Data-Driven Gyroscope Calibration
This work addresses calibration inefficiencies for low-cost gyroscopes in applications requiring rapid setup, though it is incremental as it builds on existing calibration methods.
The paper tackled the problem of calibrating low-cost gyroscopes by proposing a data-driven framework to estimate scale factor and bias, resulting in a 72% improvement in accuracy and a 75% reduction in calibration time to just seconds.
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.