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Vision-Based Robot Localization Using Sporadic Features

  • Stefan Enderle
  • Heiko Folkerts
  • Marcus Ritter
  • Stefan Sablatnög
  • Gerhard Kraetzschmar
  • Günther Palm
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1998)

Abstract

Knowing its position in an environment is an essential capability for any useful mobile robot. Monte-Carlo Localization (MCL) has become a popular framework for solving the self-localization problem in mobile robots. The known methods exploit sensor data obtained from laser range finders or sonar rings to estimate robot positions and are quite reliable and robust against noise. An open question is whether comparable localization performance can be achieved using only camera images, especially if the camera images are used both for localization and object recognition. In this paper, we discuss the problems arising from these characteristics and showex perimentally that MCL nevertheless works very well under these conditions.

Keywords

Mobile Robot Camera Image Goal Post Average Localization Error Mobile Robot Localization 
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 2001

Authors and Affiliations

  • Stefan Enderle
    • 1
  • Heiko Folkerts
    • 1
  • Marcus Ritter
    • 1
  • Stefan Sablatnög
    • 1
  • Gerhard Kraetzschmar
    • 1
  • Günther Palm
    • 1
  1. 1.Dept. of Neural Information ProcessingUniversity of UlmUlmGermany

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