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This paper proposes a global indoor localization system based on the radio frequency identification (RFID) technology. A reader, installed on the robot, measures the received signal strength indication (RSSI) and the phase shift of UHF-RFID signals coming from a set of passive tags deployed on the ceiling of the environment. The position of the tags in the environment is only roughly known at the beginning. Exploiting the complementary features of RSSI and phase-shift information in RFID signals (together with odometry data), a multihypothesis extended and unscented Kalman filter is proposed to localize the robot and to simultaneously improve the initial estimate on the tag coordinates. The simulative and experimental results are reported to illustrate the effectiveness of the proposed approach.
Francesco Martinelli (2015) studied this question.