ITSC 2025 Paper Abstract

Paper FR-EA-T43.3

LYU, QIYANG (Nanyang Technological University), Wang, Wei (Nanyang Technological University), Wu, Zhenyu (Nanyang Technological University), Shen, Hongming (Nanyang Technological University), Zhou, Huiqin (Nanyang Technological University), Wang, Danwei (Nanyang Technological University)

L2M-Calib: One-Key Calibration Method for LiDAR and Multiple Magnetic Sensors

Scheduled for presentation during the Regular Session "S43b-Multi-Sensor Fusion and Perception for Robust Autonomous Driving" (FR-EA-T43), Friday, November 21, 2025, 14:10−14:30, Stradbroke

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Advanced Sensor Fusion for Robust Autonomous Vehicle Perception, Verification of Autonomous Vehicle Sensor Systems in Real-world Scenarios

Abstract

Multimodal sensor fusion enables robust environmental perception by leveraging complementary information from heterogeneous sensing modalities. However, accurate calibration is a critical prerequisite for effective fusion. This paper proposes a novel one-key calibration framework named L2M-Calib for a fused magnetic-LiDAR system, jointly estimating the extrinsic transformation between the two kinds of sensors and the intrinsic distortion parameters of the magnetic sensors. Magnetic sensors capture ambient magnetic field (AMF) patterns, which are invariant to geometry, texture, illumination, and weather, making them suitable for challenging environments. Nonetheless, the integration of magnetic sensing into multimodal systems remains underexplored due to the absence of effective calibration techniques. To address this, we optimize extrinsic parameters using an iterative Gauss-Newton scheme, coupled with the intrinsic calibration as a weighted ridge-regularized total least squares (w-RRTLS) problem, ensuring robustness against measurement noise and ill-conditioned data. Extensive evaluations on both simulated datasets and real-world experiments, including AGV-mounted sensor configurations, demonstrate that our method achieves high calibration accuracy and robustness under various environmental and operational conditions.