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Auton Robot (2009) 26: 141151 DOI 10.1007/s10514-009-9112-4

Summary: Auton Robot (2009) 26: 141­151
DOI 10.1007/s10514-009-9112-4
Classifying dynamic objects
An unsupervised learning approach
Matthias Luber · Kai O. Arras · Christian Plagemann ·
Wolfram Burgard
Received: 7 November 2008 / Accepted: 6 March 2009 / Published online: 27 March 2009
© Springer Science+Business Media, LLC 2009
Abstract For robots operating in real-world environments,
the ability to deal with dynamic entities such as humans,
animals, vehicles, or other robots is of fundamental impor-
tance. The variability of dynamic objects, however, is large
in general, which makes it hard to manually design suitable
models for their appearance and dynamics. In this paper, we
present an unsupervised learning approach to this model-
building problem. We describe an exemplar-based model for
representing the time-varying appearance of objects in pla-
nar laser scans as well as a clustering procedure that builds a
set of object classes from given observation sequences. Ex-
tensive experiments in real environments demonstrate that


Source: Arras, Kai O. - Institut für Informatik, Albert-Ludwigs-Universität Freiburg


Collections: Computer Technologies and Information Sciences