# Data-Driven Gyroscope Calibration

arXiv: https://arxiv.org/abs/2410.12485 · HTML: https://mlsanity.com/paper/2410.12485/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Understanding Batch Normalization](https://mlsanity.com/paper/1806.02375/) (2018)
- [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023)
- [Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions](https://mlsanity.com/paper/2307.00014/) (2023)
- [Universal Pooling -- A New Pooling Method for Convolutional Neural Networks](https://mlsanity.com/paper/1907.11440/) (2019)
- [Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation](https://mlsanity.com/paper/2210.02930/) (2022)
- [A Learning-based approach for bias elimination in low cost gyroscopes](https://mlsanity.com/paper/2210.04568/) (2022)
- [Rapid Gyroscope Calibration: A Deep Learning Approach](https://mlsanity.com/paper/2409.00488/) (2024)
- [DCNet: A Data-Driven Framework for DVL Calibration](https://mlsanity.com/paper/2410.08809/) (2024)
- [Towards Learning-Based Gyrocompassing](https://mlsanity.com/paper/2312.12121/) (2023)
- [Tightly-coupled Monocular Visual-odometric SLAM using Wheels and a MEMS Gyroscope](https://mlsanity.com/paper/1804.04854/) (2018)
- [Multiple and Gyro-Free Inertial Datasets](https://mlsanity.com/paper/2403.15494/) (2024)
- [Quadrotor Dead Reckoning with Multiple Inertial Sensors](https://mlsanity.com/paper/2310.13452/) (2023)

## Abstract (arXiv)

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.