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This paper introduces RoSLAC, a novel approach for robust simultaneous localization and calibration of multiple magnetometers on autonomous mobile robots (AMRs) in GPS-denied environments. RoSLAC uses an alternating optimization scheme to iteratively estimate both the robot's pose and the magnetometer calibration parameters, addressing the challenge of magnetometer distortions caused by ferromagnetic materials on the robot platform. Experimental results in both simulated and real-world environments demonstrate that RoSLAC achieves high localization accuracy with low computational cost compared to existing magnetometer calibration techniques.
AMRs can now navigate reliably indoors without GPS or external infrastructure, thanks to a new method that simultaneously calibrates magnetometers and estimates robot pose.
Localization of autonomous mobile robots (AMRs) in enclosed or semi-enclosed environments such as offices, hotels, hospitals, indoor parking facilities, and underground spaces where GPS signals are weak or unavailable remains a major obstacle to the deployment of fully autonomous systems. Infrastructure-based localization approaches, such as QR codes and RFID, are constrained by high installation and maintenance costs as well as limited flexibility, while onboard sensor-based methods, including LiDAR- and vision-based solutions, are affected by ambiguous geometric features and frequent occlusions caused by dynamic obstacles such as pedestrians. Ambient magnetic field (AMF)-based localization has therefore attracted growing interest in recent years because it does not rely on external infrastructure or geometric features, making it well-suited for AMR applications such as service robots and security robots. However, magnetometer measurements are often corrupted by distortions caused by ferromagnetic materials present on the sensor platform, which bias the AMF and degrade localization reliability. As a result, accurate magnetometer calibration to estimate distortion parameters becomes essential. Conventional calibration methods that rely on rotating the magnetometer are impractical for large and heavy platforms. To address this limitation, this paper proposes a robust simultaneous localization and calibration (RoSLAC) approach based on alternating optimization, which iteratively and efficiently estimates both the platform pose and magnetometer calibration parameters. Extensive evaluations conducted in high-fidelity simulation and real-world environments demonstrate that the proposed RoSLAC method achieves high localization accuracy while maintaining low computational cost compared with state-of-the-art magnetometer calibration techniques.
