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Targetless IMU-LiDAR extrinsic calibration methods are gaining significantattention as the importance of the IMU-LiDAR fusion system increases. Notably,existing calibration methods derive calibration parameters under the assumptionthat the methods require full motion in all axes. When IMU and LiDAR aremounted on a ground robot the motion of which is restricted to planar motion,existing calibration methods are likely to exhibit degraded performance. Toaddress this issue, we present GRIL-Calib: a novel targetless Ground RobotIMU-LiDAR Calibration method. Our proposed method leverages ground informationto compensate for the lack of unrestricted full motion. First, we propose LiDAROdometry (LO) using ground plane residuals to enhance calibration accuracy.Second, we propose the Ground Plane Motion (GPM) constraint and incorporate itinto the optimization for calibration, enabling the determination of full 6-DoFextrinsic parameters, including theoretically unobservable direction. Finally,unlike baseline methods, we formulate the calibration not as sequential twooptimizations but as a single optimization (SO) problem, solving allcalibration parameters simultaneously and improving accuracy. We validate our\textit{GRIL-Calib} by applying it to three public real-world datasets andcomparing its performance with that of existing state-of-the-art methods interms of accuracy and robustness. Our code is available athttps://github.com/Taeyoung96/GRIL-Calib.

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