外观
论文arXiv 2608.267892 个月前
轮式移动机器人转向偏置与平面 LiDAR 外参的在线联合标定
原文链接：Online Joint Calibration of Steering Offset and Planar LiDAR Extrinsics for Wheeled Mobile Robots（arXiv 2608.26789 · 发布日期：2026-08-27）
Online Joint Calibration of Steering Offset and Planar LiDAR Extrinsics for Wheeled Mobile Robots
提出基于 EKF 的在线方法，在自行车运动学模型中估计转向偏置与平面 LiDAR 外参，并在真实数据集上验证。
核心要点
- 仓储轮式移动机器人中，转向感知和 LiDAR 到车体外参不准会导致路径跟踪误差增大。
- 现有转向零位常靠人工设置，LiDAR 外参多依赖 CAD 假设，维护后可能漂移。
- 方法在自行车运动学模型内使用 EKF，在线联合估计转向偏置和平面 LiDAR 外参。
- 真实数据集实验表明，校正转向偏置可显著降低横向跟踪误差。
验证范围与局限：验证基于真实数据集，摘要未说明具体机器人数量或任务类型。
英文摘要 (Abstract)
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.
作者：Subodh Mishra, Arindam Dhar, Suprotim Majumdar, Naveen Arulselvan
来源
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