RoSLAC: Robust Simultaneous Localization and Calibration of Multiple Magnetometers

2026-04-15

AI Summary

This work addresses the challenge of magnetic distortion caused by ferromagnetic materials on mobile robots operating in GPS-denied indoor environments, which severely degrades magnetometer-based localization. Conventional calibration methods require deliberate sensor rotation and are impractical for large, heavy platforms. To overcome this limitation, the authors propose RoSLAC (Robust Simultaneous Localization and Calibration), a novel framework that jointly estimates robot pose and multi-magnetometer distortion parameters without imposing motion constraints on the platform. By integrating geomagnetic map matching with a multi-magnetometer observation model and employing robust loss functions to suppress outliers, RoSLAC enables online joint calibration and high-precision localization for the first time. Experimental results demonstrate that RoSLAC achieves centimeter-level accuracy in both simulated and real-world scenarios with low computational overhead, significantly outperforming existing approaches.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• Simultaneous Localization and Calibration
• Magnetometer Distortion
• Autonomous Mobile Robots
• Indoor Localization
• Ambient Magnetic Field

Innovation

• simultaneous localization and calibration
• magnetometer calibration
• alternating optimization
• ambient magnetic field
• autonomous mobile robots