Name
Abstract | ||
|---|---|---|
The rules in RoboCup soccer more and more discourage a solely colorbased orientation on the soccer field.While the field size
increases, field boundary markers and goals become smaller and less colorful. For robust game play, robots therefore need
to maintain a state and rely on more subtle environmental clues. Field lines are particularly interesting, because they are
hardly completely occluded and observing them significantly reduces the number of possible poses on the field.
In this work we present a method for line-based localization. Unlike previous work, our method first recovers a line structure
graph from the image. From the graph we can then easily derive features such as lines and corners. Finally, we describe optimizations
for efficient use of the derived features in a particle filter. The method described in this paper is used regularly on our
humanoid soccer robots.
|
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-20217-9_34 | Advanced Robotics |
Keywords | Field | DocType |
line structure graph,colorbased orientation,soccer field,field boundary marker,humanoid soccer robot,robocup soccer,efficient soccer robot localization,field line,derive feature,field size increase,previous work,particle filter | Graph,Computer vision,Self localization,Field line,Artificial intelligence,Probabilistic logic,Engineering,Robot,Soccer robot,Perception | Journal |
Volume | Issue | Citations |
26 | 14 | 4 |
PageRank | References | Authors |
0.52 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hannes Schulz | 1 | 55 | 10.82 |
| Weichao Liu | 2 | 4 | 0.52 |
| Jörg Stückler | 3 | 624 | 46.80 |
| Sven Behnke | 4 | 1672 | 181.84 |