Solving the Global Localization Problem for Indoor Mobile Robots

  • Leonardo Romero
  • Eduardo Morales
  • Enrique Sucar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

Global localization is the problem of determining the position of a robot under global uncertainty. This problem can be divided in two phases: 1) from the sensor data (or sensor view), determine clusters of hypotheses where the robot can be; and 2) devise a strategy by which the robot can correctly eliminate all but the right location. In the second phase, previous approaches consider an ideal robot, a robot with a perfect odometer, to predict robot movements. This paper introduces a non deterministic prediction approach based on a Markov localization that include an uncertainty model for the movements of the robot. The non deterministic model can help to solve situations where a deterministic or ideal model fails. Hypotheses are clustered and a greedy search algorithm determines the robot movements to reduce the number of clusters of hypotheses. This approach is tested using a simulated mobile robot with promising results.

Keywords

Mobile Robot Robot Movement Global Localization Goal Cell Sensor View 
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 2003

Authors and Affiliations

  • Leonardo Romero
    • 1
  • Eduardo Morales
    • 2
  • Enrique Sucar
    • 2
  1. 1.UMSNHMoreliaMexico
  2. 2.ITESMMexico

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