Name
Abstract | ||
|---|---|---|
We study a new problem, refined localization, in this paper. Refined localization calculates the location of an object in high precision, given that the object is in a relatively small region such as the surface of a table. Refined localization is useful in many cyber-physical systems such as industrial autonomous robots. Existing vision-based approaches suffer from several disadvantages, including good lighting conditions, line of sight, pre-learning process, and high computation overhead. Also vision-based approaches cannot differentiate objects with similar colors and shapes. This paper presents a new refined localization system, called Trio, which uses passive Radio Frequency Identification (RFID) tags for low cost and easy deployment. Trio provides a new angle to utilize RF interference for tag localization by modeling the equivalent circuits of coupled tags. We implement our prototype using commercial off-the-shelf RFID reader and tags. Extensive experiment results demonstrate that Trio effectively achieves high accuracy of refined localization, i.e., < 1 cm errors for several types of main stream tags. |
Year | Venue | Field |
|---|---|---|
2018 | IEEE INFOCOM | Computer vision,Dipole antenna,Computer science,Electromagnetic interference,Electrical impedance,Interference (wave propagation),Artificial intelligence,Robot,Radio-frequency identification,Equivalent circuit,Computation,Distributed computing |
DocType | ISSN | Citations |
Conference | 0743-166X | 4 |
PageRank | References | Authors |
0.41 | 0 | 8 |