Name
Abstract | ||
|---|---|---|
This work presents methods for tracking a ball from noisy data taken by robots. In robotic soccer, getting a good estimation of a ball's position is a critical. In addition, accurate ball tracking enables robots to play well, for example, efficiently passing to other robots, fine saves by the goalie, good team play, and so on. In this paper, we present new bail tracking technique based on Monte-Carlo localization. The key idea of our technique is to consider two kinds of samples, one for position and the other for velocity. As a result of this technique, it enables us to estimate trajectory of the ball even when it goes out of view. |
Year | Venue | Keywords |
|---|---|---|
2006 | INTELLIGENT AUTONOMOUS SYSTEMS 9 | Monte-Carlo Localization, Object Tracking, Object Recognition, RoboCup |
Field | DocType | Citations |
Computer vision,Noisy data,Computer science,Simulation,Tracking system,Ball tracking,Artificial intelligence,Robot,Monte Carlo localization,Trajectory | Conference | 4 |
PageRank | References | Authors |
0.49 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Inoue | 1 | 4 | 0.49 |
| Akira Ishino | 2 | 54 | 7.31 |
| Ayumi Shinohara | 3 | 936 | 88.28 |