用深度学习从声呐数据预测水下机器人加速度，精度提升超65%
AUV Acceleration Prediction Using DVL and Deep Learning
- 用历史声呐速度数据直接端到端预测加速度向量
- 相比传统模型方法，加速度估计误差降低65%以上
- 适合需要高精度导航的水下探测任务
自主水下航行器（AUV）在海洋调查、水下测绘和基础设施检测中至关重要。精确可靠的导航是完成这些任务的关键。为此，通常融合多普勒测速仪（DVL）与惯性传感器。近期，基于模型的方法已能从DVL速度测量中提取车辆加速度矢量。受此启发，本文提出一种端到端深度学习方法，基于历史DVL速度数据估计AUV加速度矢量。利用海上实验记录的数据，我们证明该方法相较模型方法将加速度矢量估计性能提升超过65%。得益于数据驱动策略，可显著提高AUV导航的准确性与可靠性，从而提升水下任务的效率与成功率。
原文摘要 · Abstract (English)
Autonomous underwater vehicles (AUVs) are essential for various applications, including oceanographic surveys, underwater mapping, and infrastructure inspections. Accurate and robust navigation are critical to completing these tasks. To this end, a Doppler velocity log (DVL) and inertial sensors are fused together. Recently, a model-based approach demonstrated the ability to extract the vehicle acceleration vector from DVL velocity measurements. Motivated by this advancement, in this paper we present an end-to-end deep learning approach to estimate the AUV acceleration vector based on past DVL velocity measurements. Based on recorded data from sea experiments, we demonstrate that the proposed method improves acceleration vector estimation by more than 65% compared to the model-based approach by using data-driven techniques. As a result of our data-driven approach, we can enhance navigation accuracy and reliability in AUV applications, contributing to more efficient and effective underwater missions through improved accuracy and reliability.
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