快速陀螺仪校准：一种深度学习方法
Rapid Gyroscope Calibration: A Deep Learning Approach
AI总结：
针对低成本陀螺仪短时间校准的应用需求，提出端到端卷积神经网络校准方法，结合多真实/虚拟陀螺仪数据，最多可缩短89%校准时间，相关数据集已公开。
摘要：
低成本陀螺仪校准对于保障陀螺仪测量的准确性与可靠性至关重要。静态校准用于估算测量误差的确定性分量，为此常规做法是在预定义时段内对陀螺仪读数取平均，进而估算陀螺仪偏置。校准时长对校准性能有关键影响，因此通常优先选择更长的校准周期。但部分应用要求快速启动，仅允许在极短时间内完成校准。本研究聚焦于利用深度学习方法缩短低成本陀螺仪的校准时间，提出了一种适用于陀螺仪校准场景的端到端卷积神经网络，同时探索了利用多个真实与虚拟陀螺仪提升单陀螺仪校准性能的可行性。为训练并验证所提方法，研究人员使用4个不同品牌的36个陀螺仪，采集了包含186.6小时陀螺仪读数的数据集，还构建了由模拟陀螺仪读数组成的虚拟数据集，共使用6个数据集对所提方法开展评估。本研究的核心成果之一是借助3个低成本陀螺仪，将陀螺仪校准时间最多缩短了89%。研究涉及的数据集已公开，以保障研究可复现，推动该领域的相关研究进展。
英文摘要：
Low-cost gyroscope calibration is essential for ensuring the accuracy and reliability of gyroscope measurements. Stationary calibration estimates the deterministic parts of measurement errors. To this end, a common practice is to average the gyroscope readings during a predefined period and estimate the gyroscope bias. Calibration duration plays a crucial role in performance, therefore, longer periods are preferred. However, some applications require quick startup times and calibration is therefore allowed only for a short time. In this work, we focus on reducing low-cost gyroscope calibration time using deep learning methods. We propose an end-to-end convolutional neural network for the application of gyroscope calibration. We explore the possibilities of using multiple real and virtual gyroscopes to improve the calibration performance of single gyroscopes. To train and validate our approach, we recorded a dataset consisting of 186.6 hours of gyroscope readings, using 36 gyroscopes of four different brands. We also created a virtual dataset consisting of simulated gyroscope readings. The six datasets were used to evaluate our proposed approach. One of our key achievements in this work is reducing gyroscope calibration time by up to 89% using three low-cost gyroscopes. Our dataset is publicly available to allow reproducibility of our work and to increase research in the field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
