Data-Driven Gyroscope Calibration

TL;DR

This paper introduces a data-driven method for gyroscope calibration that significantly reduces calibration time and improves accuracy compared to traditional model-based approaches, especially for low-cost sensors.

Contribution

A novel data-driven framework for gyroscope calibration that outperforms existing model-based methods in accuracy and speed, validated on a new dataset.

Findings

72% improvement in scale factor and bias estimation accuracy

Calibration time reduced by 75%, from minutes to seconds

Validated on a 56-minute dataset recorded with a turntable

Abstract

Gyroscopes are inertial sensors that measure the angular velocity of the platforms to which they are attached. To estimate the gyroscope deterministic error terms prior mission start, a calibration procedure is performed. When considering low-cost gyroscopes, the calibration requires a turntable as the gyros are incapable of sensing the Earth turn rate. In this paper, we propose a data-driven framework to estimate the scale factor and bias of a gyroscope. To train and validate our approach, a dataset of 56 minutes was recorded using a turntable. We demonstrated that our proposed approach outperforms the model-based approach, in terms of accuracy and convergence time. Specifically, we improved the scale factor and bias estimation by an average of 72% during six seconds of calibration time, demonstrating an average of 75% calibration time improvement. That is, instead of minutes, our approach requires only several seconds for the calibration.