Joint Magnetometer-IMU Calibration via Maximum A Posteriori Estimation
This work addresses calibration accuracy and efficiency for magnetic field-aided inertial navigation systems, representing an incremental improvement over existing methods.
The paper tackles the problem of jointly calibrating magnetometers and inertial measurement units by formulating it as a maximum a posteriori estimation problem, achieving lower root mean square error in calibration parameters and reducing position drift by more than a factor of two in real-world experiments.
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.