Magnetometer calibration using inertial sensors

TL;DR

This paper introduces a practical offline algorithm for calibrating magnetometers affected by magnetic disturbances and sensor errors, improving orientation estimation when combined with inertial sensors.

Contribution

The paper presents a novel maximum likelihood-based calibration algorithm that corrects for magnetic disturbances, sensor errors, and axis misalignments, enhancing sensor fusion accuracy.

Findings

Calibrated magnetometers yield significantly better heading accuracy.

The algorithm performs well on data from commercial sensor units.

Improved orientation estimation when combining calibrated magnetometers with inertial sensors.

Abstract

In this work we present a practical algorithm for calibrating a magnetometer for the presence of magnetic disturbances and for magnetometer sensor errors. To allow for combining the magnetometer measurements with inertial measurements for orientation estimation, the algorithm also corrects for misalignment between the magnetometer and the inertial sensor axes. The calibration algorithm is formulated as the solution to a maximum likelihood problem and the computations are performed offline. The algorithm is shown to give good results using data from two different commercially available sensor units. Using the calibrated magnetometer measurements in combination with the inertial sensors to determine the sensor's orientation is shown to lead to significantly improved heading estimates.