GRIL-Calib

Official implementation of our paper "GRIL-Calib: Targetless Ground Robot IMU-LiDAR Extrinsic Calibration Method using Ground Plane Motion Constraints".

• ArXiv : https://arxiv.org/abs/2312.14035
• IEEE : https://ieeexplore.ieee.org/document/10506583

About GRIL-Calib

• GRIL-Calib is the LiDAR-IMU calibration method for ground robots.
• Using only planar motion, the 6-DOF calibration parameter could be estimated.

Prerequisites

• Ubuntu 18.04
• ROS Melodic
• PCL >= 1.8
• Eigen >= 3.3.4
• livox_ros_driver
• ceres-solver-2.0.0

Set up your environment easily with Docker! 🐳

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>

⚠️ You should change {container_name}, {docker image} to suit your environment.

./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

Run with a public dataset

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.

Run with your custom data

⚠️ This version only supports Spinning LiDAR (Velodyne, Ouster), not Solid-state LiDAR.

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.

Important parameters

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 residual
• set_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) ⭐️ See the ceres-solver documentation for more information.

Acknowledgments

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.

Citation

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={9},
  number={6},
  pages={5409-5416},
  keywords={Calibration;Laser radar;Robot sensing systems;Robots;Optimization;Odometry;Vectors;Calibration and identification;sensor fusion},
  doi={10.1109/LRA.2024.3392081}}