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

Qiyang Lyu, Wei Wang, Zhenyu Wu, Hongming Shen, Huiqin Zhou, Danwei Wang

TL;DR

L2M-Calib tackles the challenge of jointly calibrating extrinsic and intrinsic parameters for magnetic-LiDAR fusion in autonomous ground vehicles. It combines a two-step Gauss-Newton extrinsic refinement with a weighted TLS intrinsic calibration (w-RRTLS) guided by a GP-based magnetic map, leveraging a prebuilt multimodal map to provide robust reference readings. The method demonstrates high calibration accuracy and robustness across simulated and real-world AGV scenarios, outperforming traditional OLS and RRTLS baselines. This one-key calibration framework enables reliable magnetic-LiDAR fusion in feature-sparse or distortion-prone environments, enhancing perception and navigation robustness in practice.

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.

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

TL;DR

L2M-Calib tackles the challenge of jointly calibrating extrinsic and intrinsic parameters for magnetic-LiDAR fusion in autonomous ground vehicles. It combines a two-step Gauss-Newton extrinsic refinement with a weighted TLS intrinsic calibration (w-RRTLS) guided by a GP-based magnetic map, leveraging a prebuilt multimodal map to provide robust reference readings. The method demonstrates high calibration accuracy and robustness across simulated and real-world AGV scenarios, outperforming traditional OLS and RRTLS baselines. This one-key calibration framework enables reliable magnetic-LiDAR fusion in feature-sparse or distortion-prone environments, enhancing perception and navigation robustness in practice.

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.

Paper Structure

This paper contains 23 sections, 17 equations, 3 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Calibration of LiDAR to other Sensors
  4. Calibration via Target-based Methods
  5. Calibration via Trajectory-based Methods.
  6. Calibration of Magetic Sensors' Distortion
  7. Orientation-independent Methods Using Magnetic Sensors Only
  8. Orientation-dependent Methods Using Auxiliary Sensors
  9. The Proposed L2M-Calib Method
  10. Extrinsic Model Formulation
  11. Prebuilt Multimodal Magnetic-LiDAR Map
  12. Magnetic Sensor Intrinsic Compensation
  13. Intrinsic Model Formulation
  14. Linear Regression under Pathological Situation
  15. Weighted Regression based on Magnetic Map
  16. ...and 8 more sections

Figures (3)

  • Figure 1: Fusing magnetic sensor with LiDAR requires both the extrinsic parameters of $\mathbf{R, t}$, and the intrinsic parameters of $\mathbf{C, H}$ describing the distortion of magnetic sensors' readings. $\mathbf{B}_\mathrm{m}$ and $\mathbf{B}_\mathrm{l}$ refers to the magnetic readings under magnetic and LiDAR sensor frame, respectively.
  • Figure 2: (a) High-fidelity simulated warehouse environment; (b)(c) The trolley and AGV platform used in real-world experiments.
  • Figure 3: Different random paths generated for collecting magnetic sensor's readings for calibration.