SIA OpenIR  > 海洋信息技术装备中心
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
Source PublicationIEEE Access
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.
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
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)
Citation statistics
Document Type期刊论文
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
Detection and Pose E(12006KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liu S(刘爽)]'s Articles
[Ozay, Mete]'s Articles
[Okatani, Takayuki]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu S(刘爽)]'s Articles
[Ozay, Mete]'s Articles
[Okatani, Takayuki]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu S(刘爽)]'s Articles
[Ozay, Mete]'s Articles
[Okatani, Takayuki]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Detection and Pose Estimation for Short-Range Vision-Based Underwater Docking.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.