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Authors: Antoni Burguera and Francisco Bonin-Font

Affiliation: Universitat de les Illes Balears, Ctra. Valldemossa Km. 7.5, Palma (Illes Balears), 07122, Spain

Keyword(s): Underwater Robotics, Loop Closing, Neural Network, SLAM.

Abstract: This paper constitutes a first step towards the use of Deep Neural Networks to fast and robustly detect underwater visual loops. The proposed architecture is based on an autoencoder, replacing the decoder part by a set of fully connected layers. Thanks to that it is possible to guide the training process by means of a global image descriptor built upon clusters of local SIFT features. After training, the NN builds two different descriptors of the input image. Both descriptors can be compared among different images to decide if they are likely to close a loop. The experiments, performed in coastal areas of Mallorca (Spain), evaluate both descriptors, show the ability of the presented approach to detect loop candidates and favourably compare our proposal to a previously existing method.

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Paper citation in several formats:
Burguera, A. and Bonin-Font, F. (2020). Towards Visual Loop Detection in Underwater Robotics using a Deep Neural Network. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 667-673. DOI: 10.5220/0009162806670673

@conference{visapp20,
author={Antoni Burguera. and Francisco Bonin{-}Font.},
title={Towards Visual Loop Detection in Underwater Robotics using a Deep Neural Network},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={667-673},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009162806670673},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - Towards Visual Loop Detection in Underwater Robotics using a Deep Neural Network
SN - 978-989-758-402-2
IS - 2184-4321
AU - Burguera, A.
AU - Bonin-Font, F.
PY - 2020
SP - 667
EP - 673
DO - 10.5220/0009162806670673
PB - SciTePress

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