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 \textit{GRIL-Calib} by applying it to three public 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-激光雷达融合系统重要性的日益提升，无靶标IMU-激光雷达外参标定方法正获得广泛关注。值得注意的是，现有标定方法均基于需要全轴运动这一假设而推导标定参数。当IMU与激光雷达安装于运动受限为平面运动的地面机器人时，现有标定方法性能易出现退化。针对此问题，我们提出GRIL-Calib：一种新颖的无靶标地面机器人IMU-激光雷达标定方法。本方法利用地面信息补偿自由全运动缺失带来的不足。首先，我们提出基于地面平面残差的激光雷达里程计（LO）以提升标定精度；其次，构建地面平面运动（GPM）约束并融入标定优化过程，实现包含理论不可观方向在内的完整六自由度外参确定；最后，与基准方法不同，我们将标定问题构建为单一优化（SO）问题而非顺序双优化问题，通过同步求解所有标定参数提升精度。我们在三个公开真实数据集上验证了所提出的GRIL-Calib方法，并与现有最优方法在精度与鲁棒性方面进行了性能对比。本方法代码开源在https://github.com/Taeyoung96/GRIL-Calib。