• DocumentCode
    583310
  • Title

    An efficient localization method using RFID tag floor localization and dead reckoning

  • Author

    Lee, Jewon ; Park, Youngsu ; Kim, Daehyun ; Choi, Minho ; Goh, Taedong ; Kim, Sang-Woo

  • Author_Institution
    Dept. of Electr. Eng., POSTECH, Pohang, South Korea
  • fYear
    2012
  • fDate
    17-21 Oct. 2012
  • Firstpage
    1452
  • Lastpage
    1456
  • Abstract
    A method for using radio-frequency identification tag floor based localization (RFTL) for indoor mobile robot localization is proposed. This method does not suffer from illumination or line of sight impairment issue, and it is less affected by the environment than is conventional localization. However, it faces an inherent limitation, in terms of its low accuracy. To overcome this problem, an efficient localization method using RFTL and dead reckoning is presented. To utilize this method, it is important to find a model of map for the tag recognition probabilities. With this in mind, this paper proposes two model structures: a deterministic ellipse model, and a two variable Gaussian random variable (RV) model. The results of experiments to verify and compare the performances of these two model structure methods are presented.
  • Keywords
    Gaussian processes; mobile robots; radiofrequency identification; Gaussian random variable model; RFID tag floor localization; RV model; dead reckoning; deterministic ellipse model; indoor mobile robot localization; map model; radiofrequency identification RFTL; tag recognition probabilities; Dead reckoning; Kalman filters; Load modeling; Mathematical model; Pattern recognition; Radiofrequency identification; Robots; Kalman filter; RFID; RTFL; localization; modeling; passive RFID tag; recognition pattern;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control, Automation and Systems (ICCAS), 2012 12th International Conference on
  • Conference_Location
    JeJu Island
  • Print_ISBN
    978-1-4673-2247-8
  • Type

    conf

  • Filename
    6393065