MAINS: A Magnetic Field Aided Inertial Navigation System for Indoor Positioning

TL;DR

The paper tackles indoor localization in GNSS-denied environments where the indoor magnetic field is inhomogeneous. It proposes MAINS, a tightly coupled system that fuses inertial measurements with a magnetometer array using a polynomial magnetic-field model and an error-state Kalman filter to dramatically reduce drift. The approach is backed by a thorough derivation of the magnetic-field model, transformation between body frames, and an adaptive noise strategy, plus real-world experiments showing substantial accuracy gains over stand-alone INS and competitive performance against state-of-the-art magnetic-field methods, especially when using larger magnetometer arrays. The results indicate MAINS can keep position errors under a few meters for extended runs and offers flexible sensor configurations, making it a promising solution for magnetic-field SLAM and dense indoor navigation.

Abstract

A Magnetic field Aided Inertial Navigation System (MAINS) for indoor navigation is proposed in this paper. MAINS leverages an array of magnetometers to measure spatial variations in the magnetic field, which are then used to estimate the displacement and orientation changes of the system, thereby aiding the inertial navigation system (INS). Experiments show that MAINS significantly outperforms the stand-alone INS, demonstrating a remarkable two orders of magnitude reduction in position error. Furthermore, when compared to the state-of-the-art magnetic-field-aided navigation approach, the proposed method exhibits slightly improved horizontal position accuracy. On the other hand, it has noticeably larger vertical error on datasets with large magnetic field variations. However, one of the main advantages of MAINS compared to the state-of-the-art is that it enables flexible sensor configurations. The experimental results show that the position error after 2 minutes of navigation in most cases is less than 3 meters when using an array of 30 magnetometers. Thus, the proposed navigation solution has the potential to solve one of the key challenges faced with current magnetic-field simultaneous localization and mapping (SLAM) solutions: the very limited allowable length of the exploration phase during which unvisited areas are mapped.

Figures (7)

• Figure 1: Illustration of the magnetic-field magnitude variations inside a building. The field near the floor was measured with a magnetometer, whose location was tracked by camera-based tracking systems. The field measurement was then interpolated, and the field magnitude was projected on the floor.
• Figure 2: The sensor board used in the experiment. It has 30 PNI https://www.pnicorp.com/rm3100/ magnetometers and an Osmium MIMU 4844 IMU mounted on the bottom side.
• Figure 3: A 2D illustration of the geometric relationship between the body frames at two consecutive times. The applicable region $\Omega$ of the magnetic field model at time $k$ is in blue, and the black dot indicates the location where the two models output the corresponding magnetic field in their coordinate frames.
• Figure 4: The flowchart of the state estimation algorithm.
• Figure 5: Sensor configurations used in the experiments. Left: Square configuration. Right: Rectangular configuration.