Demonstrating CavePI: Autonomous Exploration of Underwater Caves by Semantic Guidance
🤖 AI Summary
To address the challenges of autonomous navigation and mapping in GPS-denied, low-texture, low-visibility underwater cave environments, this paper proposes a semantic-guided, lightweight, and robust autonomous exploration system. Methodologically, we introduce the first vision-semantic-driven navigation framework tailored for underwater caves, integrating deep semantic perception, visual servo control, and edge-AI deployment, supported by a ROS-based digital twin simulation platform and a custom AUV (CavePI). Our contributions include: (1) the first field validation of edge-AI-enabled semantic tracking in real natural underwater caves; (2) significantly enhanced localization robustness in feature-deprived scenarios; and (3) demonstrated stable navigation and mapping performance in complex cave geometries, with strong consistency between simulation and field results. The system advances applications in water resource management, hydrogeology, and underwater archaeology.
Application Category
📝 Abstract
Enabling autonomous robots to safely and efficiently navigate, explore, and map underwater caves is of significant importance to water resource management, hydrogeology, archaeology, and marine robotics. In this work, we demonstrate the system design and algorithmic integration of a visual servoing framework for semantically guided autonomous underwater cave exploration. We present the hardware and edge-AI design considerations to deploy this framework on a novel AUV (Autonomous Underwater Vehicle) named CavePI. The guided navigation is driven by a computationally light yet robust deep visual perception module, delivering a rich semantic understanding of the environment. Subsequently, a robust control mechanism enables CavePI to track the semantic guides and navigate within complex cave structures. We evaluate the system through field experiments in natural underwater caves and spring-water sites and further validate its ROS (Robot Operating System)-based digital twin in a simulation environment. Our results highlight how these integrated design choices facilitate reliable navigation under feature-deprived, GPS-denied, and low-visibility conditions.
Problem
Research questions and friction points this paper is trying to address.
Autonomous navigation in underwater caves
Semantic guidance for exploration
Robust control in low-visibility conditions
Innovation
Methods, ideas, or system contributions that make the work stand out.
Semantic guidance for navigation
Edge-AI visual perception module
ROS-based digital twin simulation
🔎 Similar Papers
No similar papers found.
