用数据驱动方法实现秒级高精度陀螺仪校准
Data-Driven Gyroscope Calibration
- 基于6分钟转台数据训练神经网络，直接估计陀螺仪增益和偏置
- 6秒内完成校准，精度提升72%，耗时减少75%
- 适合需要快速、低成本校准的嵌入式导航系统
陀螺仪是测量平台角速度的惯性传感器。为在任务开始前估计其确定性误差，需进行校准。对于低成本陀螺仪，因无法感知地球自转速率，通常需使用转台校准。本文提出一种数据驱动框架，用于估计陀螺仪的尺度因子和偏置。为训练和验证该方法，采集了56分钟的转台数据。实验表明，相比传统模型方法，本方法在准确性和收敛速度上均更优：在6秒校准时间内，平均提升了72%的尺度因子与偏置估计精度，校准时间平均缩短75%。即，本方法仅需数秒即可完成校准，取代原有数分钟流程。
原文摘要 · Abstract (English)
Gyroscopes are inertial sensors that measure the angular velocity of the platforms to which they are attached. To estimate the gyroscope deterministic error terms prior mission start, a calibration procedure is performed. When considering low-cost gyroscopes, the calibration requires a turntable as the gyros are incapable of sensing the Earth turn rate. In this paper, we propose a data-driven framework to estimate the scale factor and bias of a gyroscope. To train and validate our approach, a dataset of 56 minutes was recorded using a turntable. We demonstrated that our proposed approach outperforms the model-based approach, in terms of accuracy and convergence time. Specifically, we improved the scale factor and bias estimation by an average of 72% during six seconds of calibration time, demonstrating an average of 75% calibration time improvement. That is, instead of minutes, our approach requires only several seconds for the calibration.
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