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

2025-12-01

AI Summary

Existing LiDAR–magnetometer fusion systems lack efficient, unified calibration methods for jointly estimating extrinsic parameters between LiDAR and magnetometers and intrinsic distortion parameters of magnetometers. Method: This paper proposes a one-click cross-modal joint calibration framework that simultaneously optimizes LiDAR–magnetometer extrinsics and magnetometer intrinsics. Leveraging the spatial invariance of ambient magnetic fields, it establishes cross-modal geometric constraints. Extrinsic parameters are refined via Gauss–Newton optimization, while intrinsic parameters are estimated using a weighted ridge-regularized total least squares (w-RRTLS) solver, incorporating adaptive weighting and Tikhonov regularization to ensure numerical stability and robustness against measurement noise and ill-conditioning. Results: Extensive experiments in both simulated and real-world AGV environments demonstrate sub-degree extrinsic calibration accuracy and high-fidelity intrinsic parameter estimation. The method significantly enhances the robustness and generalizability of magnetic–LiDAR fusion systems in complex environments and provides the first engineering-deployable, one-click multimodal calibration solution.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Calibrates LiDAR and multiple magnetic sensors for fusion
• Estimates extrinsic and intrinsic parameters for magnetic sensors
• Ensures robustness in challenging environmental conditions

Innovation

• One-key calibration for LiDAR and magnetic sensors
• Joint extrinsic and intrinsic parameter estimation
• Robust optimization using w-RRTLS and Gauss-Newton