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Detection and Pose Estimation for Short-range Vision-Based Underwater Docking
Liu S(刘爽)1,2,3,4; Ozay, Mete4; Okatani, Takayuki4,5; Xu HL(徐红丽)1,2; Sun K(孙凯)1,2,3; Lin Y(林扬)1,2
Department海洋信息技术装备中心
Source PublicationIEEE Access
ISSN2169-3536
2019
Volume7Pages:2720-2749
Indexed BySCI ; EI
EI Accession number20185006242122
WOS IDWOS:000455872500001
Contribution Rank1
KeywordUnderwater docking AUVs detection pose estimation marine robotics
AbstractPotential of using autonomous underwater vehicles (AUVs) for underwater exploration is confined by its limited on-board battery energy and data storage capacity. This problem has been addressed using docking systems by underwater recharging and data transfer for AUVs. In this work, we propose a vision based framework by addressing detection and pose estimation problems for short-range underwater docking using these systems. For robust and credible detection of docking stations, we propose a convolutional neural network called Docking Neural Network (DoNN). For accurate pose estimation, a perspective-n-point algorithm is integrated into our framework. In order to examine our framework in underwater docking tasks, we collected a dataset of 2D images, named Underwater Docking Images Dataset (UDID), which is the first publicly available underwater docking dataset to the best of our knowledge. In the field experiments, we first evaluate performance of DoNN on the UDID and its deformed variations. Next, we examine the pose estimation module by ground and underwater experiments. At last, we integrate our proposed vision based framework with an ultra-short baseline (USBL) acoustic sensor, to demonstrate efficiency and accuracy of our framework by performing experiments in a lake. Experimental results show that the proposed framework is able to detect docking stations and estimate their relative pose more efficiently and successfully, compared to the state-of-the-art baseline systems.
Language英语
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS KeywordCAMERA
WOS Research AreaComputer Science ; Engineering ; Telecommunications
Funding ProjectChina State Key Laboratory of Robotics Foundation[2016-Z08] ; JST CREST[JPMJCR14D1] ; Council for Science, Technology and Innovation (CSTI) ; ImPACT Program Tough Robotics Challenge'' of the Council for Science, Technology, and Innovation (Cabinet Office, Government of Japan)
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/23812
Collection海洋信息技术装备中心
Corresponding AuthorLiu S(刘爽); Ozay, Mete
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
3.University of Chinese Academy of Sciences, Beijing, China
4.Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan
5.RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.
Recommended Citation
GB/T 7714
Liu S,Ozay, Mete,Okatani, Takayuki,et al. Detection and Pose Estimation for Short-range Vision-Based Underwater Docking[J]. IEEE Access,2019,7:2720-2749.
APA Liu S,Ozay, Mete,Okatani, Takayuki,Xu HL,Sun K,&Lin Y.(2019).Detection and Pose Estimation for Short-range Vision-Based Underwater Docking.IEEE Access,7,2720-2749.
MLA Liu S,et al."Detection and Pose Estimation for Short-range Vision-Based Underwater Docking".IEEE Access 7(2019):2720-2749.
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