用深度学习提升水下机器人惯导与多普勒测速仪对齐精度和速度。
A Data-Driven Method for INS/DVL Alignment
- 设计端到端深度学习框架，自动优化传感器对齐。
- 仿真结果表明对齐精度更高、收敛时间更短。
- 适合需要高精度水下导航的自主航行器研发人员。
自主水下航行器（AUV）是广泛应用于多种场景的复杂机器人平台，其导航系统精度直接决定任务成败。惯性传感器与多普勒测速仪（DVL）融合是实现长距离水下导航的有前景方案，但其效果高度依赖于惯性传感器与DVL之间的精确对齐。现有对齐方法虽具潜力，但在精度、收敛时间及对齐轨迹效率方面仍有改进空间。本文提出一种端到端深度学习框架用于对齐过程。通过利用深度学习在降噪和捕捉数据非线性特征方面的优势，基于仿真数据验证，所提方法在对齐精度和收敛速度上均优于当前基于模型的方法。
原文摘要 · Abstract (English)
Autonomous underwater vehicles (AUVs) are sophisticated robotic platforms crucial for a wide range of applications. The accuracy of AUV navigation systems is critical to their success. Inertial sensors and Doppler velocity logs (DVL) fusion is a promising solution for long-range underwater navigation. However, the effectiveness of this fusion depends heavily on an accurate alignment between the inertial sensors and the DVL. While current alignment methods show promise, there remains significant room for improvement in terms of accuracy, convergence time, and alignment trajectory efficiency. In this research we propose an end-to-end deep learning framework for the alignment process. By leveraging deep-learning capabilities, such as noise reduction and capture of nonlinearities in the data, we show using simulative data, that our proposed approach enhances both alignment accuracy and reduces convergence time beyond current model-based methods.
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