Multi-Cue Localization for Soccer Playing
Humanoid Robots
- Authors: Hauke Strasdat, Maren Bennewitz, and Sven Behnke
- In Proceedings of: 10th RoboCup International
Symposium, Bremen, 06/2006.
- Abstract:
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 eld 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.
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