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Mohan Sridharan and Peter Stone. Color Learning and Illumination Invariance on Mobile Robots: A Survey. Robotics and Autonomous Systems (RAS) Journal, 57(60-7):629–44, June 2009.
Recent developments in sensor technology have made it feasible to use mobile robots in several fields, but robots still lack the ability to accurately sense the environment. A major challenge to the widespread deployment of mobile robots is the ability to function autonomously, learning useful models of environmental features, recognizing environmental changes, and adapting the learned models in response to such changes. This article focuses on such learning and adaptation in the context of color segmentation on mobile robots in the presence of illumination changes. The main contribution of this article is a survey of vision algorithms that are potentially applicable to color-based mobile robot vision. We therefore look at algorithms for color segmentation, color learning and illumination invariance on mobile robot platforms, including approaches that tackle just the underlying vision problems. Furthermore, we investigate how the interdependencies between these modules and high-level action planning can be exploited to achieve autonomous learning and adaptation. The goal is to determine the suitability of the state-of-the-art vision algorithms for mobile robot domains, and to identify the challenges that still need to be addressed to enable mobile robots to learn and adapt models for color, so as to operate autonomously in natural conditions.
@Article{RAS09-sridharan,
author = "Mohan Sridharan and Peter Stone",
title = "Color Learning and Illumination Invariance on Mobile Robots: A Survey",
journal = "Robotics and Autonomous Systems (RAS) Journal",
volume="57",number="60-7",pages="629--44",
month="June",
year = "2009",
abstract = {
Recent developments in sensor technology have made it feasible to use
mobile robots in several fields, but robots still lack the ability to
accurately sense the environment. A major challenge to the widespread
deployment of mobile robots is the ability to function autonomously,
learning useful models of environmental features, recognizing
environmental changes, and adapting the learned models in response to
such changes. This article focuses on such learning and adaptation in
the context of color segmentation on mobile robots in the presence of
illumination changes. The main contribution of this article is a
survey of vision algorithms that are potentially applicable to
color-based mobile robot vision. We therefore look at algorithms for
color segmentation, color learning and illumination invariance on
mobile robot platforms, including approaches that tackle just the
underlying vision problems. Furthermore, we investigate how the
interdependencies between these modules and high-level action
planning can be exploited to achieve autonomous learning and
adaptation. The goal is to determine the suitability of the
state-of-the-art vision algorithms for mobile robot domains, and to
identify the challenges that still need to be addressed to enable
mobile robots to learn and adapt models for color, so as to operate
autonomously in natural conditions.
},
}
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