D. Schulz and D. Fox
Bayesian Color Estimation for Adaptive Vision-based Robot
Proceedings of IROS, 2004.
In this article we introduce a hierarchical Bayesian model to
estimate a set of colors with a mobile robot. Estimating colors is
particularly important if objects in an environment can only be
distinguished by their color. Since the appearance of colors can
change due to variations in the lighting condition, a robot needs to
adapt its color model to such changes. We propose a two level
Gaussian model in which the lighting conditions are estimated at the
upper level using a switching Kalman filter. A hierarchical
Bayesian technique learns Gaussian priors from data collected in
other environments. Furthermore, since estimation of the color
model depends on knowledge of the robot's location, we employ a
Rao-Blackwellised particle filter to maintain a joint posterior over
robot positions and lighting conditions. We evaluate the technique
in the context of the RoboCup AIBO league, where a legged AIBO robot
has to localize itself in an environment similar to a soccer field.
Our experiments show that the robot can localize under different
lighting conditions and adapt to changes in the lighting condition,
for example, due to a light being turned on or off.
Full paper [pdf]
(484 kb, 6 pages)
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