Magnetic Field SLAM Approach based on Unscented Kalman Filter and Physics Informed Neural Network

This study introduces a method for magnetic field-based Simultaneous Localization and Mapping (SLAM), utilizing an Unscented Kalman Filter (UKF) in combination with a Physics-Informed Neural Network (PINN) model. The aim is to improve the accuracy and efficiency of magnetic field-based localization in environments with spatially varying magnetic characteristics. The proposed approach leverages an offline training of a PINN to model the magnetic field by embedding physical constraints directly into the learning process, resulting in a compact and computationally efficient representation. The dynamic system states are then estimated using UKF design. Simulation results validate the effectiveness of the proposed approach in accurately estimating the true system states, with considerable improvement on the localization and tracking accuracy compared to the current state of the art approaches.