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Journal of Sensors - Volume 2016 2016, Article ID 1209507, 21 pages -

Research ArticleDepartment of Systems Engineering and Automation, Miguel Hernández University, Avenida de la Universidad s-n, Elche, 03202 Alicante, Spain

Received 23 October 2015; Accepted 11 February 2016

Academic Editor: Yassine Ruichek

Copyright © 2016 L. Payá et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Nowadays, the design of fully autonomous mobile robots is a key discipline. Building a robust model of the unknown environment is an important ability the robot must develop. Using this model, this robot must be able to estimate its current position and to navigate to the target points. The use of omnidirectional vision sensors is usual to solve these tasks. When using this source of information, the robot must extract relevant information from the scenes both to build the model and to estimate its position. The possible frameworks include the classical approach of extracting and describing local features or working with the global appearance of the scenes, which has emerged as a conceptually simple and robust solution. While feature-based techniques have been extensively studied in the literature, appearance-based ones require a full comparative evaluation to reveal the performance of the existing methods and to tune correctly their parameters. This work carries out a comparative evaluation of four global-appearance techniques in map building tasks, using omnidirectional visual information as the only source of data from the environment.





Autor: L. Payá, O. Reinoso, Y. Berenguer, and D. Úbeda

Fuente: https://www.hindawi.com/



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