Full Magnetometer and Gyroscope Bias Estimation using Angular Rates: Theory and Experimental Evaluation of a Factor Graph-Based Approach

TL;DR

This paper presents a novel factor graph-based method called MAGYC for calibrating magnetometers and gyroscopes using angular rate measurements, improving attitude estimation accuracy in challenging environments.

Contribution

It introduces a new calibration approach that requires fewer movement restrictions and no prior magnetic field knowledge, with both batch and online implementations.

Findings

Reduced underwater vehicle position error from 10% to 0.5%.

Validated through simulations and field experiments.

Facilitates integration into existing factor graph frameworks.

Abstract

Despite their widespread use in determining system attitude, Micro-Electro-Mechanical Systems (MEMS) Attitude and Heading Reference Systems (AHRS) are limited by sensor measurement biases. This paper introduces a method called MAgnetometer and GYroscope Calibration (MAGYC), leveraging three-axis angular rate measurements from an angular rate gyroscope to estimate both the hard- and soft-iron biases of magnetometers as well as the bias of gyroscopes. We present two implementation methods of this approach based on batch and online incremental factor graphs. Our method imposes fewer restrictions on instrument movements required for calibration, eliminates the need for knowledge of the local magnetic field magnitude or instrument's attitude, and facilitates integration into factor graph algorithms for Smoothing and Mapping frameworks. We validate the proposed methods through numerical simulations and in-field experimental evaluations with a sensor onboard an underwater vehicle. By implementing the proposed method in field data of a seafloor mapping dive, the dead reckoning-based position estimation error of the underwater vehicle was reduced from 10% to 0.5% of the distance traveled.