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This paper introduces an Extended Kalman Filter (EKF) method for the online calibration of steering offset and planar LiDAR extrinsics in wheeled mobile robots (WMRs), addressing the common issue of miscalibration that leads to path tracking errors. By integrating this method into a bicycle-kinematics model, the authors demonstrate a significant reduction in cross-track error (CTE) during real-world experiments, showcasing the effectiveness of automated calibration over traditional manual methods. The findings highlight the potential for improved reliability in safety-critical robotic applications, where precise navigation is essential.
Steering miscalibration can lead to dangerous path tracking errors, but an EKF-based online calibration method significantly reduces these errors in real-world mobile robots.
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.
