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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments – a survey. IEEE Transactions on Evolutionary Computation 9, 303–317 (2005)
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)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE Int. Conf. on Neural Networks, Perth, Australia (1995)
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)
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)
Moreno, L., Garrido, S., Muñoz, M.L.: Evolutionary filter for robust global localization. Robotics and Autonomous Systems 54(7), 590–600 (2006)
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)
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)
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)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. Journal of Computer Vision 60(2), 91–110 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kronfeld, M., Weiss, C., Zell, A. (2008). A Dynamic Swarm for Visual Location Tracking. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2008. Lecture Notes in Computer Science, vol 5217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87527-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-87527-7_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87526-0
Online ISBN: 978-3-540-87527-7
eBook Packages: Computer ScienceComputer Science (R0)