AI提升水下无人艇定位精度，解决深海导航难题。
AI-Aided Advancements in Autonomous Underwater Vehicle Navigation
- 融合惯导、多普勒测速仪和摄像头，实现多源感知融合。
- 利用AI学习方法改进惯性里程计，提升长时间导航精度。
- 适合深海探测、海洋科研及水下机器人研发人员参考。
自主水下航行器（AUV）已成为深海探索不可或缺的工具，广泛应用于科学考察与商业领域。由于电磁波在水中迅速衰减，卫星无线电信号无法使用，加之海洋环境动态且不可预测，给导航带来巨大挑战。本文探讨了基于人工智能的AUV定位最新进展，重点分析了将惯性导航系统（INS）、多普勒速度计（DVL）与相机融合的先进传感器融合架构。超越传统基于模型的滤波方法，文中还研究了人工智能驱动的学习方法在提升惯性死区推算（dead-reckoning）性能及自适应融合算法方面的突破性进展。通过梳理这些关键技术里程碑，本文为实现高精度自主水下导航提供了系统性路线图。
原文摘要 · Abstract (English)
Autonomous underwater vehicles (AUVs) have become indispensable for deep-sea exploration, spanning critical scientific research and commercial applications. The rapid attenuation of electromagnetic waves renders satellite radio signals unavailable, while the dynamic unpredictability of the marine environment presents formidable navigation challenges. This chapter explores recent advancements in AI-aided AUV positioning, specifically focusing on advanced sensor fusion architectures that integrate inertial navigation systems with Doppler velocity logs and cameras. Beyond traditional model-based filtering, we examine the transformative emergence of AI-driven learning approaches in enhancing inertial dead-reckoning tasks and adaptive fusion algorithms. By addressing these recent milestones, this chapter provides a comprehensive roadmap for achieving the high-precision navigation essential for autonomous underwater missions.
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