Collective classification for labeling of places and objects in 2D and 3D range data

Triebel, Rudolph, Martinez Mozos, Oscar and Burgard, Wolfram (2008) Collective classification for labeling of places and objects in 2D and 3D range data. In: Data analysis, machine learning and applications. Studies in Classification, Data Analysis, and Knowledge Organization . Springer, Germany, pp. 293-300. ISBN 9783540782391, 9783540782469

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In this paper, we present an algorithm to identify types of places and objects from 2D and 3D laser range data obtained in indoor environments. Our approach is a combination of a collective classification method based on associative Markov networks together with an instance-based feature extraction using nearest neighbor. Additionally, we show how to select the best features needed to represent the objects and places, reducing the time needed for the learning and inference steps while maintaining high classification rates. Experimental results in real data demonstrate the effectiveness of our approach in indoor environments.

Additional Information:Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7–9, 2007
Keywords:Artificial Intelligence, Robotics, Statistics, Data Mining, Knowledge Discovery
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
ID Code:9565
Deposited On:28 May 2013 09:04

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