S-GPR: Sliding Gaussian Process Regression-based Magnetic Mapping and Evaluation of Different Magnetic Mapping Methods

The localization of autonomous robots in modern enclosed or semi-enclosed environments, such as office/hotel/hospital, supermarket, and indoor car park environments where GPS signals are severely challenged, remains a bottleneck for the deployment of fully autonomous mobile systems. Existing infrastructure-based (e.g., QR codes, RFID) localization methods are troubled by high maintenance cost and inflexibility issues, while onboard sensors-based solutions (e.g., LiDAR/camera-based) suffer from the ambiguous geometric features and view obstructions from crowded dynamic obstacles (e.g., pedestrians). Magnetic field (MF)-based localization has been gradually utilized in recent years due to its independence from positioning infrastructures and geometric features, thus making it ideal for applications such as service robots and security robots. Magnetic map building serves as the basis and prerequisite component for MF-based localization tasks. The well-acknowledged Gaussian Process Regression (GPR) method can be implemented to build magnetic maps but with heavy computational burdens. Thus in this paper, we propose an efficient and accurate magnetic mapping system based on a novel Sliding-GPR (i.e., S-GPR) method, and evaluate different magnetic mapping methods. A unique region-of-interest (ROI) selection technique and a down/up-sampling method are proposed for the S-GPR to dramatically decrease the computational time while maintaining the mapping accuracy. Extensive experiments in a high-fidelity simulated warehouse and real-world car park environments show that our proposed S-GPR mapping method has exhibited the highest accuracy and relatively low computational time compared with the SOTA magnetic mapping methods.