A Survey on Visual Navigation and Positioning for Autonomous UUVs
Autonomous navigation and positioning are key to the successful performance of unmanned underwater vehicles (UUVs) in environmental monitoring, oceanographic mapping, and critical marine infrastructure inspections in the sea. Cameras have been at the center of attention as an underwater sensor due t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/15/3794 |
| _version_ | 1797412354021916672 |
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| author | Jiangying Qin Ming Li Deren Li Jiageng Zhong Ke Yang |
| author_facet | Jiangying Qin Ming Li Deren Li Jiageng Zhong Ke Yang |
| author_sort | Jiangying Qin |
| collection | DOAJ |
| description | Autonomous navigation and positioning are key to the successful performance of unmanned underwater vehicles (UUVs) in environmental monitoring, oceanographic mapping, and critical marine infrastructure inspections in the sea. Cameras have been at the center of attention as an underwater sensor due to the advantages of low costs and rich content information in high visibility ocean waters, especially in the fields of underwater target recognition, navigation, and positioning. This paper is not only a literature overview of the vision-based navigation and positioning of autonomous UUVs but also critically evaluates the methodologies which have been developed and that directly affect such UUVs. In this paper, the visual navigation and positioning algorithms are divided into two categories: geometry-based methods and deep learning-based. In this paper, the two types of SOTA methods are compared experimentally and quantitatively using a public underwater dataset and their potentials and shortcomings are analyzed, providing a panoramic theoretical reference and technical scheme comparison for UUV visual navigation and positioning research in the highly dynamic and three-dimensional ocean environments. |
| first_indexed | 2024-03-09T05:01:53Z |
| format | Article |
| id | doaj.art-3b91de8315ce491492ff5de1a2007e78 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:01:53Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3b91de8315ce491492ff5de1a2007e782023-12-03T12:59:01ZengMDPI AGRemote Sensing2072-42922022-08-011415379410.3390/rs14153794A Survey on Visual Navigation and Positioning for Autonomous UUVsJiangying Qin0Ming Li1Deren Li2Jiageng Zhong3Ke Yang4State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Water Resources and Hydropower Engineering, Wuhan University, Wuhan 430079, ChinaAutonomous navigation and positioning are key to the successful performance of unmanned underwater vehicles (UUVs) in environmental monitoring, oceanographic mapping, and critical marine infrastructure inspections in the sea. Cameras have been at the center of attention as an underwater sensor due to the advantages of low costs and rich content information in high visibility ocean waters, especially in the fields of underwater target recognition, navigation, and positioning. This paper is not only a literature overview of the vision-based navigation and positioning of autonomous UUVs but also critically evaluates the methodologies which have been developed and that directly affect such UUVs. In this paper, the visual navigation and positioning algorithms are divided into two categories: geometry-based methods and deep learning-based. In this paper, the two types of SOTA methods are compared experimentally and quantitatively using a public underwater dataset and their potentials and shortcomings are analyzed, providing a panoramic theoretical reference and technical scheme comparison for UUV visual navigation and positioning research in the highly dynamic and three-dimensional ocean environments.https://www.mdpi.com/2072-4292/14/15/3794unmanned underwater vehicles (UUVs)simultaneous localization and mappingvisual navigationpositioningdeep learning |
| spellingShingle | Jiangying Qin Ming Li Deren Li Jiageng Zhong Ke Yang A Survey on Visual Navigation and Positioning for Autonomous UUVs Remote Sensing unmanned underwater vehicles (UUVs) simultaneous localization and mapping visual navigation positioning deep learning |
| title | A Survey on Visual Navigation and Positioning for Autonomous UUVs |
| title_full | A Survey on Visual Navigation and Positioning for Autonomous UUVs |
| title_fullStr | A Survey on Visual Navigation and Positioning for Autonomous UUVs |
| title_full_unstemmed | A Survey on Visual Navigation and Positioning for Autonomous UUVs |
| title_short | A Survey on Visual Navigation and Positioning for Autonomous UUVs |
| title_sort | survey on visual navigation and positioning for autonomous uuvs |
| topic | unmanned underwater vehicles (UUVs) simultaneous localization and mapping visual navigation positioning deep learning |
| url | https://www.mdpi.com/2072-4292/14/15/3794 |
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