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Learning moving objects in a multi-target tracking scenario for mobile robots that use laser range measurements

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3 Author(s)
Kondaxakis, P. ; Inst. of Comput. Sci., Found. for Res. & Technol., Hellas, Greece ; Baltzakis, H. ; Trahanias, P.

This paper addresses the problem of real-time moving-object detection, classification and tracking in populated and dynamic environments. In this scenario, a mobile robot uses 2D laser range data to recognize, track and avoid moving targets. Most previous approaches either rely on pre-defined data features or off-line training of a classifier for specific data sets, thus eliminating the possibility to detect and track different-shaped moving objects. We propose a novel and adaptive technique where potential moving objects are classified and learned in real-time using a fuzzy ART neural network algorithm. Experimental results indicate that our method can effectively distinguish and track moving targets in cluttered indoor environments, while at the same time learning their shape.

Published in:

Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on

Date of Conference:

10-15 Oct. 2009