A Two-Tiered Approach to Self-Localization
RoboCup 2001: Robot Soccer World Cup V
A Localization Method for a Soccer Robot Using a Vision-Based Omni-Directional Sensor
RoboCup 2000: Robot Soccer World Cup IV
Vision-Based Localization in RoboCup Environments
RoboCup 2000: Robot Soccer World Cup IV
Localization with non-unique landmark observations
RoboCup 2010
Robust visual localization of a humanoid robot in a symmetric space
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Gradient vector griding: an approach to shape-based object detection in RoboCup scenarios
Robot Soccer World Cup XV
Hi-index | 0.00 |
An essential capability of a soccer playing robot is to robustly and accurately estimate its pose on the field. Tracking the pose of a humanoid robot is, however, a complex problem. The main difficulties are that the robot has only a constrained field of view, which is additionally often affected by occlusions, that the roll angle of the camera changes continously and can only be roughly estimated, and that dead reckoning provides only noisy estimates. In this paper, we present a technique that uses field lines, the center circle, corner poles, and goals extracted out of the images of a low-cost wide-angle camera as well as motion commands and a compass to localize a humanoid robot on the soccer field. We present a new approach to robustly extract lines using detectors for oriented line pints and the Hough transform. Since we first estimate the orientation, the individual line points are localized well in the Hough domain. In addition, while matching observed lines and model lines, we do not only consider their Hough parameters. Our similarity measure also takes into account the positions and lengths of the lines. In this way, we obtain a much more reliable estimate how well two lines fit. We apply Monte-Carlo localization to estimate the pose of the robot. The observation model used to evaluate the individual particles considers the differences of expected and measured distances and angles of the other landmarks. As we demonstrate in real-world experiments, our technique is able to robustly and accurately track the position of a humanoid robot on a soccer field. We also present experiments to evaluate the utility of using the different cues for pose estimation.