Inertial Magnetic SLAM Systems Using Low-Cost Sensors

TL;DR

This paper introduces low-cost inertial magnetic SLAM systems that use inexpensive sensors for 3D indoor positioning, demonstrating their feasibility and accuracy in real-world environments.

Contribution

It proposes loosely and tightly coupled IM-SLAM systems using only IMU, magnetometers, and a barometer, eliminating the need for high-cost sensors or visual odometry.

Findings

Tightly coupled IM-SLAM achieves lower positioning errors than loosely coupled.

The systems operate effectively in multi-floor indoor environments.

Typical errors are on the order of meters per 100 meters traveled.

Abstract

Spatially inhomogeneous magnetic fields offer a valuable, non-visual information source for positioning. Among systems leveraging this, magnetic field-based simultaneous localization and mapping (SLAM) systems are particularly attractive. These systems execute positioning and magnetic field mapping tasks simultaneously, and they have bounded positioning error within previously visited regions. However, state-of-the-art magnetic-field SLAM methods typically require low-drift odometry data provided by visual odometry, a wheel encoder, or pedestrian dead-reckoning technology. To address this limitation, this work proposes loosely coupled and tightly coupled inertial magnetic SLAM (IM-SLAM) systems, which use only low-cost sensors: an inertial measurement unit (IMU), 30 magnetometers, and a barometer. Both systems are based on a magnetic-field-aided inertial navigation system (INS) and use error-state Kalman filters for state estimation. The key difference between the two systems is whether the navigation state estimation is done in one or two steps. These systems are evaluated in real-world indoor environments with multi-floor structures. The results of the experiment show that the tightly coupled IM-SLAM system achieves lower positioning errors than the loosely coupled system in most scenarios, with typical errors on the order of meters per 100 meters traveled. These results demonstrate the feasibility of developing a full 3D IM-SLAM system using low-cost sensors. A potential application of the proposed systems is for the positioning of emergency response officers.