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.
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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
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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
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