{"title":"用于无人机目标定位的点集配准","authors":"Dhruvil Darji, G. Vejarano","doi":"10.1145/3586575","DOIUrl":null,"url":null,"abstract":"The problem of point set registration (PSR) on images obtained using a group of unmanned aerial vehicles (UAVs) is addressed in this article. UAVs are given a flight plan each, which they execute autonomously. A flight plan consists of a series of GPS coordinates and altitudes that indicate where the UAV stops and hovers momentarily to capture an image of stationary targets on ground. A PSR algorithm is proposed that, given any two images and corresponding GPS coordinates and altitude, estimates the overlap between the images, identifies targets in the overlapping area, and matches these targets according to the geometric patterns they form. The algorithm estimates the overlap considering the error in UAVs’ locations due to wind, and it differentiates similar geometrical patterns by their GPS location. The algorithm is evaluated using the percentage of targets in the overlapping area that are matched correctly and the percentage of overlapping images matched correctly. The target-matching rate achieved using only the GPS locations of targets varied from 44% to 55% for target densities that varied from 6.4 down to 3.2 targets/m2. The proposed algorithm achieved target-matching rates of 48% to 87%. Well-known algorithms for PSR achieved lower rates on average.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2023-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Point Set Registration for Target Localization Using Unmanned Aerial Vehicles\",\"authors\":\"Dhruvil Darji, G. Vejarano\",\"doi\":\"10.1145/3586575\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of point set registration (PSR) on images obtained using a group of unmanned aerial vehicles (UAVs) is addressed in this article. UAVs are given a flight plan each, which they execute autonomously. A flight plan consists of a series of GPS coordinates and altitudes that indicate where the UAV stops and hovers momentarily to capture an image of stationary targets on ground. A PSR algorithm is proposed that, given any two images and corresponding GPS coordinates and altitude, estimates the overlap between the images, identifies targets in the overlapping area, and matches these targets according to the geometric patterns they form. The algorithm estimates the overlap considering the error in UAVs’ locations due to wind, and it differentiates similar geometrical patterns by their GPS location. The algorithm is evaluated using the percentage of targets in the overlapping area that are matched correctly and the percentage of overlapping images matched correctly. The target-matching rate achieved using only the GPS locations of targets varied from 44% to 55% for target densities that varied from 6.4 down to 3.2 targets/m2. The proposed algorithm achieved target-matching rates of 48% to 87%. Well-known algorithms for PSR achieved lower rates on average.\",\"PeriodicalId\":43641,\"journal\":{\"name\":\"ACM Transactions on Spatial Algorithms and Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2023-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Spatial Algorithms and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3586575\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Spatial Algorithms and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3586575","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"REMOTE SENSING","Score":null,"Total":0}
Point Set Registration for Target Localization Using Unmanned Aerial Vehicles
The problem of point set registration (PSR) on images obtained using a group of unmanned aerial vehicles (UAVs) is addressed in this article. UAVs are given a flight plan each, which they execute autonomously. A flight plan consists of a series of GPS coordinates and altitudes that indicate where the UAV stops and hovers momentarily to capture an image of stationary targets on ground. A PSR algorithm is proposed that, given any two images and corresponding GPS coordinates and altitude, estimates the overlap between the images, identifies targets in the overlapping area, and matches these targets according to the geometric patterns they form. The algorithm estimates the overlap considering the error in UAVs’ locations due to wind, and it differentiates similar geometrical patterns by their GPS location. The algorithm is evaluated using the percentage of targets in the overlapping area that are matched correctly and the percentage of overlapping images matched correctly. The target-matching rate achieved using only the GPS locations of targets varied from 44% to 55% for target densities that varied from 6.4 down to 3.2 targets/m2. The proposed algorithm achieved target-matching rates of 48% to 87%. Well-known algorithms for PSR achieved lower rates on average.
期刊介绍:
ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes the highest quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective in that it spans a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually) such as geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.