Autonomous systems often employ multiple LiDARs to leverage the integrated advantages, enhancing perception and robustness. The most critical prerequisite under this setting is the estimating the extrinsic between each LiDAR, i.e., calibration. Despite the exciting progress in multi-LiDAR calibration efforts, a universal, sensor-agnostic calibration method remains elusive. According to the coarse-to-fine framework, we first design a spherical descriptor TERRA for 3-DoF rotation initialization with no prior knowledge. To further optimize, we present JEEP for the joint estimation of extrinsic and pose, integrating geometric and motion information to overcome factors affecting the point cloud registration. Finally, the LiDAR poses optimized by the hierarchical optimization module are input to time syn- chronization module to produce the ultimate calibration results, including the time offset. To verify the effectiveness, we conduct extensive experiments on eight datasets, where 16 diverse types of LiDARs in total and dozens of calibration tasks are tested. In the challenging tasks, the calibration errors can still be controlled within 5cm and 1{\deg} with a high success rate.

翻译：自主系统常采用多激光雷达以利用集成优势，增强感知能力和鲁棒性。在此设定下，最关键的前提是估计每个激光雷达之间的外参，即标定。尽管多激光雷达标定工作取得了令人瞩目的进展，但一种通用的、不依赖于传感器的标定方法仍未实现。依据从粗到细的框架，我们首先设计了一种球形描述子TERRA，用于在无先验知识的情况下实现3自由度旋转初始化。为进一步优化，我们提出了JEEP方法，用于外参和位姿的联合估计，融合几何与运动信息以克服影响点云配准的因素。最后，将由分层优化模块优化后的激光雷达位姿输入时间同步模块，生成最终标定结果，包括时间偏移。为验证有效性，我们在八个数据集上进行了大量实验，共测试了16种不同类型的激光雷达及数十项标定任务。在具有挑战性的任务中，标定误差仍能控制在5厘米和1度以内，且成功率较高。