A Magnetic Field SLAM Algorithm Based on Bayesian Filtering and Gaussian Process Regression

This study presents a method for simultaneously localizing and mapping magnetic fields (SLAM) via unscented Kalman filter (UKF) coupled with reduced-rank Gaussian process (GP) regression with the magnetic field measurement. The goal is to enhance the efficiency and precision of magnetic field-based localization in environments with spatial variations. The approach first involves breaking down the magnetic field potential into a series of basic functions. By employing Reduced-Rank GP Regression, the representation becomes more stream-lined, leading to quicker computations and decreased storage needs. Then, two estimation techniques are compared: extended Kalman filter (EKF) and UKF filtering methods for estimating the states of the dynamic model. Simulation results indicate the effectiveness of the proposed methods in estimating the true dynamic states. Additionally, the proposed UKF design exhibits a slight improvement in accuracy at specific magnetic field length scales compared to the EKF approach.