M. Longhi, A. Millane, Z. Taylor, J. Nieto, R. Siegwart, G. Marrocco
{"title":"An Integrated MAV-RFID System for Geo-referenced Monitoring of Harsh Environments","authors":"M. Longhi, A. Millane, Z. Taylor, J. Nieto, R. Siegwart, G. Marrocco","doi":"10.1109/CAMA.2018.8530596","DOIUrl":null,"url":null,"abstract":"Micro Aerial Vehicles (MAVs) equipped with lightweight Radio Frequency Identification (RFID) sensor dataloggers, have the potential to assist in achieving environmental awareness in a large range of situations. However, in order to gain such insight, the system must be able to accurately localize itself and fuse any readings of its surroundings into a consistent map. In this paper we demonstrate how camera, IMU and environmental data obtained with an RFID-enabled temperature sensor may be merged together to create accurate 3D maps along the MAV curvilinear trajectories in unknown locations. The idea is demonstrated through experimentations in both indoor and outdoor harsh environments.","PeriodicalId":112989,"journal":{"name":"2018 IEEE Conference on Antenna Measurements & Applications (CAMA)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Conference on Antenna Measurements & Applications (CAMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAMA.2018.8530596","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Micro Aerial Vehicles (MAVs) equipped with lightweight Radio Frequency Identification (RFID) sensor dataloggers, have the potential to assist in achieving environmental awareness in a large range of situations. However, in order to gain such insight, the system must be able to accurately localize itself and fuse any readings of its surroundings into a consistent map. In this paper we demonstrate how camera, IMU and environmental data obtained with an RFID-enabled temperature sensor may be merged together to create accurate 3D maps along the MAV curvilinear trajectories in unknown locations. The idea is demonstrated through experimentations in both indoor and outdoor harsh environments.