Abstract
An indoor global localization problem for a mobile robot based on Radio Frequency IDentification (RFID) is considered. The localization system consists of a reader installed on the robot which measures the phase of UHF-RFID signals coming from a set of passive tags deployed on the ceiling of the environment. Assuming only an approximate information is available at the beginning on the position of the tags, this paper presents an algorithm, based on a Multi-Hypothesis Extended Kalman Filter, which improves the initial estimate on the tag coordinates while simultaneously localizing the robot. Simulative and experimental results are reported to illustrate the effectiveness of the proposed approach.
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