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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2403.02618 |
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| _version_ | 1866917637985927168 |
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| author | Chao, Cui Jiankang, Zhao |
| author_facet | Chao, Cui Jiankang, Zhao |
| contents | This paper presents a learning-based method for calibrating and denoising microelectromechanical system (MEMS) gyroscopes, which is designed based on a convolutional network, and only contains hundreds of parameters, so the network can be trained on a graphics processing unit (GPU) before being deployed on a microcontroller unit (MCU) with limited computational resources. In this method, the neural network model takes only the raw measurements from the gyroscope as input values, and handles the calibration and noise reduction tasks separately to ensure interpretability. The proposed method is validated on public datasets and real-world experiments, without relying on a specific dataset for training in contrast to existing learning-based methods. The experimental results demonstrate the practicality and effectiveness of the proposed method, suggesting that this technique is a viable candidate for applications that require IMUs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02618 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TinyGC-Net: An Extremely Tiny Network for Calibrating MEMS Gyroscopes Chao, Cui Jiankang, Zhao Robotics This paper presents a learning-based method for calibrating and denoising microelectromechanical system (MEMS) gyroscopes, which is designed based on a convolutional network, and only contains hundreds of parameters, so the network can be trained on a graphics processing unit (GPU) before being deployed on a microcontroller unit (MCU) with limited computational resources. In this method, the neural network model takes only the raw measurements from the gyroscope as input values, and handles the calibration and noise reduction tasks separately to ensure interpretability. The proposed method is validated on public datasets and real-world experiments, without relying on a specific dataset for training in contrast to existing learning-based methods. The experimental results demonstrate the practicality and effectiveness of the proposed method, suggesting that this technique is a viable candidate for applications that require IMUs. |
| title | TinyGC-Net: An Extremely Tiny Network for Calibrating MEMS Gyroscopes |
| topic | Robotics |
| url | https://arxiv.org/abs/2403.02618 |