An Optimal Viewpoint-Guided Visual Indexing Method for UAV Autonomous Localization
The autonomous positioning of drone-based remote sensing plays an important role in navigation in urban environments. Due to GNSS (Global Navigation Satellite System) signal occlusion, obtaining precise drone locations is still a challenging issue. Inspired by vision-based positioning methods, we pr...
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| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2194 |
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| author | Zhiyang Ye Yukun Zheng Zheng Ji Wei Liu |
| author_facet | Zhiyang Ye Yukun Zheng Zheng Ji Wei Liu |
| author_sort | Zhiyang Ye |
| collection | DOAJ |
| description | The autonomous positioning of drone-based remote sensing plays an important role in navigation in urban environments. Due to GNSS (Global Navigation Satellite System) signal occlusion, obtaining precise drone locations is still a challenging issue. Inspired by vision-based positioning methods, we proposed an autonomous positioning method based on multi-view reference images rendered from the scene’s 3D geometric mesh and apply a bag-of-words (BoW) image retrieval pipeline to achieve efficient and scalable positioning, without utilizing deep learning-based retrieval or 3D point cloud registration. To minimize the number of reference images, scene coverage quantification and optimization are employed to generate the optimal viewpoints. The proposed method jointly exploits a visual-bag-of-words tree to accelerate reference image retrieval and improve retrieval accuracy, and the Perspective-n-Point (PnP) algorithm is utilized to obtain the drone’s pose. Experiments are conducted in urban real-word scenarios and the results show that positioning errors are decreased, with accuracy ranging from sub-meter to 5 m and an average latency of 0.7–1.3 s; this indicates that our method significantly improves accuracy and latency, offering robust, real-time performance over extensive areas without relying on GNSS or dense point clouds. |
| format | Article |
| id | doaj-art-d94b1b6a79cb494ebfff62cbadd83e36 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-d94b1b6a79cb494ebfff62cbadd83e362025-08-20T02:36:33ZengMDPI AGRemote Sensing2072-42922025-06-011713219410.3390/rs17132194An Optimal Viewpoint-Guided Visual Indexing Method for UAV Autonomous LocalizationZhiyang Ye0Yukun Zheng1Zheng Ji2Wei Liu3School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaXi’an Institute of Surveying and Mapping, Xi’an 710054, ChinaThe autonomous positioning of drone-based remote sensing plays an important role in navigation in urban environments. Due to GNSS (Global Navigation Satellite System) signal occlusion, obtaining precise drone locations is still a challenging issue. Inspired by vision-based positioning methods, we proposed an autonomous positioning method based on multi-view reference images rendered from the scene’s 3D geometric mesh and apply a bag-of-words (BoW) image retrieval pipeline to achieve efficient and scalable positioning, without utilizing deep learning-based retrieval or 3D point cloud registration. To minimize the number of reference images, scene coverage quantification and optimization are employed to generate the optimal viewpoints. The proposed method jointly exploits a visual-bag-of-words tree to accelerate reference image retrieval and improve retrieval accuracy, and the Perspective-n-Point (PnP) algorithm is utilized to obtain the drone’s pose. Experiments are conducted in urban real-word scenarios and the results show that positioning errors are decreased, with accuracy ranging from sub-meter to 5 m and an average latency of 0.7–1.3 s; this indicates that our method significantly improves accuracy and latency, offering robust, real-time performance over extensive areas without relying on GNSS or dense point clouds.https://www.mdpi.com/2072-4292/17/13/2194optimal viewpointGNSS-deniedautonomous localizationdrones |
| spellingShingle | Zhiyang Ye Yukun Zheng Zheng Ji Wei Liu An Optimal Viewpoint-Guided Visual Indexing Method for UAV Autonomous Localization Remote Sensing optimal viewpoint GNSS-denied autonomous localization drones |
| title | An Optimal Viewpoint-Guided Visual Indexing Method for UAV Autonomous Localization |
| title_full | An Optimal Viewpoint-Guided Visual Indexing Method for UAV Autonomous Localization |
| title_fullStr | An Optimal Viewpoint-Guided Visual Indexing Method for UAV Autonomous Localization |
| title_full_unstemmed | An Optimal Viewpoint-Guided Visual Indexing Method for UAV Autonomous Localization |
| title_short | An Optimal Viewpoint-Guided Visual Indexing Method for UAV Autonomous Localization |
| title_sort | optimal viewpoint guided visual indexing method for uav autonomous localization |
| topic | optimal viewpoint GNSS-denied autonomous localization drones |
| url | https://www.mdpi.com/2072-4292/17/13/2194 |
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