Towards Visual Loop Detection in Underwater Robotics using a Deep Neural Network

Antoni Burguera, Francisco Bonin-Font

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 Harvard Style

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 - Volume 5: VISAPP, ISBN 978-989-758-402-2, pages 667-673. DOI: 10.5220/0009162806670673


in Bibtex Style

@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 - Volume 5: VISAPP,},
year={2020},
pages={667-673},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009162806670673},
isbn={978-989-758-402-2},
}


in EndNote Style

TY - CONF

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