Indoor SLAM Using a Foot-mounted IMU and the local Magnetic Field

TL;DR

This paper presents a SLAM algorithm for indoor pedestrian tracking that combines foot-mounted IMU data with magnetic field maps, using a probabilistic filter to improve localization accuracy.

Contribution

It introduces a novel SLAM approach utilizing magnetic field anomalies and pedestrian motion patterns with a Rao-Blackwellized particle filter for indoor localization.

Findings

Effective localization in indoor environments demonstrated

Magnetic field anomalies improve position accuracy

Combines motion and magnetic maps for robust SLAM

Abstract

In this paper, a simultaneous localization and mapping (SLAM) algorithm for tracking the motion of a pedestrian with a foot-mounted inertial measurement unit (IMU) is proposed. The algorithm uses two maps, namely, a motion map and a magnetic field map. The motion map captures typical motion patterns of pedestrians in buildings that are constrained by e.g. corridors and doors. The magnetic map models local magnetic field anomalies in the environment using a Gaussian process (GP) model and uses them as position information. These maps are used in a Rao-Blackwellized particle filter (RBPF) to correct the pedestrian position and orientation estimates from the pedestrian dead-reckoning (PDR). The PDR is computed using an extended Kalman filter with zero-velocity updates (ZUPT-EKF). The algorithm is validated using real experimental sequences and the results show the efficacy of the algorithm in localizing pedestrians in indoor environments.