Detection and Pose Estimation for Short-range Vision-Based Underwater Docking
文献类型：期刊论文
| 作者 | Sun K(孙凯)3,4,5 ; Xu HL(徐红丽3,5; Lin Y(林扬)3,5; Okatani, Takayuki1,2; Liu S(刘爽)1,3,4,5; Ozay, Mete1; Xu HL(徐红丽)
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| 刊名 | IEEE Access
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| 出版日期 | 2019 |
| 卷号 | 7页码:2720-2749 |
| 关键词 | Underwater docking AUVs detection pose estimation marine robotics |
| ISSN号 | 2169-3536 |
| 产权排序 | 1 |
| 英文摘要 | Potential 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关键词 | CAMERA |
| 资助项目 | China 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) |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000455872500001 |
| 源URL | [http://ir.sia.cn/handle/173321/23812]  |
| 专题 | 沈阳自动化研究所_海洋信息技术装备中心 |
| 通讯作者 | Liu S(刘爽); Ozay, Mete |
| 作者单位 | 1.Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan 2.RIKEN Center for Advanced Intelligence Project, Tokyo, Japan. 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China 4.University of Chinese Academy of Sciences, Beijing, China 5.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Sun K,Xu HL(徐红丽,Lin Y,et al. Detection and Pose Estimation for Short-range Vision-Based Underwater Docking[J]. IEEE Access,2019,7:2720-2749. |
| APA | Sun K.,Xu HL.,Lin Y.,Okatani, Takayuki.,Liu S.,...&Xu HL.(2019).Detection and Pose Estimation for Short-range Vision-Based Underwater Docking.IEEE Access,7,2720-2749. |
| MLA | Sun K,et al."Detection and Pose Estimation for Short-range Vision-Based Underwater Docking".IEEE Access 7(2019):2720-2749. |
入库方式： OAI收割
来源：沈阳自动化研究所
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