轮式移动机器人的转向偏移与平面激光雷达外参的在线联合校准

中文摘要 AI 辅助

准确的转向传感与激光雷达（LiDAR）到车辆的外参对仓库移动机器人（WMR）的可靠路径跟踪至关重要；校准误差常导致蛇形运动、摆动及增大的横向跟踪误差（CTE）。实际应用中，转向“零点”通常手动设置（如通过PS4游戏摇杆目测直线度），而激光雷达外参多取自CAD模型，维护后可能发生漂移。这类静态手动流程常导致安全关键环境下的校准误差。本文提出一种基于扩展卡尔曼滤波（EKF）的方法，在自行车运动学模型内在线估计转向偏移与平面激光雷达外参，为手动校准提供了有原则的替代方案。真实数据集上的实验表明，校正转向偏移可大幅降低CTE，验证了所提方法的有效性。

英文摘要

Accurate steering sensing and LiDAR-to-vehicle extrinsics are crucial for reliable path tracking in warehouse mobile robots (WMRs); miscalibration often leads to snaking, weaving, and elevated cross-track error (CTE). In practice, steering ``zero'' is commonly set manually (e.g., eyeballing straightness via a PS4 joystick), while LiDAR extrinsics are assumed from CAD and may drift after maintenance. Such static, manual procedures frequently cause miscalibration in safety-critical environments. This paper presents an Extended Kalman Filter (EKF)--based method for online estimation of steering offset and planar LiDAR extrinsics within a bicycle-kinematics model, providing a principled alternative to manual calibration. Experiments on real datasets show that correcting steering offset reduces CTE substantially, validating the effectiveness of the proposed approach.

发表机构

• ATI Motors

机构由 AI 辅助整理，请以论文原文为准。