{"title":"基于多无人机闭环协调的gps拒绝环境下的定位保证导航","authors":"Shenghao Jiang, Macheng Shen","doi":"10.1109/AERO47225.2020.9172535","DOIUrl":null,"url":null,"abstract":"Consider a scenario where multiple Unmanned Aerial Vehicles (UAVs) autonomously collaborate with each other to explore an unknown environment where GPS is not available. A visual fiducial marker with known geometry is fixed on each UAV to provide relative localization between any pair of UAVs. Nevertheless, the UAV s need to plan their motion to ensure that the marker always appears in camera's field-of-view so that they can be localized. Such requirement limits the trajectory space of UAVs when they are exploring the environment. To solve this issue, our first technical contribution is an innovative multi-UAV spatial closed-loop coordination mechanism, which provides guaranteed relative localization wherever they are in the unknown and texture-less environment. The coordination, however, requires that the environment satisfy line-of-sight (LOS) constraints, and therefore necessities the division of the global environment into different subareas such that LOS constraints are met within each subarea. Our second contribution is a novel temporal-spatial pose graph to register different subareas into one global environment accurately. Finally, we present an iterative strategy to simultaneously maximize the volume of exploration space and minimize the localization error under the line-of-sight (LOS) constraints. Comparison with STOA visual localization techniques in simulated unknown environment demonstrates that our method is robust, accurate and independent of the environment.","PeriodicalId":114560,"journal":{"name":"2020 IEEE Aerospace Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Localization - guaranteed navigation in GPS-denied environment via multi-UAV closed-loop coordination\",\"authors\":\"Shenghao Jiang, Macheng Shen\",\"doi\":\"10.1109/AERO47225.2020.9172535\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Consider a scenario where multiple Unmanned Aerial Vehicles (UAVs) autonomously collaborate with each other to explore an unknown environment where GPS is not available. A visual fiducial marker with known geometry is fixed on each UAV to provide relative localization between any pair of UAVs. Nevertheless, the UAV s need to plan their motion to ensure that the marker always appears in camera's field-of-view so that they can be localized. Such requirement limits the trajectory space of UAVs when they are exploring the environment. To solve this issue, our first technical contribution is an innovative multi-UAV spatial closed-loop coordination mechanism, which provides guaranteed relative localization wherever they are in the unknown and texture-less environment. The coordination, however, requires that the environment satisfy line-of-sight (LOS) constraints, and therefore necessities the division of the global environment into different subareas such that LOS constraints are met within each subarea. Our second contribution is a novel temporal-spatial pose graph to register different subareas into one global environment accurately. Finally, we present an iterative strategy to simultaneously maximize the volume of exploration space and minimize the localization error under the line-of-sight (LOS) constraints. Comparison with STOA visual localization techniques in simulated unknown environment demonstrates that our method is robust, accurate and independent of the environment.\",\"PeriodicalId\":114560,\"journal\":{\"name\":\"2020 IEEE Aerospace Conference\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Aerospace Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AERO47225.2020.9172535\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Aerospace Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AERO47225.2020.9172535","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Localization - guaranteed navigation in GPS-denied environment via multi-UAV closed-loop coordination
Consider a scenario where multiple Unmanned Aerial Vehicles (UAVs) autonomously collaborate with each other to explore an unknown environment where GPS is not available. A visual fiducial marker with known geometry is fixed on each UAV to provide relative localization between any pair of UAVs. Nevertheless, the UAV s need to plan their motion to ensure that the marker always appears in camera's field-of-view so that they can be localized. Such requirement limits the trajectory space of UAVs when they are exploring the environment. To solve this issue, our first technical contribution is an innovative multi-UAV spatial closed-loop coordination mechanism, which provides guaranteed relative localization wherever they are in the unknown and texture-less environment. The coordination, however, requires that the environment satisfy line-of-sight (LOS) constraints, and therefore necessities the division of the global environment into different subareas such that LOS constraints are met within each subarea. Our second contribution is a novel temporal-spatial pose graph to register different subareas into one global environment accurately. Finally, we present an iterative strategy to simultaneously maximize the volume of exploration space and minimize the localization error under the line-of-sight (LOS) constraints. Comparison with STOA visual localization techniques in simulated unknown environment demonstrates that our method is robust, accurate and independent of the environment.