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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 importance. 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 planar laser
scans as well as a clustering procedure that builds a set of object
classes from given observation sequences. Extensive experiments in
real environments demonstrate that our system is able to
autonomously learn useful models for, e.g., pedestrians, skaters, or
cyclists without being provided with external class information.
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