Saying Goodbyes to Rotating Your Phone: Magnetometer Calibration During SLAM—Extended Version

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 a 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 both indoors and outdoors by using smartphone data from a shopping mall, a university campus building, and an urban outdoor block, alongside mobile robotics data from an office and apartment building environments. Results support the claim that magnetometer calibration can be achieved during SLAM with comparable accuracy to manual calibration. This is supported by analyzing the positioning performance and map consistency, both of which produce results that are almost identical to the rotation-based calibration reported by the operating system.