Online Joint Calibration of Steering Offset and Planar LiDAR Extrinsics for Wheeled Mobile Robots

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.

Robotics (cs.RO)

arXiv:2608.26789 [cs.RO]

(or arXiv:2608.26789v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.26789 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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