Abstract

Radio Frequency Identification (RFID) achieves the identification of objects through electromagnetic waves and has wide applications in many areas. The system reports a list of tags in proximity without any knowledge about distance or bearing of the object. This paper presents an approach to integrate RFID phase and laser range information for the tracking of dynamic objects in an environment. The proposed system determines locations of objects by comparing the velocities estimated from two different systems. In particular, the laser range data is segmented into clusters using DBSCAN (Density-based spatial clustering of applications with noise). We compute the radial velocities of these clusters and compare them to the radial velocity estimated from RFID phase difference. The particle filtering is used to fuse the ambiguous phase measurements and to track moving objects with multiple hypothesis. The proposed approach uses the commercial off the shelf RFID devices and does not require the modelling of radio signal propagation. Experiments were conducted with a SCITOS G5 robot to verify the feasibility of the approach. The results showed that our approach can achieve a positioning accuracy of approx. 0.37 meters in a complex environment.

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