Saying goodbyes to rotating your phone: Magnetometer calibration during SLAM

TL;DR

This paper demonstrates that magnetometer calibration can be integrated into SLAM processes without prior map data, using a factorized particle filter, achieving accuracy comparable to manual calibration and enhancing existing methods.

Contribution

It introduces a novel SLAM-based calibration method that eliminates the need for pre-collected maps and manual procedures, streamlining magnetometer calibration during navigation.

Findings

Calibration during SLAM achieves comparable accuracy to manual calibration.

The method slightly improves manual calibration when combined.

Validated on smartphone and robotics data in real environments.

Abstract

While Wi-Fi positioning is still more common indoors, using magnetic field features has become widely known and utilized as an alternative or supporting source of information. Magnetometer bias presents significant challenge in magnetic field navigation and SLAM. Traditionally, magnetometers have been calibrated using standard sphere or ellipsoid fitting methods and by requiring manual user procedures, such as rotating a smartphone in a figure-eight shape. This is not always feasible, particularly when the magnetometer is attached to heavy or fast-moving platforms, or when user behavior cannot be reliably controlled. Recent research has proposed using map data for calibration during positioning. This paper takes a step further and verifies that a pre-collected map is not needed; instead, calibration can be done as part of a SLAM process. The presented solution uses a factorized particle filter that factors out calibration in addition to the magnetic field map. The method is validated using smartphone data from a shopping mall and mobile robotics data from an office environment. Results support the claim that magnetometer calibration can be achieved during SLAM with comparable accuracy to manual calibration. Furthermore, the method seems to slightly improve manual calibration when used on top of it, suggesting potential for integrating various calibration approaches.