Towards a Factor Graph-Based Method using Angular Rates for Full Magnetometer Calibration and Gyroscope Bias Estimation

MEMS Attitude Heading Reference Systems are widely employed to determine a system’s attitude, but sensor measurement biases limit their accuracy. This paper introduces a novel factor graph-based method called MAgnetometer and GYroscope Calibration (MAGYC). MAGYC leverages three-axis angular rate measurements from an angular rate gyroscope to enhance calibration for batch and online applications. Our approach imposes less restrictive conditions for instrument movements required for calibration, eliminates the need for knowledge of the local magnetic field or instrument attitude, and facilitates integration into factor graph algorithms within Smoothing and Mapping frameworks. We evaluate the proposed methods through numerical simulations and in-field experimental assessments using a sensor installed on an underwater vehicle. Ultimately, our proposed methods reduced the underwater vehicle’s heading error standard deviation from 6.21° to 0.57° for a standard seafloor mapping survey.

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Publication Details

Published
2024-10-14
DOI
https://doi.org/10.1109/iros58592.2024.10801438
Citations
2
Primary Topic
Inertial Sensor and Navigation
Type
article
Field-Weighted Citation Impact
2.60

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article

Towards a Factor Graph-Based Method using Angular Rates for Full Magnetometer Calibration and Gyroscope Bias Estimation

Giancarlo Troni, Sebastián Rodríguez-Martínez
2 citations
Inertial Sensor and Navigation
2.60
article

Towards a Factor Graph-Based Method using Angular Rates for Full Magnetometer Calibration and Gyroscope Bias Estimation

Giancarlo Troni, Sebastián Rodríguez-Martínez
article en
2 citations

Abstract

MEMS Attitude Heading Reference Systems are widely employed to determine a system’s attitude, but sensor measurement biases limit their accuracy. This paper introduces a novel factor graph-based method called MAgnetometer and GYroscope Calibration (MAGYC). MAGYC leverages three-axis angular rate measurements from an angular rate gyroscope to enhance calibration for batch and online applications. Our approach imposes less restrictive conditions for instrument movements required for calibration, eliminates the need for knowledge of the local magnetic field or instrument attitude, and facilitates integration into factor graph algorithms within Smoothing and Mapping frameworks. We evaluate the proposed methods through numerical simulations and in-field experimental assessments using a sensor installed on an underwater vehicle. Ultimately, our proposed methods reduced the underwater vehicle’s heading error standard deviation from 6.21° to 0.57° for a standard seafloor mapping survey.

Monterey Bay Aquarium Research Institute (US)
David and Lucile Packard Foundation
Openalex Percentile: Top 8%
Inertial Sensor and Navigation
2.60
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