AquaVis: A Perception-Aware Autonomous Navigation Framework for Underwater Vehicles
Marios Xanthidis, Michail Kalaitzakis, Nare Karapetyan, James Johnson, Nikolaos Vitzilaios, Jason M. O’Kane, Ioannis Rekleitis
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.Accompanying short video: https://youtu.be/JKO bbrIZyU
BibTeX
@inproceedings{iros2021_aquavisapercepti,
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},
booktitle = {IROS 2021},
year = {2021}
}