Indoor localization: Automatically constructing today's radio map by iRobot and RFIDs

Lun Wu Yeh*, Ming Shiou Hsu, Yueh Feng Lee, Yu-Chee Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations


For outdoor localization, GPS already provides a satisfactory solution. For indoor localization, however, a globally usable solution is still missing. One promising direction that is proposed recently is the fingerprinting-based solution. It involves a training phase to collect the radio signal strength (RSS) patterns in fields where localization is needed into a database (called radio map). The radio signal could be from WiFi access points, GSM base stations, or other RF-based networks. Then, during the positioning phase, an object which is interested in its own location can collect its current RSS pattern and compare it against the radio map established in the training phase to identify its possible location. We present an interesting system based a robot and numerous cheap RFID tags deployed on the ground to automate the training process and, more importantly, to frequently update radio maps to reflect the current RSS patterns. This not only significantly reduces human labors but also improves positioning accuracy.

Original languageEnglish
Title of host publicationIEEE Sensors 2009 Conference - SENSORS 2009
Number of pages4
StatePublished - 1 Dec 2009
EventIEEE Sensors 2009 Conference - SENSORS 2009 - Christchurch, New Zealand
Duration: 25 Oct 200928 Oct 2009

Publication series

NameProceedings of IEEE Sensors


ConferenceIEEE Sensors 2009 Conference - SENSORS 2009
CountryNew Zealand


  • Indoor positioning
  • Localization
  • Pervasive computing
  • RFID
  • Robot

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