Di Hu;Xia Yuan;Huiying Xi;Jie Li;Zhenbo Song;Fengchao Xiong;Kai Zhang;Chunxia Zhao
{"title":"受道路结构启发的 UGV 卫星跨视角地理定位","authors":"Di Hu;Xia Yuan;Huiying Xi;Jie Li;Zhenbo Song;Fengchao Xiong;Kai Zhang;Chunxia Zhao","doi":"10.1109/JSTARS.2024.3457756","DOIUrl":null,"url":null,"abstract":"This article presents a new approach to address the challenge of combining ground-based LiDAR data with satellite images for cross-view image geo-localization. The task is to figure out the position and orientation of the LiDAR within the given satellite image. While previous research has mainly focused on imagery, the integration of ground-based point clouds with satellite images has been limited due to significant differences in modalities. To release this limitation, we propose a novel method that utilizes the road structure as a consistent reference between satellite images and ground LiDAR data for accurate geo-localization. Our methodology encompasses the extraction of road structures from both point clouds and satellite images. To extract road structures from point clouds, we leverage the enhanced viewpoint beam model, which effectively captures the spatial characteristics of ground landmarks. In addition, we utilize fractional-order differential-based super-resolution technology for satellite images to improve road structure detection, ensuring reliable performance across different altitudes. Following this, our approach involves matching road structures from the ground and satellite views, simplifying the localization process to a template-matching task. Consequently, we successfully address the challenge of accurately determining the 3-DoF pose of the LiDAR within the satellite image context. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in geo-localization, outperforming comparable methods. In addition, the approach shows versatility across various altitudes.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675432","citationCount":"0","resultStr":"{\"title\":\"Road Structure Inspired UGV-Satellite Cross-View Geo-Localization\",\"authors\":\"Di Hu;Xia Yuan;Huiying Xi;Jie Li;Zhenbo Song;Fengchao Xiong;Kai Zhang;Chunxia Zhao\",\"doi\":\"10.1109/JSTARS.2024.3457756\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article presents a new approach to address the challenge of combining ground-based LiDAR data with satellite images for cross-view image geo-localization. The task is to figure out the position and orientation of the LiDAR within the given satellite image. While previous research has mainly focused on imagery, the integration of ground-based point clouds with satellite images has been limited due to significant differences in modalities. To release this limitation, we propose a novel method that utilizes the road structure as a consistent reference between satellite images and ground LiDAR data for accurate geo-localization. Our methodology encompasses the extraction of road structures from both point clouds and satellite images. To extract road structures from point clouds, we leverage the enhanced viewpoint beam model, which effectively captures the spatial characteristics of ground landmarks. In addition, we utilize fractional-order differential-based super-resolution technology for satellite images to improve road structure detection, ensuring reliable performance across different altitudes. Following this, our approach involves matching road structures from the ground and satellite views, simplifying the localization process to a template-matching task. Consequently, we successfully address the challenge of accurately determining the 3-DoF pose of the LiDAR within the satellite image context. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in geo-localization, outperforming comparable methods. 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This article presents a new approach to address the challenge of combining ground-based LiDAR data with satellite images for cross-view image geo-localization. The task is to figure out the position and orientation of the LiDAR within the given satellite image. While previous research has mainly focused on imagery, the integration of ground-based point clouds with satellite images has been limited due to significant differences in modalities. To release this limitation, we propose a novel method that utilizes the road structure as a consistent reference between satellite images and ground LiDAR data for accurate geo-localization. Our methodology encompasses the extraction of road structures from both point clouds and satellite images. To extract road structures from point clouds, we leverage the enhanced viewpoint beam model, which effectively captures the spatial characteristics of ground landmarks. In addition, we utilize fractional-order differential-based super-resolution technology for satellite images to improve road structure detection, ensuring reliable performance across different altitudes. Following this, our approach involves matching road structures from the ground and satellite views, simplifying the localization process to a template-matching task. Consequently, we successfully address the challenge of accurately determining the 3-DoF pose of the LiDAR within the satellite image context. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in geo-localization, outperforming comparable methods. In addition, the approach shows versatility across various altitudes.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.