# Rapid Gyroscope Calibration: A Deep Learning Approach

arXiv: https://arxiv.org/abs/2409.00488 · HTML: https://mlsanity.com/paper/2409.00488/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 31 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions](https://mlsanity.com/paper/2307.00014/) (2023)
- [Data-Driven Denoising of Stationary Accelerometer Signals](https://mlsanity.com/paper/2206.05937/) (2022)
- [Information Aided Navigation: A Review](https://mlsanity.com/paper/2301.01114/) (2023)
- [A Learning-based approach for bias elimination in low cost gyroscopes](https://mlsanity.com/paper/2210.04568/) (2022)
- [A Lightweight and Accurate Localization Algorithm Using Multiple Inertial Measurement Units](https://mlsanity.com/paper/1909.04869/) (2019)
- [A Unified Filter for Fusion of Multiple Inertial Measurement Units](https://mlsanity.com/paper/2208.14524/) (2022)
- [An Efficient Calibration Method for Triaxial Gyroscope](https://mlsanity.com/paper/2103.11096/) (2021)

## Built on by (most cited first, 4 of 4)

- [Data-Driven Gyroscope Calibration](https://mlsanity.com/paper/2410.12485/) (2024)
- [Orientation-Free Neural Network-Based Bias Estimation for Low-Cost Stationary Accelerometers](https://mlsanity.com/paper/2511.13071/) (2025)
- [ICD-Net: Inertial Covariance Displacement Network for Drone Visual-Inertial SLAM](https://mlsanity.com/paper/2512.00037/) (2025)
- [Neural-Assisted in-Motion Self-Heading Alignment](https://mlsanity.com/paper/2604.00168/) (2026)

## Abstract (arXiv)

Low-cost gyroscope calibration is essential for ensuring the accuracy and reliability of gyroscope measurements. Stationary calibration estimates the deterministic parts of measurement errors. To this end, a common practice is to average the gyroscope readings during a predefined period and estimate the gyroscope bias. Calibration duration plays a crucial role in performance, therefore, longer periods are preferred. However, some applications require quick startup times and calibration is therefore allowed only for a short time. In this work, we focus on reducing low-cost gyroscope calibration time using deep learning methods. We propose an end-to-end convolutional neural network for the application of gyroscope calibration. We explore the possibilities of using multiple real and virtual gyroscopes to improve the calibration performance of single gyroscopes. To train and validate our approach, we recorded a dataset consisting of 186.6 hours of gyroscope readings, using 36 gyroscopes of four different brands. We also created a virtual dataset consisting of simulated gyroscope readings. The six datasets were used to evaluate our proposed approach. One of our key achievements in this work is reducing gyroscope calibration time by up to 89% using three low-cost gyroscopes. Our dataset is publicly available to allow reproducibility of our work and to increase research in the field.