Robust Positioning a Mobile Robot with Active Beacon Sensors

  • JaeMu Yun
  • SungBu Kim
  • JangMyung Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


With the development of service robots and with the emerging concept of the ubiquitous world, localization of a mobile robot has become a popular issue. Even though several localization schemes have been previously, none of them is free from the economical constraints placed on commercial robots. To implement a real time commercial localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed in this paper. Design and implementation of the localization system comes from the usage of active beacon systems each of which is composed of an RFID receiver and an ultra-sonic transmitter. The RFID receiver gets the synchronization signal from the mobile robot and the ultra-sonic transmitter sends out a short-term signal to be used for measuring the distance from the beacon to the mobile robot. The position of a mobile robot in a three dimensional space can be calculated basically from the distance information from three beacons which have their own absolute position information. One of the interesting problems comes from the static localization scheme. That is, the collision avoidance of surrounding objects can be planned and controlled efficiently if the velocity of the moving object can be estimated. The main contribution of this paper is developing a dynamic localization scheme to be used for the moving objects, which is a new application in the field of active beacon sensors. In the dynamic localization scheme, sensor fusion techniques and extended Kalman filter have been used to improve the estimation accuracy. Experimental results demonstrate the effectiveness and feasibility of this scheme.


Mobile Robot Kalman Filter Extend Kalman Filter Service Robot Ultrasonic Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • JaeMu Yun
    • 1
  • SungBu Kim
    • 1
  • JangMyung Lee
    • 1
  1. 1.Department of Electronics EngineeringPusan National UniversityBusanKorea

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