Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/A8831FF7-0293-4A4E-A4BD-E36F4D27001D | ||
| Year | 2020 | ||
| Type of Item | Conference Publication | ||
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| Bibliographic Citation | A. Tzitzis, A. Filotheou, S. Siachalou, E. Tsardoulias, S. Megalou, A. Bletsas, K. Panayiotou, A. Symeonidis, T. Yioultsis, and A. G. Dimitriou, "Real-time 3D localization of RFID-tagged products by ground robots and drones with commercial off-the-shelf RFID equipment: challenges and solutions," in IEEE Int. Conf. RFID, 2020, pp. 1-8, doi: 10.1109/RFID49298.2020.9244904. https://doi.org/10.1109/RFID49298.2020.9244904 | ||
| Appears in Collections |
In this paper we investigate the problem of localizing passive RFID tags by ground robots and drones. We focus on autonomous robots, capable of entering a previously unknown environment, creating a 3D map of it, navigating safely in it, localizing themselves while moving, then localizing all RFID tagged objects and pinpointing their locations in the 3D map with cm accuracy. To the best of our knowledge, this is the first paper that presents the complex joint problem, including challenges from the field of robotics - i) sensors utilization, ii) local and global path planners, iii) navigation, iv) simultaneous localization of the robot and mapping - and from the field of RFIDs - vi) localization of the tags. We restrict our analysis to solutions, involving commercial UHF EPC Gen2 RFID tags, commercial off-the-self RFID readers and 3D real-time-only methods for tag-localization. We briefly present a new method, suitable for real-time 3D inventorying, and compare it with our two recent methods. Comparison is carried out on a new set of experiments, conducted in a multipath-rich indoor environment, where the actual problem is treated; i.e. our prototype robot constructs a 3D map, navigates in the environment, continuously estimates its poses as well as the locations of the surrounding tags. Localization results are given in a few seconds for 100 tags, parsing approximately 100000 measured samples from 4 antennas, collected within 4 minutes and achieving a mean 3D error of 25cm, which includes the error propagating from robotics and the uncertainty related to the "ground truth" of the tags' placement.
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