calib: Targetless Ground Robot IMU-LiDAR Extrinsic Calibration Method using Ground Plane Motion Constraints".
- GRIL-Calib is the LiDAR-IMU calibration method for ground robots.
- Using only planar motion, the 6-DOF calibration parameter could be estimated.
- Ubuntu 18.04
- ROS Melodic
- PCL >= 1.8
- Eigen >= 3.3.4
- livox_ros_driver
- ceres-solver-2.0.0
Requires Docker and the NVIDIA Container Toolkit installed.
1. Enter the /docker folder and make a docker image.
git clone https://github.com/Taeyoung96/GRIL-Calib.git
cd GRIL-Calib/docker
docker build -t gril-calib .
When you have finished it, use the command docker images and you can see the output below.
REPOSITORY TAG IMAGE ID CREATED SIZE
gril-calib latest 9f90339349a0 5 months ago 3.78GB
2. Make docker container (same path as above)
In /docker,
sudo chmod -R 777 container_run.sh
./container_run.sh <container_name> <image_name:tag>
./container_run.sh gril-calib-container gril-calib:latest
If you have successfully created the docker container, the terminal output will be similar to the below.
================Gril-Calib Docker Env Ready================
root@taeyoung-cilab:/root/catkin_ws#
3. Build and run GRIL-Calib
Inside the docker container, build and run the package.
catkin_make
source devel/setup.bash
The launch files for M2DGR, HILTI, and S3E, as experimented with in the paper, are shown below.
- For M2DGR,
roslaunch gril_calib m2dgr_xxxx.launch
- For HILTI,
roslaunch gril_calib hilti_xxxx.launch
- For S3E,
roslaunch gril_calib s3e_xxxx.launch
After running the launch file, you simply run the bag file for each sequence.
The reason for this is that the LiDAR ground segmentation algorithm has only been tested on Spinning LiDAR.
If we could achieve ground segmentation, we could theoretically do it for a Solid-state LiDAR like Livox Avia.
- Make sure to acquire your data on an area with flat ground.
- It would be helpful to collect data as the ground robot draws an "8".
- Please make sure the unit of your input angular velocity is rad/s.
Similar to LI-Init, edit config/xxx.yaml to set the below parameters:
lid_topic: Topic name of LiDAR point cloud.imu_topic: Topic name of IMU measurements.imu_sensor_height: Height from ground to IMU sensor (meter)data_accum_length: A threshold to assess if the data is enough for calibration.x_accumulate: Parameter that determines how much the x-axis rotates (Assuming the x-axis is front)y_accumulate: Parameter that determines how much the y-axis rotates (Assuming the y-axis is left)z_accumulate: Parameter that determines how much the z-axis rotates (Assuming the z-axis is up)gyro_factor,acc_factor,ground_factor: Weight for each residualset_boundary: When performing nonlinear optimization, set the bound based on the initial value. (only translation vector)bound_th: Set the threshold for the bound. (meter)
Thanks to hku-mars/LiDAR_IMU_Init for sharing their awesome work!
We also thanks to url-kaist/patchwork-plusplus-ros for sharing LiDAR ground segmentation algorithm.
If you find our paper useful in your research, please cite us using the following entry:
@ARTICLE{10506583,
author={Kim, TaeYoung and Pak, Gyuhyeon and Kim, Euntai},
journal={IEEE Robotics and Automation Letters},
title={GRIL-Calib: Targetless Ground Robot IMU-LiDAR Extrinsic Calibration Method using Ground Plane Motion Constraints},
year={2024},
volume={},
number={},
pages={1-8},
keywords={Calibration;Laser radar;Robot sensing systems;Robots;Optimization;Odometry;Vectors;Sensor Fusion;Calibration and Identification},
doi={10.1109/LRA.2024.3392081}}