{"title":"UAV Indoor Localization Using 3D Laser Radar","authors":"Huai Wen, Wei Nie, Xiaolong Yang, Mu Zhou","doi":"10.1109/APCAP56600.2022.10068973","DOIUrl":null,"url":null,"abstract":"At present, unmanned aerial vehicle (UAV) mainly relies on GPS to achieve positioning in outdoor environment. However, the GPS signal is weak and easy to be lost in complex indoor environment. This paper presents an indoor positioning system for UAV using 3D laser radar. First, we receive the point cloud data through the UAV airborne lidar, subsequently, its edge feature points and plane feature points are extracted, and then the motion of UAV is estimated through the feature matching between the point cloud data at two consecutive times, In the meantime, a point cloud map is constructed. Finally, the public data set is used to test in the UAV simulation environment. The simulation results show that the localization accuracy of UAV can achieve 0.09m in indoor environment.","PeriodicalId":197691,"journal":{"name":"2022 IEEE 10th Asia-Pacific Conference on Antennas and Propagation (APCAP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 10th Asia-Pacific Conference on Antennas and Propagation (APCAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APCAP56600.2022.10068973","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
At present, unmanned aerial vehicle (UAV) mainly relies on GPS to achieve positioning in outdoor environment. However, the GPS signal is weak and easy to be lost in complex indoor environment. This paper presents an indoor positioning system for UAV using 3D laser radar. First, we receive the point cloud data through the UAV airborne lidar, subsequently, its edge feature points and plane feature points are extracted, and then the motion of UAV is estimated through the feature matching between the point cloud data at two consecutive times, In the meantime, a point cloud map is constructed. Finally, the public data set is used to test in the UAV simulation environment. The simulation results show that the localization accuracy of UAV can achieve 0.09m in indoor environment.
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