在线联合校准轮式机器人转向偏移与激光雷达外参，提升路径跟踪精度。
Online Joint Calibration of Steering Offset and Planar LiDAR Extrinsics for Wheeled Mobile Robots
- 基于自行车运动模型与扩展卡尔曼滤波，实时估计转向偏移和激光雷达平面外参。
- 实测数据表明校正转向偏移后横向误差显著降低。
- 适合需要高精度路径跟踪的仓储机器人场景，尤其适用于维护后重新校准。
精准的转向感知与激光雷达到车体的外参对于仓库移动机器人（WMRs）可靠路径跟踪至关重要；校准偏差常导致蛇形轨迹、晃动及横向误差（CTE）升高。实际应用中，转向零点通常手动设定（如通过手柄目视对齐），而激光雷达外参则依赖CAD假设，维护后可能漂移。此类静态、人工校准方法在安全关键环境中易引发偏差。本文提出一种基于扩展卡尔曼滤波（EKF）的方法，在自行车运动模型框架下实现转向偏移与平面激光雷达外参的在线联合估计，提供比手动校准更可靠的替代方案。真实数据集实验表明，修正转向偏移可显著降低横向误差（CTE），验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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