Towards Eliminating Manual Color Calibration at RoboCup
Mohan Sridharan and Peter Stone.
Towards Eliminating Manual Color Calibration at RoboCup. In Under Review. , February 2005.
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Abstract
Color calibration is a time-consuming, and therefore costly requirement for most robot teams at RoboCup. This paper presents an approach for autonomous color learning on-board a mobile robot with limited computational and memory resources. It works without any labeled training data and trains autonomously from a color-coded map of its environment. The process is fully implemented, completely autonomous, and provides high degree of segmentation accuracy. Most importantly, it dramatically reduces the time needed to train a color map in a new environment.
BibTeX Entry
@inproceedings(RoboSymp2005-vision,
author="Mohan Sridharan and Peter Stone",
title="Towards Eliminating Manual Color Calibration at RoboCup",
booktitle="Under Review. ",
month="February",year="2005",
abstract={
Color calibration is a time-consuming, and therefore
costly requirement for most robot teams at RoboCup.
This paper presents an approach for autonomous color
learning on-board a mobile robot with limited
computational and memory resources. It works without
any labeled training data and trains autonomously
from a color-coded map of its environment. The
process is fully implemented, completely autonomous,
and provides high degree of segmentation accuracy.
Most importantly, it dramatically reduces the time
needed to train a color map in a new environment.
},
)
