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.
Paper: [pdf: 912k]
Bibtex
@InProceedings{triebel2007ijcai,
TITLE = {Instace-based AMN Classification for Improved Object Recognition in 2D and 3D Laser Range Data},
AUTHOR = {Rudolph Triebel and Richard Schmidt and Oscar Martinez Mozos and Wolfram Burgard},
BOOKTITLE = {Proceedings of the International Joint Conference on Artificial Intelligence},
PAGES = {2225--2230},
ADDRESS = {Hyderabad, India},
YEAR = {2007},
URL = {http://www.informatik.uni-freiburg.de/~omartine/publications/triebel2007ijcai.pdf}
}