GRIL-Calib: Targetless Ground Robot IMU-LiDAR Extrinsic Calibration Method using Ground Plane Motion Constraints

TL;DR

GRIL-Calib introduces a novel targetless ground robot IMU-LiDAR calibration method that leverages ground plane constraints to accurately determine full 6-DoF extrinsic parameters despite restricted planar motion.

Contribution

It proposes a ground plane-based calibration approach that enables full 6-DoF calibration without requiring full 3D motion, using a single optimization framework.

Findings

Improved calibration accuracy over existing methods.

Effective in real-world datasets with planar motion constraints.

Robustness comparable or superior to state-of-the-art methods.

Abstract

Targetless IMU-LiDAR extrinsic calibration methods are gaining significant attention as the importance of the IMU-LiDAR fusion system increases. Notably, existing calibration methods derive calibration parameters under the assumption that the methods require full motion in all axes. When IMU and LiDAR are mounted on a ground robot the motion of which is restricted to planar motion, existing calibration methods are likely to exhibit degraded performance. To address this issue, we present GRIL-Calib: a novel targetless Ground Robot IMU-LiDAR Calibration method. Our proposed method leverages ground information to compensate for the lack of unrestricted full motion. First, we propose LiDAR Odometry (LO) using ground plane residuals to enhance calibration accuracy. Second, we propose the Ground Plane Motion (GPM) constraint and incorporate it into the optimization for calibration, enabling the determination of full 6-DoF extrinsic parameters, including theoretically unobservable direction. Finally, unlike baseline methods, we formulate the calibration not as sequential two optimizations but as a single optimization (SO) problem, solving all calibration parameters simultaneously and improving accuracy. We validate our GRIL-Calib by applying it to various real-world datasets and comparing its performance with that of existing state-of-the-art methods in terms of accuracy and robustness. Our code is available at https://github.com/Taeyoung96/GRIL-Calib.