Advertisement

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)

Abstract

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments – a survey. IEEE Transactions on Evolutionary Computation 9, 303–317 (2005)CrossRefGoogle Scholar
  2. 2.
    Weiss, C., Masselli, A., Tamimi, H., Zell, A.: Fast outdoor robot localization using integral invariants. In: Proc. of the 5th International Conference on Computer Vision Systems (ICVS), Bielefeld, Germany (2007)Google Scholar
  3. 3.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE Int. Conf. on Neural Networks, Perth, Australia (1995)Google Scholar
  4. 4.
    Kronfeld, M., Weiss, C., Zell, A.: Swarm-supported outdoor localization with sparse visual data. In: 3rd Europ. Conf. on Mobile Robots, pp. 259–264 (2007)Google Scholar
  5. 5.
    Vahdat, A.R., NourAshrafoddin, N., Ghidary, S.S.: Mobile robot global localization using differential evolution and particle swarm optimization. In: Srinivasan, D., Wang, L. (eds.) 2007 IEEE Congress on Evolutionary Computation, Singapore, IEEE Computational Intelligence Society, pp. 1527–1534. IEEE Press, Los Alamitos (2007)CrossRefGoogle Scholar
  6. 6.
    Moreno, L., Garrido, S., Muñoz, M.L.: Evolutionary filter for robust global localization. Robotics and Autonomous Systems 54(7), 590–600 (2006)CrossRefGoogle Scholar
  7. 7.
    Li, X., Branke, J., Blackwell, T.: Particle swarm with speciation and adaptation in a dynamic environment. In: GECCO 2006: Proc. of the 8th annual conf. on Genetic and evolutionary computation, pp. 51–58. ACM Press, New York (2006)CrossRefGoogle Scholar
  8. 8.
    Eberhart, R.C., Shi, Y.: Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 94–100 (2001)Google Scholar
  9. 9.
    Fox, D., Thrun, S., Burgard, W., Dellaert, F.: Particle Filters for Mobile Robot Localization. In: Sequential Monte Carlo Methods in Practice, pp. 401–428. Springer, Heidelberg (2000)Google Scholar
  10. 10.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar

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

Personalised recommendations