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
In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems
2021

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.

@inproceedings{XanKal+21,
  author = {Marios Xanthidis and Michail Kalaitzakis and Nare Karapetyan
            and James Johnson and Nikolaos Vitzilaios and
            Jason M. O'Kane and Ioannis Rekleitis},
  booktitle = {Proc. IEEE/RSJ International Conference on Intelligent
               Robots and Systems},
  title = {AquaVis: A Perception-Aware Autonomous Navigation Framework
           for Underwater Vehicles},
  year = {2021}
}


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