Abstract

In this paper, we present an algorithm to identify different types of objects from 2D and 3D
laser range data. Our method is a combination of an instance-based feature extraction similar to the Nearest-Neighbor classifier (NN) and a collective classification method that utilizes associative Markov networks (AMNs). Compared to previous approaches, we transform the feature vectors so that they are better separable by linear hyperplanes, which are learned by the AMN classifier. We present results of extensive experiments in which we evaluate the performance of our algorithm on several recorded indoor scenes and compare it to the standard AMN approach as well as the NN classifier. The classification rate obtained with our algorithm substantially exceeds those of the AMN and the NN.