文章基本信息

标题：Lightweight Underwater Visual Loop Detection and Classification using a Siamese Convolutional Neural Network ⁎
本地全文：下载
作者：Antoni Burguera
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：16
页码：410-415
DOI：10.1016/j.ifacol.2021.10.124
语种：English
出版社：Elsevier
摘要：AbstractThis paper presents an end-to-end Neural Network (NN) to estimate the overlap between two scenes observed by an underwater robot endowed with a bottom-looking camera. This information is extremely valuable to perform visual loop detection in Simultaneous Localization and Mapping (SLAM). Contrarily to other existing approaches, this study does not depend on handcrafted features or similarity metrics, but jointly optimizes the image description and the loop detection by means of a Siamese NN architecture.Twelve different configurations have been experimentally tested using large balanced datasets synthetically generated from real data. These experiments demonstrate the ability of our proposal to properly estimate the overlap with precisions, recalls and fall-outs close to 95%, 98% and 5% respectively and execution times close to 0.7 ms per loop in a standard laptop computer. The source code of this proposal is publicly available.
关键词：KeywordsSLAMNeural NetworkVisual Loop DetectionDeep Learning