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Autonomous underwater vehicle navigation using sonar image matching based on convolutional neural network

Citation

Yang, W and Fan, S and Xu, S and King, P and Kang, B and Kim, E, Autonomous underwater vehicle navigation using sonar image matching based on convolutional neural network, IFAC PapersOnLine, 52, (21) pp. 156-162. ISSN 2405-8963 (2019) [Refereed Article]


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Copyright Statement

2019, IFAC (International Federation of Automatic Control). 2019 the authors. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) http://creativecommons.org/licenses/by-nc-nd/4.0/

DOI: doi:10.1016/j.ifacol.2019.12.300

Abstract

This paper presents an image matching algorithm based on convolutional neural network (CNN) to aid in the navigating of an Autonomous Underwater Vehicle (AUV) where external navigation aids are not available. We aim to solve the problem where traditional image feature representations and similarity learning are not learned jointly and to improve the matching accuracy of sonar images in deep ocean with dynamic backgrounds, low-intensity and high-noise scenes. In our work, the proposed CNN-based model can train the texture features of sonar images without any manually designed feature descriptors, which can jointly optimize the representation of the input data conditioned on the similarity measure being used. The validation studies show the feasibility and veracity of the proposed method for many general and offset cases using collected sonar images.

Item Details

Item Type:Refereed Article
Keywords:sonar image matching, convolutional neural network, feature extraction, AUV, teach-and-repeat path following
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Image Processing
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Computer Software and Services not elsewhere classified
UTAS Author:Yang, W (Ms Wenli Yang)
UTAS Author:Xu, S (Dr Shuxiang Xu)
UTAS Author:King, P (Mr Peter King)
UTAS Author:Kang, B (Professor Byeong Kang)
UTAS Author:Kim, E (Miss Eonjoo Kim)
ID Code:137586
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2020-02-20
Last Modified:2020-05-18
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