Abstract
This paper proposes a low-cost solution for mapping and locating UHF-band RFID tags in a 3D space using reader-equipped smartphones. Our solution includes a mobile augmented reality application for data collection and information visualization, and a cloud-based application server for calculating
locations of the reader-equipped smartphones and the read RFID tags. Our solution applies computer vision and motion sensing techniques to track 3D locations of the RFID reader based on the visual and inertial sensor data collected from the companion smartphones. Meanwhile, it obtains the exact locations of RFID tags by calculating their relative positions from the readers based on the Angle of Arrival (AoA) concept. Our solution can be implemented with any low-cost fixed transmit power RFID readers, since it only requires the readers to report identifiers of read RFID tags. Furthermore, our solution does not require
machine-controlled uniform movement of RFID readers, as it can handle the bias in the readings collected from randomly scattered positions. We have evaluated our solution with experiments in real environments using a commercially-off-the-shelf RFID reader and an Android phone. Results show that the average error in the positions of RFID tags is around 25cm for each of orthogonal axes on the floor plane, with the orders of RFID tags correctly detected in most cases.
locations of the reader-equipped smartphones and the read RFID tags. Our solution applies computer vision and motion sensing techniques to track 3D locations of the RFID reader based on the visual and inertial sensor data collected from the companion smartphones. Meanwhile, it obtains the exact locations of RFID tags by calculating their relative positions from the readers based on the Angle of Arrival (AoA) concept. Our solution can be implemented with any low-cost fixed transmit power RFID readers, since it only requires the readers to report identifiers of read RFID tags. Furthermore, our solution does not require
machine-controlled uniform movement of RFID readers, as it can handle the bias in the readings collected from randomly scattered positions. We have evaluated our solution with experiments in real environments using a commercially-off-the-shelf RFID reader and an Android phone. Results show that the average error in the positions of RFID tags is around 25cm for each of orthogonal axes on the floor plane, with the orders of RFID tags correctly detected in most cases.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 15th IEEE International Conference on Mobile Ad-hoc and Sensor Systems |
| Place of Publication | United States |
| Publisher | IEEE |
| Pages | 299-307 |
| Number of pages | 9 |
| ISBN (Electronic) | 978-1-5386-5580-1 |
| ISBN (Print) | 978-1-5386-5581-8 |
| DOIs | |
| Publication status | Published - 2018 |
| MoE publication type | A4 Conference publication |
| Event | IEEE International Conference on Mobile Ad-hoc and Sensor Systems - Chengdu, China Duration: 9 Oct 2018 → 12 Oct 2018 Conference number: 15 |
Publication series
| Name | IEEE International Conference on Mobile Ad-hoc and Sensor Systems |
|---|---|
| ISSN (Print) | 2155-6806 |
| ISSN (Electronic) | 2155-6814 |
Conference
| Conference | IEEE International Conference on Mobile Ad-hoc and Sensor Systems |
|---|---|
| Abbreviated title | MASS |
| Country/Territory | China |
| City | Chengdu |
| Period | 09/10/2018 → 12/10/2018 |
Keywords
- RFID
- localization
- augmented reality