AquaVis: A Perception-Aware Autonomous Navigation Framework for Underwater Vehicles
Marios Xanthidis, Michail Kalaitzakis, Nare Karapetyan, James Johnson, Nikolaos Vitzilaios, Jason O'Kane, Ioannis Rekleitis
In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021.
Abstract
Visual monitoring operations underwater require both observing in close-proximity the objects of interest, 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.
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BibTeX
@inproceedings{XanthidisIROS2021,
author = {Marios Xanthidis and Michail Kalaitzakis and Nare
Karapetyan and James Johnson and Nikolaos Vitzilaios and
Jason O'Kane and Ioannis Rekleitis},
title = {AquaVis: A Perception-Aware Autonomous Navigation
Framework for Underwater Vehicles},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS)},
year = {2021},
pages = {5387-5394},
address = {Prague, Czech Republic},
doi = {10.1109/IROS51168.2021.9636124}
}
Tue Apr 16 06:21:02 EDT 2024