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Monte Carlo Localization Using SIFT Features

  • Arturo Gil
  • Óscar Reinoso
  • Asunción Vicente
  • César Fernández
  • Luis Payá
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3522)

Abstract

The ability of finding its situation in a given environment is crucial for an autonomous agent. While navigating through a space, a mobile robot must be capable of finding its location in a map of the environment (i.e. its pose < x, y, θ>), otherwise, the robot will not be able to complete its task. This problem becomes specially challenging if the robot does not possess any external measure of its global position. Typically, dead-reckoning systems do fail in the estimation of robot’s pose when working for long periods of time. In this paper we present a localization method based on the Monte Carlo algorithm. During the last decade this method has been extensively tested in the field of mobile Robotics, proving to be both robust and efficient. On the other hand, our approach takes advantage from the use of a vision sensor. In particular, we have chosen to use SIFT features as visual landmarks finding them suitable for the global localization of a mobile robot. We have succesfully tested our approach in a B21r mobile robot, achieving to globally localize the robot in few iterations. The technique is suitable for office-like environments and behaves correctly in the presence of people and moving objects.

Keywords

Mobile Robot Mobile Agent Scale Invariant Feature Transform Stereo Vision Monte Carlo Algorithm 
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 2005

Authors and Affiliations

  • Arturo Gil
    • 1
  • Óscar Reinoso
    • 1
  • Asunción Vicente
    • 1
  • César Fernández
    • 1
  • Luis Payá
    • 1
  1. 1.Área de Ingeniería de Sistemas y AutomáticaUniversidad Miguel HernándezElche (Alicante)Spain

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