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

Authors Antoni Burguera Burguera | Francisco Bonin-Font
In Intenational Conference on Computer Vision Theory and Applications (VISAPP), Valetta (Malta), 2020.
ISBN 978-989-758-402-2


{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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