Simultaneous localization and mapping using ambient magnetic field

Abstract

In this paper we propose a simultaneous localization and mapping (SLAM) method that utilizes local anomalies of the ambient magnetic field present in many indoor environments. We use a Rao-Blackwellized particle filter to estimate the pose distribution of the robot and Gaussian Process regression to model the magnetic field map. The feasibility of the proposed approach is validated by real world experiments, which demonstrate that the approach produces geometrically consistent maps using only odometric data and measurements obtained from the ambient magnetic field. The proposed approach provides a simple, low-cost, and space-efficient solution for solving the SLAM problem present in many domestic and swarm robotics application domains.