Skewed-redundant Hall-effect Magnetic Sensor Fusion for Perturbation-free Indoor Heading Estimation

Mojtaba Karimi,Tamay Aykut,Eckehard Steinbach,Martin Oelsch,Edwin Babaians

doi:10.1109/irc.2020.00064

Mojtaba Karimi, Tamay Aykut + Show 3 more

Open Access

https://doi.org/10.1109/irc.2020.00064

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Publication Date: Nov 1, 2020
Citations: 1	License type: cc-by

Affiliation: Technical University of Munich, Stanford University

Abstract

Robust attitude and heading estimation with respect to a known reference is an essential component for indoor localization in robotic applications. Affordable Attitude and Heading Reference Systems (AHRS) are typically using 9-axis solid-state MEMS-based sensors. The accuracy of heading estimation on such a system depends on the Earth's magnetic field measurement accuracy. The measurement of the Earth's magnetic field using MEMS-based magnetometer sensors in an indoor environment, however, is strongly affected by external magnetic perturbations. This paper presents a novel approach for robust indoor heading estimation based on skewed-redundant magnetometer fusion. A tetrahedron platform based on Hall-effect magnetic sensors is designed to determine the Earth's magnetic field with the ability to compensate for external magnetic field anomalies. Additionally, a correlation-based fusion technique is introduced for perturbation mitigation using the proposed skewed-redundant configuration. The proposed fusion technique uses a correlation coefficient analysis for determining the distorted axis and extracts the perturbation-free Earth's magnetic field vector from the redundant magnetic measurement. Our experimental results show that the proposed scheme is able to successfully mitigate the anomalies in the magnetic field measurement and estimates the Earth's true magnetic field. Using the proposed platform, we achieve a Root Mean Square Error of 12.74° for indoor heading estimation without using an additional gyroscope.

Highlights

Attitude and heading estimation is one of the fundamental requirements for robotics, human machine interaction, and navigation in indoor environments [1]–[3]
We introduce a skewed-redundant magnetometer platform based on the Hall-effect magnetic sensor principle in order to mitigate the external magnetic perturbation
In order to evaluate the heading estimation and data recording, we used two different setups: one is a stationary platform mounted on an absolute rotary shaft encoder which can be rotated while providing the absolute orientation, and the second is the mobile robot platform MAVI [57] with the ability of self-localization using Inertial-LiDAR fusion (Fig. 7)

Summary

INTRODUCTION

Attitude and heading estimation is one of the fundamental requirements for robotics, human machine interaction, and navigation in indoor environments [1]–[3]. Determining error-free and reliable heading estimation with respect to a known reference is problematic in case of indoor applications This is mainly because of different sources of errors in the MEMS-based magnetometer measurements [14]–[17]. The authors used an estimation of the expected magnetic field using the temporary rotation vector to compensate the external magnetic perturbation These approaches need a precise and simultaneous calibration of the magnetometers and the gyroscopes to be able to provide an acceptable heading estimation. The proposed approach uses a correlation-based sensor fusion method for external magnetic perturbation mitigation and robust heading estimation (Fig. 1). We introduce a skewed-redundant magnetometer platform based on the Hall-effect magnetic sensor principle in order to mitigate the external magnetic perturbation. In contrast to the naive approach or KF-based fusion, we show its superior performance, where no perturbation compensation is applied

MAGNETIC FIELD COMPONENTS AND HEADING ESTIMATION USING MAGNETOMETERS

SKEWED-REDUNDANT MAGNETOMETER PLATFORM

SKEWED-REDUNDANT MAGNETOMETER FUSION

EXPERIMENTAL RESULTS

CONCLUSIONS