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

• URL: http://arxiv.org/abs/2608.26789v1
• Date: Thu, 27 Aug 2026 08:19:06 GMT
• Title: Online Joint Calibration of Steering Offset and Planar LiDAR Extrinsics for Wheeled Mobile Robots
• Abstract summary: 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.<n> Experiments on real datasets show that correcting steering offset reduces CTE substantially, validating the effectiveness of the proposed approach.
• Score: 0.23624125155742057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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