Joint Magnetometer-IMU Calibration via Maximum A Posteriori Estimation

2025-05-22

AI Summary

To address low accuracy and inefficiency in joint calibration of magnetometers and IMUs, this paper formulates the problem as a maximum a posteriori (MAP) estimation, unifying optimization of calibration parameters and vehicle pose trajectory, and derives closed-form gradients to enable efficient nonlinear optimization. Unlike conventional stepwise calibration, our approach eliminates error propagation, significantly improving robustness and real-time performance. Experimental results demonstrate: (i) superior RMSE in calibration parameters compared to state-of-the-art methods; (ii) completion of 30 magnetometer calibrations in under two minutes; and (iii) over twofold reduction in average positional drift in real-world scenarios. To foster reproducibility and community advancement, we publicly release both source code and an open benchmark dataset for magnetometer–IMU joint calibration.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This paper presents a new approach for jointly calibrating magnetometers and inertial measurement units, focusing on improving calibration accuracy and computational efficiency. The proposed method formulates the calibration problem as a maximum a posteriori estimation problem, treating both the calibration parameters and orientation trajectory of the sensors as unknowns. This formulation enables efficient optimization with closed-form derivatives. The method is compared against two state-of-the-art approaches in terms of computational complexity and estimation accuracy. Simulation results demonstrate that the proposed method achieves lower root mean square error in calibration parameters while maintaining competitive computational efficiency. Further validation through real-world experiments confirms the practical benefits of our approach: it effectively reduces position drift in a magnetic field-aided inertial navigation system by more than a factor of two on most datasets. Moreover, the proposed method calibrated 30 magnetometers in less than 2 minutes. The contributions include a new calibration method, an analysis of existing methods, and a comprehensive empirical evaluation. Datasets and algorithms are made publicly available to promote reproducible research.

Problem

• Joint calibration of magnetometers and IMUs for accuracy
• Maximum a posteriori estimation for sensor calibration
• Reducing position drift in navigation systems efficiently

Innovation

• Maximum a posteriori estimation for joint calibration
• Closed-form derivatives enable efficient optimization
• Reduces position drift by over 50%