The paper presents a simulated mobile system that learns to solve the egolocation task in a known environment, in a supervised way, using a very low resolution sampling of the optical array and RBF approximation techniques. The impact of the number of sensors, of their layout, in particular of Sobol sequences with respect to regular grids for a progressively refined sampling of images, and of the complexity of response of each sensing unit has been investigated in an attempt to simplify as much as possible the architecture of the image processing module retaining good localization ability.

Low Resolution Image Sampling for Pattern Matching

Brunelli, Roberto
2004

Abstract

The paper presents a simulated mobile system that learns to solve the egolocation task in a known environment, in a supervised way, using a very low resolution sampling of the optical array and RBF approximation techniques. The impact of the number of sensors, of their layout, in particular of Sobol sequences with respect to regular grids for a progressively refined sampling of images, and of the complexity of response of each sensing unit has been investigated in an attempt to simplify as much as possible the architecture of the image processing module retaining good localization ability.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/47381
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