Abstract
The aim of this paper is to present a novel approach to the autonomous robot navigation based on clustering of image descriptors. The descriptor called Speeded-Up Robust Features (SURF) is a scale- and rotation-invariant detector, which can visually navigate a robot in a large outdoor and indoor environment. By incorporating several clustering methods, which are derived from fuzzy set theory and inspired with biological background, such as Fuzzy C-mean (FCM), Kohonen’s Self-Organizing Map (SOM) and Neural Gas algorithm (NG), we detect center positions of natural clusters crosswise the recorded images. Center positions represented by vector prototypes are used as reference points in the decision making of the robot navigation.
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Vintr, T., Pastorek, L., Rezankova, H. (2011). Autonomous Robot Navigation Based on Clustering across Images. In: Obdržálek, D., Gottscheber, A. (eds) Research and Education in Robotics - EUROBOT 2011. EUROBOT 2011. Communications in Computer and Information Science, vol 161. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21975-7_27
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DOI: https://doi.org/10.1007/978-3-642-21975-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21974-0
Online ISBN: 978-3-642-21975-7
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