Adaptive gyroscope denoising scheme based on frequency-domain attention

Purpose This paper aims to propose a gyroscope denoising method based on the real data obtained from the inertial measurement unit to acquire the robot’s attitude. Experiments show that, compared with existing algorithms, this network structure and loss function can achieve better accuracy. This method is based on dilated convolution, which overcomes the limitations of traditional recurrent neural networks and improves the training speed. Design/methodology/approach Innovatively, the authors transform the time-domain task into a frequency-domain task. Such a change can reduce the limitations of time series data in terms of noise sensitivity and small data sets. Specifically, the module the authors propose captures the interaction between long- and short-term information by using the inertial convolutional block (ICB) and dilated convolution and enhances the feature representation through Fourier analysis. In addition, the authors also adopt the idea of channel attention to capture more detailed temporal information in the sensor data. The adaptive threshold method selectively filters out high-frequency noise information, enabling us to better predict the required attitude. The authors also reconstruct a loss function, which takes into account both the incremental directions of each small range and the global incremental errors. Findings The authors have compared this method with the widely used European Robotics Challenge data set and the publicly available TUM Stereo Visual-Inertial Event data set. Finally, the authors conclude that this method outperforms the current algorithms on both data sets. Originality/value The value of this paper is that to overcome the limitations of time-domain analysis, the authors designed a Fourier adaptive attention module. Meanwhile, to handle non-continuous information, the authors introduced the ICB module. The authors reconstructed a loss function, which takes into account both the incremental directions of each small range and the global incremental errors.