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Forward-looking Sonar Image Mosaicking by Feature Tracking
Song SM(宋三明); J. Michael Herrmann; Liu KZ(刘开周); Li S(李硕); Feng XS(封锡盛)
Department水下机器人研究室
Conference Name2016 IEEE International Conference on Robotics and Biomimetics (ROBIO 2016)
Conference DateDecember 3-7, 2016
Conference PlaceQingdao, China
Source PublicationProceedings of the 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO 2016)
PublisherIEEE
Publication PlaceNew York
2016
Pages1613-1618
Indexed ByEI ; CPCI(ISTP)
EI Accession number20171403534790
WOS IDWOS:000405724600271
Contribution Rank1
ISBN978-1-5090-4363-7
AbstractHigh-frequency forward-looking sonar are appropriate for the operation or search close to the seabed. Constructing a panoramic mosaic not only facilitates an interpretation of the underwater environment, but also supports the vehicle’s self-localization. In this paper, a method to register sonar sequences is proposed that is based on the feature tracking using the particle filtering. Our methods starts with the extraction of the intensity, the texture and the shape features from the unstructured seabed environment. Next a region of interest (ROI) is tracked until it disappears from the view field of an autonomous underwater vehicle (AUV). Then, another ROI is selected and the tracking procedures are repeated. Experimental results show that (1) Feature tracking is feasible for the forward-looking sonar image mosaicking. (2) Fusion of the texture and the shape feature lead to a robust feature extraction method for more precise motion estimation. (3) The prior information on the AUV’s movement is necessary for the tracking of highlighted regions.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/19687
Collection水下机器人研究室
Corresponding AuthorSong SM(宋三明)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.Institute of Perception, Action and Behaviour, University of Edinburgh, EH8 9AB, United Kingdom
Recommended Citation
GB/T 7714
Song SM,J. Michael Herrmann,Liu KZ,et al. Forward-looking Sonar Image Mosaicking by Feature Tracking[C]. New York:IEEE,2016:1613-1618.
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