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.

翻译：本文提出了一种基于学习的微机电系统（MEMS）陀螺仪校准与去噪方法。该方法基于卷积网络设计，仅包含数百个参数，因此可在图形处理器（GPU）上完成训练，然后部署至计算资源有限的微控制器单元（MCU）中。在该方法中，神经网络模型仅以陀螺仪的原始测量值作为输入，并分别处理校准与降噪任务以确保可解释性。所提方法在公开数据集和真实实验中进行了验证，与现有基于学习的方法不同，无需依赖特定数据集进行训练。实验结果表明了所提方法的实用性与有效性，表明该技术可作为需要惯性测量单元（IMU）应用场景的可行方案。