{"title":"An integrated UAV navigation system based on geo-registered 3D point cloud","authors":"Shuai Zhang, Shuaijun Wang, Chengyang Li, Guocheng Liu, Qi Hao","doi":"10.1109/MFI.2017.8170396","DOIUrl":null,"url":null,"abstract":"The autonomous navigation of unmanned aerial vehicles (UAVs) require a lot of sensing modalities to improve their cruise efficiency. This paper presents a system for autonomous navigation and path planning of UAVs in GPS-denied environment based on the fusion of geo-registered 3D point clouds with proprioceptive sensors (IMU, odometry and barometer) and the 2D Google maps. The contributions of this paper are illustrated as follows: 1) combination of 2D map and geo-registered 3D point clouds; 2) registration of local point cloud to global geo-registered 3D point clouds; 3) integration of visual odometry, IMU, GPS and barometer. Experiment and simulation results demonstrate the efficacy and robustness of the proposed system.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MFI.2017.8170396","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The autonomous navigation of unmanned aerial vehicles (UAVs) require a lot of sensing modalities to improve their cruise efficiency. This paper presents a system for autonomous navigation and path planning of UAVs in GPS-denied environment based on the fusion of geo-registered 3D point clouds with proprioceptive sensors (IMU, odometry and barometer) and the 2D Google maps. The contributions of this paper are illustrated as follows: 1) combination of 2D map and geo-registered 3D point clouds; 2) registration of local point cloud to global geo-registered 3D point clouds; 3) integration of visual odometry, IMU, GPS and barometer. Experiment and simulation results demonstrate the efficacy and robustness of the proposed system.
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