Research on the Visual SLAM Algorithm for Unmanned Surface Vehicles in Nearshore Dynamic Scenarios
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| Publicado en: | Journal of Marine Science and Engineering vol. 13, no. 4 (2025), p. 679 |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3194618489 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2077-1312 | ||
| 024 | 7 | |a 10.3390/jmse13040679 |2 doi | |
| 035 | |a 3194618489 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231479 |2 nlm | ||
| 100 | 1 | |a Zhang Yanran |u College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China; z17879573206@163.com | |
| 245 | 1 | |a Research on the Visual SLAM Algorithm for Unmanned Surface Vehicles in Nearshore Dynamic Scenarios | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a To address the challenges of visual SLAM algorithms in unmanned surface vehicles (USVs) during nearshore navigation or docking, where dynamic feature points degrade localization accuracy and dynamic objects impede static dense mapping, this study proposes an improved visual SLAM algorithm that removes dynamic feature points. Building upon the ORB-SLAM3 framework, the improved SLAM algorithm integrates a shore segmentation module and a dynamic region elimination module, while enabling static dense point cloud mapping. The system first implements shore segmentation based on Otsu’s method to generate masks covering water and sky regions, ensuring the SLAM system avoids extracting interfering feature points from these areas. Secondly, the deep learning network YOLOv8n-seg is employed to detect priori dynamic objects, with the motion consistency check method to identify non-priori dynamic feature points, collectively removing dynamic feature points. Additionally, the ELAS algorithm computes disparity maps, integrating depth information and dynamic object information to construct a static dense map. Experimental results demonstrate that, compared to the original ORB-SLAM3, the improved SLAM algorithm achieves superior localization accuracy in dynamic nearshore environments, significantly reduces the impact of dynamic objects on pose estimation, and successfully constructs ghosting-free static dense point cloud maps. | |
| 653 | |a Simultaneous localization and mapping | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Algorithms | ||
| 653 | |a Identification methods | ||
| 653 | |a Surface vehicles | ||
| 653 | |a Segmentation | ||
| 653 | |a Navigation | ||
| 653 | |a Unmanned vehicles | ||
| 653 | |a Modules | ||
| 653 | |a Localization | ||
| 653 | |a Mapping | ||
| 653 | |a Cameras | ||
| 653 | |a Methods | ||
| 653 | |a Pose estimation | ||
| 653 | |a Geometry | ||
| 653 | |a Vehicles | ||
| 653 | |a Masks | ||
| 653 | |a Semantics | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Zhang, Lan |u Shanghai Zhongchuan SDT-NERC Co., Ltd., Shanghai 201114, China | |
| 700 | 1 | |a Yu, Qiang |u Shanghai Zhongchuan SDT-NERC Co., Ltd., Shanghai 201114, China | |
| 700 | 1 | |a Bowen, Xing |u College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China; z17879573206@163.com | |
| 773 | 0 | |t Journal of Marine Science and Engineering |g vol. 13, no. 4 (2025), p. 679 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3194618489/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3194618489/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3194618489/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |