Persistent Localization of Autonomous Underwater Vehicles Using Visual Perception of Artificial Landmarks
Persistent localization is a critical requirement for autonomous underwater vehicles (AUVs) engaged in long-term missions. Conventional dead-reckoning (DR) methods for estimating the position and orientation of AUVs often suffer from drift, necessitating additional information to correct accumulatin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/5/828 |
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| author | Jongdae Jung Hyun-Taek Choi Yeongjun Lee |
| author_facet | Jongdae Jung Hyun-Taek Choi Yeongjun Lee |
| author_sort | Jongdae Jung |
| collection | DOAJ |
| description | Persistent localization is a critical requirement for autonomous underwater vehicles (AUVs) engaged in long-term missions. Conventional dead-reckoning (DR) methods for estimating the position and orientation of AUVs often suffer from drift, necessitating additional information to correct accumulating errors. In this paper, we propose a visual artificial landmarks-based simultaneous localization and mapping (SLAM) system for AUVs. This system utilizes two types of underwater artificial landmarks that are observed using forward and downward-looking cameras. The information obtained from these detected landmarks, along with incremental DR estimates, is integrated within a framework based on the extended Kalman filter (EKF) SLAM approach, allowing for the recursive estimation of both the robot and the landmark states. We implemented the proposed visual SLAM method using our yShark II AUV and conducted experiments in an engineering basin to validate its effectiveness. A ceiling vision-based reference pose acquisition system was installed, facilitating a comparison between the pose estimation results obtained from DR and those derived from the SLAM method. |
| format | Article |
| id | doaj-art-e3e4efce5765472ea866bcf74ac058d4 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-e3e4efce5765472ea866bcf74ac058d42025-08-20T03:47:58ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-04-0113582810.3390/jmse13050828Persistent Localization of Autonomous Underwater Vehicles Using Visual Perception of Artificial LandmarksJongdae Jung0Hyun-Taek Choi1Yeongjun Lee2Department of Autonomous Vehicle System Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaAdvanced-Intelligent Ship Research Division, Korea Research Institute of Ships and Ocean Engineering, Daejeon 34103, Republic of KoreaOcean and Maritime Digital Technology Research Division, Korea Research Institute of Ships and Ocean Engineering, Daejeon 34103, Republic of KoreaPersistent localization is a critical requirement for autonomous underwater vehicles (AUVs) engaged in long-term missions. Conventional dead-reckoning (DR) methods for estimating the position and orientation of AUVs often suffer from drift, necessitating additional information to correct accumulating errors. In this paper, we propose a visual artificial landmarks-based simultaneous localization and mapping (SLAM) system for AUVs. This system utilizes two types of underwater artificial landmarks that are observed using forward and downward-looking cameras. The information obtained from these detected landmarks, along with incremental DR estimates, is integrated within a framework based on the extended Kalman filter (EKF) SLAM approach, allowing for the recursive estimation of both the robot and the landmark states. We implemented the proposed visual SLAM method using our yShark II AUV and conducted experiments in an engineering basin to validate its effectiveness. A ceiling vision-based reference pose acquisition system was installed, facilitating a comparison between the pose estimation results obtained from DR and those derived from the SLAM method.https://www.mdpi.com/2077-1312/13/5/828autonomous underwater vehiclessimultaneous localization and mappingartificial landmarksoptical cameras |
| spellingShingle | Jongdae Jung Hyun-Taek Choi Yeongjun Lee Persistent Localization of Autonomous Underwater Vehicles Using Visual Perception of Artificial Landmarks Journal of Marine Science and Engineering autonomous underwater vehicles simultaneous localization and mapping artificial landmarks optical cameras |
| title | Persistent Localization of Autonomous Underwater Vehicles Using Visual Perception of Artificial Landmarks |
| title_full | Persistent Localization of Autonomous Underwater Vehicles Using Visual Perception of Artificial Landmarks |
| title_fullStr | Persistent Localization of Autonomous Underwater Vehicles Using Visual Perception of Artificial Landmarks |
| title_full_unstemmed | Persistent Localization of Autonomous Underwater Vehicles Using Visual Perception of Artificial Landmarks |
| title_short | Persistent Localization of Autonomous Underwater Vehicles Using Visual Perception of Artificial Landmarks |
| title_sort | persistent localization of autonomous underwater vehicles using visual perception of artificial landmarks |
| topic | autonomous underwater vehicles simultaneous localization and mapping artificial landmarks optical cameras |
| url | https://www.mdpi.com/2077-1312/13/5/828 |
| work_keys_str_mv | AT jongdaejung persistentlocalizationofautonomousunderwatervehiclesusingvisualperceptionofartificiallandmarks AT hyuntaekchoi persistentlocalizationofautonomousunderwatervehiclesusingvisualperceptionofartificiallandmarks AT yeongjunlee persistentlocalizationofautonomousunderwatervehiclesusingvisualperceptionofartificiallandmarks |