Classification and characterization of places for mapping of environment using hierarchical neural network and omnivision

Mobile robots need autonomy to fulfill their tasks. Such autonomy is related with their capacity to explorer and to recognize their navigation environments. In this context, the present work considers techniques for the classification and extraction of features from images, using artificial neural n...

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Bibliographic Details
Main Authors: Silva, L.L., Tronco, M.L., Vian, H.A., Souza, R., Porto, A.
Format: Conference Proceeding
Language:English
Subjects:
affine invariant pattern
Artificial neural networks
attributes vector
hierarchical artificial neural network (RNAH)
mapping system
Mobile robots
Navigation
Neurons
omnivision
Pattern recognition
Robot sensing systems
Robots
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Summary:Mobile robots need autonomy to fulfill their tasks. Such autonomy is related with their capacity to explorer and to recognize their navigation environments. In this context, the present work considers techniques for the classification and extraction of features from images, using artificial neural networks. This images are used in the mapping and localization system of LACE (automation and evolutive computing laboratory) mobile robot. In this direction, the robot uses a sensorial system composed by ultrasound sensors and a catadioptric vision system equipped with a camera and a conical mirror. The mapping system is composed of three modules; two of them will be presented in this paper: the classifier and the characterizer modules. Results of these modules simulations are presented in this paper.
ISSN:2157-8672
DOI:10.1109/IWSSIP.2008.4604472