{"title":"A SLAM algorithm based on range and bearing estimation of passive UHF-RFID tags","authors":"F. Martinelli, Fabrizio Romanelli","doi":"10.1109/RFID-TA53372.2021.9617420","DOIUrl":null,"url":null,"abstract":"In this paper we propose a Simultaneous Localization and Mapping (SLAM) algorithm for a mobile robot measuring the phase of the signal backscattered by a set of passive UHF-RFID tags, deployed in unknown position on the ceiling of the environment. The solution is based on a two level architecture. On a first (slave) level, a set of Multi-Hypothesis Extended Kalman Filters (MHEKF), one for each tag, estimates the range and the bearing of the tag with respect to the robot. On a second (master) level, the range and the bearing information of the responding tags is used in an EKF-SLAM algorithm which solves the SLAM problem. The proposed approach is more robust and computationally efficient with respect to other approaches available in the literature.","PeriodicalId":212607,"journal":{"name":"2021 IEEE International Conference on RFID Technology and Applications (RFID-TA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on RFID Technology and Applications (RFID-TA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RFID-TA53372.2021.9617420","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
In this paper we propose a Simultaneous Localization and Mapping (SLAM) algorithm for a mobile robot measuring the phase of the signal backscattered by a set of passive UHF-RFID tags, deployed in unknown position on the ceiling of the environment. The solution is based on a two level architecture. On a first (slave) level, a set of Multi-Hypothesis Extended Kalman Filters (MHEKF), one for each tag, estimates the range and the bearing of the tag with respect to the robot. On a second (master) level, the range and the bearing information of the responding tags is used in an EKF-SLAM algorithm which solves the SLAM problem. The proposed approach is more robust and computationally efficient with respect to other approaches available in the literature.