RFID-Enabled Location Fingerprinting based on Similarity Models from Probabilistic Similarity Measures

Abstract

In this work we present a novel fingerprint similarity sensor model for the purpose of localizing a mobile robot with passive ultra-high frequency (UHF) radio-frequency identification (RFID) through location fingerprinting. We firstly evaluate the performance of different probabilistic similarity measures applied to received signal strength (RSS) and compare them to previous results obtained with well known vector similarity measures. We furthermore extend the observation model used in a particle filter to dynamically adapt to the uncertainty of single candidate fingerprints by using their similarity to the current observation. For this purpose, we derive a new likelihood function and introduce an alternative way of selecting candidate fingerprints using a combination of their signal space similarity as well as the distance between the currently estimated pose and reference fingerprints. Results obtained from experiments in two different environments highlight the improved accuracy as well as robustness of the proposed methods.

BibTeX

@inproceedings{koch2016icra,
  author = {Artur Koch and Andreas Zell},
  title = {{RFID}-Enabled Location Fingerprinting based on Similarity Models
from Probabilistic Similarity Measures},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  year = {2016},
  address = {Stockholm, Sweden},
  month = {May},
  publisher = {IEEE},
  abstract = {In this work we present a novel fingerprint similarity sensor model for the purpose of localizing a mobile robot with passive ultra-high frequency (UHF) radio-frequency identification (RFID) through location fingerprinting. We firstly evaluate the performance of different probabilistic similarity measures applied to received signal strength (RSS) and compare them to previous results obtained with well known vector similarity measures. We furthermore extend the observation model used in a particle filter to dynamically adapt to the uncertainty of single candidate fingerprints by using their similarity to the current observation. For this purpose, we derive a new likelihood function and introduce an alternative way of selecting candidate fingerprints using a combination of their signal space similarity as well as the distance between the currently estimated pose and reference fingerprints. Results obtained from experiments in two different environments highlight the improved accuracy as well as robustness of the proposed methods.},
}