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This chapter surveys recent progress in AI-enhanced navigation for autonomous underwater vehicles (AUVs), focusing on the integration of inertial navigation systems (INS) with Doppler velocity logs (DVL) and cameras. It highlights the shift from traditional model-based filtering to AI-driven learning approaches for improving inertial dead-reckoning and adaptive sensor fusion. The work synthesizes advancements in sensor fusion architectures to achieve high-precision navigation in challenging underwater environments.
AI is enabling a new generation of AUV navigation systems that overcome the limitations of traditional model-based approaches in complex underwater environments.
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.
