A Dynamic Swarm for Visual Location Tracking

  • Marcel Kronfeld
  • Christian Weiss
  • Andreas Zell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5217)


The visual localization problem in robotics poses a dynamically changing environment due to the movement of the robot compared to a static image set serving as environmental map. We develop a particle swarm method adapted to this task and apply elements from dynamic optimization research. We show that our algorithm is able to outperform a Particle Filter, which is a standard localization approach in robotics, in a scenario of two visual outdoor datasets, being computationally more effective and delivering a better localization result.


Particle Swarm Optimization Mobile Robot Particle Filter Scale Invariant Feature Transform 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 2008

Authors and Affiliations

  • Marcel Kronfeld
    • 1
  • Christian Weiss
    • 1
  • Andreas Zell
    • 1
  1. 1.Computer Science DepartmentUniversity of TübingenTübingenGermany

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