Recently, researchers showed growing interest in utilizing UHF Radio-Frequency Identification (RFID) technology for localizing tagged items with mobile robots in industrial scenarios. In this paper we present a novel three-dimensional (3D) probability sensor model of RFID antennas in the context of mapping passive RFID tags with mobile robots. The proposed 3D sensor model characterizes both detection rates and received signal strength (RSS). Compared to 2D-sensor model based approaches, the 3D model gains a higher mapping accuracy for 2D position estimation. Specially, with this sensor model, we are able to localize the tags in 3D by integrating the measurements from a pair of RFID antennas mounted at different heights of the robot. Furthermore, by integrating negative information (i.e., non-detections), the 3D mapping accuracy can be improved. Additionally, we utilize KLD-sampling to reduce the number of particles for our specific application, so that our algorithm can be performed online. Indoor experiments with a Scitos G5 robot demonstrate the effectiveness of our approach.
[pdf]
@inproceedings{RanArtur_IROS_2013,
author = {Ran Liu and Artur Koch and Andreas Zell},
title = {{Mapping UHF RFID Tags with a Mobile Robot using 3D Sensor Model}},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS 2013)},
year = {2013},
address = {Big Sight, Tokyo, Japan},
month = {November},
abstract = {Recently, researchers showed growing interest in utilizing UHF Radio-Frequency
Identification (RFID) technology for localizing tagged items with
mobile robots in industrial scenarios. In this paper we present a
novel three-dimensional (3D) probability sensor model of RFID antennas
in the context of mapping passive RFID tags with mobile robots. The
proposed 3D sensor model characterizes both detection rates and received
signal strength (RSS). Compared to 2D-sensor model based approaches,
the 3D model gains a higher mapping accuracy for 2D position estimation.
Specially, with this sensor model, we are able to localize the tags
in 3D by integrating the measurements from a pair of RFID antennas
mounted at different heights of the robot. Furthermore, by integrating
negative information (i.e., non-detections), the 3D mapping accuracy
can be improved. Additionally, we utilize KLD-sampling to reduce
the number of particles for our specific application, so that our
algorithm can be performed online. Indoor experiments with a Scitos
G5 robot demonstrate the effectiveness of our approach. },
days = {3-8},
url = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2013/ran_iros_2013.pdf}
}