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Journal of Sensors
Volume 2016, Article ID 1537891, 12 pages
http://dx.doi.org/10.1155/2016/1537891
Research Article

A Study of Visual Descriptors for Outdoor Navigation Using Google Street View Images

Department of Systems Engineering and Automation, Miguel Hernandez University, Avda. de la Universidad s/n, Elche, 03202 Alicante, Spain

Received 22 March 2016; Revised 24 August 2016; Accepted 2 November 2016

Academic Editor: Jose R. Martinez-de Dios

Copyright © 2016 L. Fernández 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

A comparative analysis between several methods to describe outdoor panoramic images is presented. The main objective consists in studying the performance of these methods in the localization process of a mobile robot (vehicle) in an outdoor environment, when a visual map that contains images acquired from different positions of the environment is available. With this aim, we make use of the database provided by Google Street View, which contains spherical panoramic images captured in urban environments and their GPS position. The main benefit of using these images resides in the fact that it permits testing any novel localization algorithm in countless outdoor environments anywhere in the world and under realistic capture conditions. The main contribution of this work consists in performing a comparative evaluation of different methods to describe images to solve the localization problem in an outdoor dense map using only visual information. We have tested our algorithms using several sets of panoramic images captured in different outdoor environments. The results obtained in the work can be useful to select an appropriate description method for visual navigation tasks in outdoor environments using the Google Street View database and taking into consideration both the accuracy in localization and the computational efficiency of the algorithm.