AquaVis: A Perception-Aware Autonomous Navigation Framework for Underwater Vehicles
Abstract
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
Cite
@article{arxiv.2110.01646,
title = {AquaVis: A Perception-Aware Autonomous Navigation Framework for Underwater Vehicles},
author = {Marios Xanthidis and Michail Kalaitzakis and Nare Karapetyan and James Johnson and Nikolaos Vitzilaios and Jason M. O'Kane and Ioannis Rekleitis},
journal= {arXiv preprint arXiv:2110.01646},
year = {2021}
}
Comments
Presented at IROS2021