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.

翻译：随着IMU-LiDAR融合系统的重要性日益提升，无靶标IMU-LiDAR外参标定方法正受到广泛关注。值得注意的是，现有标定方法在推导标定参数时，均假设系统需具备全轴系的完整运动。当IMU与LiDAR安装于运动受限于平面运动的地面机器人时，现有标定方法的性能很可能出现退化。为解决此问题，我们提出GRIL-Calib：一种新颖的无靶标地面机器人IMU-LiDAR标定方法。该方法利用地面信息来补偿非受限完整运动的缺失。首先，我们提出采用地面平面残差的激光雷达里程计（LO）以提升标定精度。其次，我们提出地面平面运动（GPM）约束并将其纳入标定优化过程，从而能够确定完整的6自由度外参，包括理论上不可观测的方向。最后，与基线方法不同，我们将标定问题构建为单一优化（SO）问题而非顺序两次优化，同步求解所有标定参数并提升精度。我们通过在多种真实世界数据集上应用GRIL-Calib，并与现有先进方法在精度和鲁棒性方面进行性能对比，验证了本方法的有效性。代码开源地址：https://github.com/Taeyoung96/GRIL-Calib。