Classifying Dynamic Objects: An Unsupervised Learning Approach

Matthias Luber, Kai O. Arras, Christian Plagemann, Wolfram Burgard
2008 Robotics: Science and Systems IV  
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
more » ... e-varying appearance of objects in planar laser scans as well as a clustering procedure that builds a set of object classes from given training 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.
doi:10.15607/rss.2008.iv.035 dblp:conf/rss/LuberAPB08 fatcat:spnak2lvbvcdpnuw2paklwmccy