TinyGC-Net:用于校准 MEMS 陀螺仪的超小型网络

ArXiv Pub Date : 2024-07-26 DOI:10.48550/arXiv.2403.02618
Chao Cui, Jiankang Zhao
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引用次数: 0

摘要

本文介绍了一种为微电子机械系统(MEMS)陀螺仪量身定制的基于学习的校准方法。所提出的方法集成了两个线性网络,通过参数整流线性单元(PReLU)进行连接,结构紧凑,仅有 25 个参数。这种简易性允许在图形处理器(GPU)上进行高效训练,然后再部署到资源受限的微控制器单元(MCU)上。损失函数经过精心设计,通过消除对开源数据集的依赖来强化神经模型,并通过三轴手动旋转表来快速收集训练数据。此外,所提出的方法还经过了公共数据集和实际场景的严格验证,不仅保持了其超轻量级属性,而且在准确性方面优于其他现有解决方案。实验结果证明了该方法的实用性和有效性,表明它适用于需要惯性测量单元(IMU)的应用。该方法的开源实现可通过以下网址访问: https://github.com/tsuibeyond/TinyGC-Net
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TinyGC-Net: An Extremely Tiny Network for Calibrating MEMS Gyroscopes
This paper introduces a learning-based calibration method tailored for microelectromechanical system (MEMS) gyroscopes. The proposed method integrates two linear networks, linked by a parametric rectified linear unit (PReLU), and boasts a compacted architecture with only 25 parameters. This simplicity allows for efficient training on a graphics processing unit (GPU) before deployment on resource-constrained microcontroller units (MCUs). The loss function has been carefully devised to strengthen the neural model by eliminating reliance on open-source datasets, and facilitates the swift collection of training data solely via a tri-axial manual rotation table. Furthermore, the proposed method has undergone rigorous validation through public datasets and real-world scenarios, which not only maintains its ultra-lightweight attributes but also outperforms other existing solutions in terms of accuracy. Experimental results demonstrate the method's practicality and efficacy, indicating its suitability for applications requiring inertial measurement units (IMUs). And the open-source implementation of this method is accessible at: https://github.com/tsuibeyond/TinyGC-Net
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