Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87527-7_18 | ANTS Conference |
Keywords | Field | DocType |
dynamic swarm,visual outdoor datasets,visual location tracking,dynamic optimization research,standard localization approach,environmental map,particle filter,localization result,static image,visual localization problem,particle swarm method,particle swarm | Particle swarm optimization,Computer vision,Swarm behaviour,Computer science,Particle filter,Multi-swarm optimization,Artificial intelligence,Robot,Monte Carlo localization,Mobile robot,Robotics | Conference |
Volume | ISSN | Citations |
5217 | 0302-9743 | 2 |
PageRank | References | Authors |
0.39 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcel Kronfeld | 1 | 74 | 6.67 |
| Christian Weiss | 2 | 86 | 6.62 |
| Andreas Zell | 3 | 1419 | 137.58 |