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Action Selection for Illumination Invariant Color Learning.
Mohan Sridharan
and Peter Stone.
In The IEEE International Conference on Intelligent
Robots and Systems (IROS), 2007.
[PDF]176.5kB [postscript]490.5kB
A major challenge in the path of widespread use of mobile robots is the ability to function autonomously, learning useful features from the environment and using them to adapt to environmental changes. We propose an algorithm for mobile robots equipped with color cameras that allows for smooth operation under illumination changes. The robot uses image statistics and the environmental structure to autonomously detect and adapt to both major and minor illumination changes. Furthermore, the robot autonomously plans an action sequence that maximizes color learning opportunities while minimizing localization errors. Our approach is fully implemented and tested on the Sony AIBO robots.
@InProceedings{IROS07-mohan,
author = "Mohan Sridharan and Peter Stone",
title = "Action Selection for Illumination Invariant Color Learning",
booktitle = "The IEEE International Conference on Intelligent Robots and Systems (IROS)",
year = "2007",
bibauthor = "smohan",
abstract = {A major challenge in the path of widespread use of
mobile robots is the ability to function autonomously,
learning useful features from the environment and using
them to adapt to environmental changes. We propose an
algorithm for mobile robots equipped with color cameras
that allows for smooth operation under illumination
changes. The robot uses image statistics and the
environmental structure to autonomously detect and adapt
to both major and minor illumination changes. Furthermore,
the robot autonomously plans an action sequence that
maximizes color learning opportunities while minimizing
localization errors. Our approach is fully implemented and
tested on the Sony AIBO robots.},
}
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