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Robotics and Autonomous Systems | ||
Volume 22, Issues 3-4 | ![]() |
Abstract |
December 1997 | Journal Format-PDF (1123 K) | |
Pages 329-344 |
PII: S0921-8890(97)00046-8
Expectation-based selective attention for visual monitoring and control of a
robot vehicle
Shumeet Balujaa, * and Dean A. Pomerleaua
a School of Computer Science and The Robotics
Institute, Carnegie Mellon University, 5000 Forbes Avenue Pittsburgh, PA 15213
USA
Available online 18 June 1998.
Reliable vision-based control of an autonomous vehicle requires the ability to focus attention on the important features in an input scene. Previous work with an autonomous lane following system, ALVINN (Pomerleau, 1993), has yielded good results in uncluttered conditions. This paper presents an artificial neural network based learning approach for handling difficult scenes which will confuse the ALVINN system. This work presents a mechanism for achieving task-specific focus of attention by exploiting temporal coherence. A saliency map, which is based upon a computed expectation of the contents of the inputs in the next time step, indicates which regions of the input retina are important for performing the task. The saliency map can be used to accentuate the features which are important for the task, and de-emphasize those which are not.
Author Keywords: Expectation-based selective attention; Autonomous navigation; Temporal coherence; Saliency map; Artificial neural networks
Index Terms: Mobile robots; Motion control; Computer vision;
Monitoring; Navigation; Neural networks; Robot learning; Expectation based
selective attention; Temporal coherence; Saliency map
*Corresponding author.
Robotics and Autonomous Systems | ![]() |
Abstract Journal Format-PDF (1123 K) |
Volume 22, Issues 3-4 | ||
December 1997 | ||
Pages 329-344 |
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