文章基本信息

标题：Visual Loop Detection in Underwater Robotics: an Unsupervised Deep Learning Approach ⁎
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作者：Antoni Burguera ; Francisco Bonin-Font
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：14656-14661
DOI：10.1016/j.ifacol.2020.12.1476
语种：English
出版社：Elsevier
摘要：AbstractThis paper presents a novel Deep Neural Network aimed at fast and robust visual loop detection targeted to underwater images. In order to help the proposed network to learn the features that define loop closings, a global image descriptor built upon clusters of local SIFT descriptors is proposed. Also, a method allowing unsupervised training is presented, eliminating the need for a hand-labelled ground truth. Once trained, the Neural Network builds two descriptors of an image that can be easily compared to other image descriptors to ascertain if they close a loop or not. The experimental results, performed using real data gathered in coastal areas of Mallorca (Spain), show the validity of our proposal and favourably compares it to previously existing methods.
关键词：KeywordsRobot visionunderwater roboticsneural networksloop detectionSLAM