TY - GEN
T1 - Collective classification for labeling of places and objects in 2D and 3D range data
AU - Triebel, Rudolph
AU - Mozos, Óscar Martínez
AU - Burgard, Wolfram
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84879588541&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-78246-9_35
DO - 10.1007/978-3-540-78246-9_35
M3 - Conference contribution
AN - SCOPUS:84879588541
SN - 9783540782391
T3 - Studies in Classification, Data Analysis, and Knowledge Organization
SP - 293
EP - 300
BT - Data Analysis, Machine Learning and Applications - Proceedings of the 31st Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKI 2007
PB - Kluwer Academic Publishers
T2 - 31st Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Data Analysis, Machine Learning, and Applications, GfKl 2007
Y2 - 7 March 2007 through 9 March 2007
ER -