Multi-Source Image Matching Algorithms for UAV Positioning: Benchmarking, Innovation, and Combined Strategies
文献类型：期刊论文
| 作者 | Liu, Jianli3; Xiao, Jincheng3; Ren, Yafeng3; Liu, Fei2; Yue, Huanyin1; Ye, Huping1; Li, Yingcheng3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-08-01 |
| 卷号 | 16期号:16页码:18 |
| 关键词 | UAV positioning image matching features keypoints benchmarking consistency verification combined strategies |
| DOI | 10.3390/rs16163025 |
| 产权排序 | 3 |
| 英文摘要 | The accuracy and reliability of unmanned aerial vehicle (UAV) visual positioning systems are dependent on the performance of multi-source image matching algorithms. Despite many advancements, targeted performance evaluation frameworks and datasets for UAV positioning are still lacking. Moreover, existing consistency verification methods such as Random Sample Consensus (RANSAC) often fail to entirely eliminate mismatches, affecting the precision and stability of the matching process. The contributions of this research include the following: (1) the development of a benchmarking framework accompanied by a large evaluation dataset for assessing the efficacy of multi-source image matching algorithms; (2) the results of this benchmarking framework indicate that combinations of multiple algorithms significantly enhance the Match Success Rate (MSR); (3) the introduction of a novel Geographic Geometric Consistency (GGC) method that effectively identifies mismatches within RANSAC results and accommodates rotational and scale variations; and (4) the implementation of a distance threshold iteration (DTI) method that, according to experimental results, achieves an 87.29% MSR with a Root Mean Square Error (RMSE) of 1.11 m (2.22 pixels) while maintaining runtime at only 1.52 times that of a single execution, thus optimizing the trade-off between MSR, accuracy, and efficiency. Furthermore, when compared with existing studies on UAV positioning, the multi-source image matching algorithms demonstrated a sub-meter positioning error, significantly outperforming the comparative method. These advancements are poised to enhance the application of advanced multi-source image matching technologies in UAV visual positioning. |
| WOS关键词 | LOCALIZATION |
| 资助项目 | National Key Research and Development Program of China ; Central Guiding Local Technology Development[226Z5901G] ; [2022YFC3320802] ; [2023YFB3905704] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001304793200001 |
| 出版者 | MDPI |
| 资助机构 | National Key Research and Development Program of China ; Central Guiding Local Technology Development |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/208892]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Li, Yingcheng |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Beijing Univ Civil Engn & Architecture, Sch Geomat & Urban Spatial Informat, Beijing 102616, Peoples R China 3.China TopRS Technol Co Ltd, Natl Engn Res Ctr Surveying & Mapping, Beijing 100039, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Jianli,Xiao, Jincheng,Ren, Yafeng,et al. Multi-Source Image Matching Algorithms for UAV Positioning: Benchmarking, Innovation, and Combined Strategies[J]. REMOTE SENSING,2024,16(16):18. |
| APA | Liu, Jianli.,Xiao, Jincheng.,Ren, Yafeng.,Liu, Fei.,Yue, Huanyin.,...&Li, Yingcheng.(2024).Multi-Source Image Matching Algorithms for UAV Positioning: Benchmarking, Innovation, and Combined Strategies.REMOTE SENSING,16(16),18. |
| MLA | Liu, Jianli,et al."Multi-Source Image Matching Algorithms for UAV Positioning: Benchmarking, Innovation, and Combined Strategies".REMOTE SENSING 16.16(2024):18. |
入库方式： OAI收割
来源：地理科学与资源研究所
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