Mobile robots equipped with multiple light detection and ranging (LiDARs) and capable of recognizing their surroundings are increasing due to the minitualization and cost reduction of LiDAR. This paper proposes a target-less extrinsic calibration method of multiple LiDARs with non-overlapping field of view (FoV). The proposed method uses accumulated point clouds of floor plane and objects while in motion. It enables accurate calibration with challenging configuration of LiDARs that directed towards the floor plane, caused by biased feature values. Additionally, the method includes a noise removal module that considers the scanning pattern to address bleeding points, which are noises of significant source of error in point cloud alignment using high-density LiDARs. Evaluations through simulation demonstrate that the proposed method achieved higher accuracy extrinsic calibration with two and four LiDARs than conventional methods, regardless type of objects. Furthermore, the experiments using a real mobile robot has shown that our proposed noise removal module can eliminate noise more precisely than conventional methods, and the estimated extrinsic parameters have successfully created consistent 3D maps.

翻译：随着激光雷达的小型化与成本降低，配备多个激光雷达且具备环境感知能力的移动机器人日益增多。本文提出一种面向视场非重叠多激光雷达的无目标外部标定方法。该方法利用运动过程中累积的地面平面与物体点云数据，能够针对因特征值偏差导致激光雷达朝向地面方向的挑战性配置实现精准标定。此外，方法包含考虑扫描模式的噪声去除模块，用于处理高密度激光雷达点云对齐中显著误差源的拖尾噪声。仿真评估表明，所提方法在双激光雷达与四激光雷达配置下，无论物体类型如何，均能实现优于传统方法的外部标定精度。进一步基于真实移动机器人的实验显示，所提出的噪声去除模块较传统方法能更精准地消除噪声，且估计的外部参数成功生成了具有一致性的三维地图。