Mapping of passive UHF RFID tags with a mobile robot using outlier detection and negative information

In this paper we propose a novel approach to classify detection events from a stream of radio-frequency identification (RFID) measurements for the purpose of mapping RFID transponders. Since raw readings from RFID readers only provide information on positive read attempts, i.e. the detections of a tag, we propose an outlier filter method solely based on the spatial extent of the sensor model that is used for the mapping process. Furthermore, we use this filter to actually classify detections as well as non-detections of tags into valid and invalid positive as well as negative detection events. We incorporate the different classes into our mapping pipeline and introduce several extensions to improve the mapping accuracy. Experimental results including the classification and mapping accuracy are presented to prove the effectiveness of our approach.