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About this paper
Alankrit Gupta, Adnan Abdullah, Xianyao Li, Vaishnav Ramesh, Ioannis Rekleitis, Md Jahidul Islam
The University of Florida's RoboPI laboratory introduces CavePI, an Autonomous Underwater Vehicle designed for autonomous exploration of underwater cave environments. CavePI navigates by tracking semantic visual cues, specifically cavelines, and was validated through extensive laboratory and challenging real-world field trials, demonstrating robust depth control and real-time semantic perception on edge hardware.
Key insights
- Semantic guidance, particularly caveline tracking, offers a robust and effective navigation strategy for AUVs in GPS-denied and feature-deprived underwater cave environments.
- Real-time semantic perception for AUV navigation is achievable on resource-constrained edge-AI hardware by using computationally lightweight deep learning models.
- Thorough validation across laboratory, simulation, and diverse real-world field environments is essential for identifying practical limitations and informing the design of future autonomous underwater systems.
Summary by alphaXiv.
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