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GFKL
2007
Springer

Collective Classification for Labeling of Places and Objects in 2D and 3D Range Data

8 years 9 months ago
Collective Classification for Labeling of Places and Objects in 2D and 3D Range Data
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.
Rudolph Triebel, Óscar Martínez Mozo
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2007
Where GFKL
Authors Rudolph Triebel, Óscar Martínez Mozos, Wolfram Burgard
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