AquaVis: A Perception-Aware Autonomous Navigation Framework for Underwater Vehicles
This addresses the challenge of perception-aware navigation for underwater vehicles, which is incremental as it builds on existing motion planning methods.
The paper tackles the problem of enabling Autonomous Underwater Vehicles (AUVs) to navigate while tracking multiple visual objectives in real-time, resulting in significant improvement in tracking points of interest with low computational overhead and fast re-planning times.
Visual monitoring operations underwater require both observing the objects of interest in close-proximity, and tracking the few feature-rich areas necessary for state estimation.This paper introduces the first navigation framework, called AquaVis, that produces on-line visibility-aware motion plans that enable Autonomous Underwater Vehicles (AUVs) to track multiple visual objectives with an arbitrary camera configuration in real-time. Using the proposed pipeline, AUVs can efficiently move in 3D, reach their goals while avoiding obstacles safely, and maximizing the visibility of multiple objectives along the path within a specified proximity. The method is sufficiently fast to be executed in real-time and is suitable for single or multiple camera configurations. Experimental results show the significant improvement on tracking multiple automatically-extracted points of interest, with low computational overhead and fast re-planning times