{"title":"基于运动驱动和视觉特征匹配的混合自适应无人机识别:海报摘要","authors":"C. Dominguez, Xinlei Chen, Pei Zhang","doi":"10.1145/3055031.3055072","DOIUrl":null,"url":null,"abstract":"Unmanned aerial vehicle (UAV) swarms provide situation awareness in tasks such as emergency response, search and rescue, etc. However, most of these scenarios take place in GPS-denied environments, where accurately localizing each UAV is challenging. Heterogeneous UAV swarms, in which only a subset of the drones carry cameras, face the additional challenge of identifying each individual UAV to avoid sending position updates to the wrong drone, thus crashing. This work presents an identification mechanism based on the correlation between motion observed from external camera, and acceleration measured on each UAV's accelerometer.","PeriodicalId":206082,"journal":{"name":"Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Hybrid and adaptive drone identification through motion actuation and vision feature matching: poster abstract\",\"authors\":\"C. Dominguez, Xinlei Chen, Pei Zhang\",\"doi\":\"10.1145/3055031.3055072\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unmanned aerial vehicle (UAV) swarms provide situation awareness in tasks such as emergency response, search and rescue, etc. However, most of these scenarios take place in GPS-denied environments, where accurately localizing each UAV is challenging. Heterogeneous UAV swarms, in which only a subset of the drones carry cameras, face the additional challenge of identifying each individual UAV to avoid sending position updates to the wrong drone, thus crashing. This work presents an identification mechanism based on the correlation between motion observed from external camera, and acceleration measured on each UAV's accelerometer.\",\"PeriodicalId\":206082,\"journal\":{\"name\":\"Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3055031.3055072\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3055031.3055072","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hybrid and adaptive drone identification through motion actuation and vision feature matching: poster abstract
Unmanned aerial vehicle (UAV) swarms provide situation awareness in tasks such as emergency response, search and rescue, etc. However, most of these scenarios take place in GPS-denied environments, where accurately localizing each UAV is challenging. Heterogeneous UAV swarms, in which only a subset of the drones carry cameras, face the additional challenge of identifying each individual UAV to avoid sending position updates to the wrong drone, thus crashing. This work presents an identification mechanism based on the correlation between motion observed from external camera, and acceleration measured on each UAV's accelerometer.