Summary of Rapid Gyroscope Calibration: a Deep Learning Approach, by Yair Stolero and Itzik Klein
Rapid Gyroscope Calibration: A Deep Learning Approach
by Yair Stolero, Itzik Klein
First submitted to arxiv on: 31 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel deep-learning framework is presented for reducing the calibration time of low-cost gyroscopes, which is crucial for ensuring accurate and reliable measurements. The approach utilizes multiple real and virtual gyroscopes to improve the calibration performance of single gyroscopes. A dataset consisting of 169 hours of gyroscope readings from 24 gyroscopes of two different brands was recorded and used to train and validate the proposed method. Additionally, a virtual dataset of simulated gyroscope readings was created to further evaluate the approach. The results show that the framework can reduce calibration time by up to 89% using three low-cost gyroscopes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low-cost gyroscopes are important for many devices, but they need to be calibrated to work well. Calibration makes sure the measurements are accurate and reliable. One way to do this is to average the readings over a long period of time. However, some devices don’t have time for that, so we need a faster way to calibrate them. This paper uses deep learning to come up with a new way to calibrate gyroscopes quickly. It’s like using multiple sensors at once to get a better reading. The team recorded lots of data from different gyroscopes and used it to test their approach. They were able to reduce the calibration time by as much as 89%! |
Keywords
» Artificial intelligence » Deep learning
