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arxiv: 2505.16662 · v4 · submitted 2025-05-22 · 💻 cs.RO · eess.SP

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Joint Magnetometer-IMU Calibration via Maximum A Posteriori Estimation

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classification 💻 cs.RO eess.SP
keywords methodproposedcalibrationcalibratedmagnetometer-imuparametersstate-of-the-artaccuracy
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This paper presents a new method for jointly calibrating a magnetometer and inertial measurement unit (IMU), focusing on balancing calibration accuracy and computational efficiency. The proposed method is based on a maximum a posteriori estimation framework, treating both the calibration parameters and orientation trajectory of the sensors as unknowns. This method enables efficient optimization of the calibration parameters using analytically derived derivatives. The performance of the proposed method is compared against that of two state-of-the-art methods. Simulation results demonstrate that the proposed method achieves the lowest root mean square error in calibration parameters, increasing the calibration accuracy by 20-30%, while maintaining competitive computational efficiency. Further validation through real-world experiments confirms the practical benefits of the proposed method. The proposed method calibrated 30 magnetometer-IMU pairs in under two minutes on a consumer-grade laptop, which is one order of magnitude faster than the most accurate state-of-the-art algorithm as implemented in this work. Moreover, when calibrated using the proposed method, a magnetic-field-aided inertial navigation system achieved positioning performance comparable to when it is calibrated with the state-of-the-art method. These results demonstrate that the proposed method is a reliable and effective choice for jointly calibrating magnetometer-IMU pairs.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. SL(C)AMma: Simultaneous Localisation, (Calibration) and Mapping With a Magnetometer Array

    cs.RO 2026-04 unverdicted novelty 6.0

    Magnetometer-array SLAM with optional joint calibration delivers accurate indoor trajectories and over 80% drift reduction versus single-sensor or pure integration baselines on datasets where prior magnetic SLAM fails.