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

Rudolph Triebel; Óscar Martínez Mozos; Wolfram Burgard

doi:10.1007/978-3-540-78246-9_35

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

Rudolph Triebel, Óscar Martínez Mozos, Wolfram Burgard

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Data Analysis, Machine Learning and Applications - Proceedings of the 31st Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKI 2007
Publisher	Kluwer Academic Publishers
Pages	293-300
Number of pages	8
ISBN (Print)	9783540782391
DOIs	https://doi.org/10.1007/978-3-540-78246-9_35
State	Published - 2008
Externally published	Yes
Event	31st Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Data Analysis, Machine Learning, and Applications, GfKl 2007 - Freiburg, Germany Duration: 7 Mar 2007 → 9 Mar 2007

Publication series

Name	Studies in Classification, Data Analysis, and Knowledge Organization
ISSN (Print)	1431-8814

Conference

Conference	31st Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Data Analysis, Machine Learning, and Applications, GfKl 2007
Country/Territory	Germany
City	Freiburg
Period	7/03/07 → 9/03/07

Access to Document

10.1007/978-3-540-78246-9_35

Cite this

Triebel, R., Mozos, Ó. M., & Burgard, W. (2008). Collective classification for labeling of places and objects in 2D and 3D range data. In Data Analysis, Machine Learning and Applications - Proceedings of the 31st Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKI 2007 (pp. 293-300). (Studies in Classification, Data Analysis, and Knowledge Organization). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-540-78246-9_35

Triebel, Rudolph ; Mozos, Óscar Martínez ; Burgard, Wolfram. / Collective classification for labeling of places and objects in 2D and 3D range data. Data Analysis, Machine Learning and Applications - Proceedings of the 31st Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKI 2007. Kluwer Academic Publishers, 2008. pp. 293-300 (Studies in Classification, Data Analysis, and Knowledge Organization).

@inproceedings{010e7a64785e4dbf9f310d334f615019,

title = "Collective classification for labeling of places and objects in 2D and 3D range data",

abstract = "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.",

author = "Rudolph Triebel and Mozos, {{\'O}scar Mart{\'i}nez} and Wolfram Burgard",

year = "2008",

doi = "10.1007/978-3-540-78246-9_35",

language = "English",

isbn = "9783540782391",

series = "Studies in Classification, Data Analysis, and Knowledge Organization",

publisher = "Kluwer Academic Publishers",

pages = "293--300",

booktitle = "Data Analysis, Machine Learning and Applications - Proceedings of the 31st Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKI 2007",

note = "31st Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Data Analysis, Machine Learning, and Applications, GfKl 2007 ; Conference date: 07-03-2007 Through 09-03-2007",

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Triebel, R, Mozos, ÓM & Burgard, W 2008, Collective classification for labeling of places and objects in 2D and 3D range data. in Data Analysis, Machine Learning and Applications - Proceedings of the 31st Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKI 2007. Studies in Classification, Data Analysis, and Knowledge Organization, Kluwer Academic Publishers, pp. 293-300, 31st Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Data Analysis, Machine Learning, and Applications, GfKl 2007, Freiburg, Germany, 7/03/07. https://doi.org/10.1007/978-3-540-78246-9_35

Collective classification for labeling of places and objects in 2D and 3D range data. / Triebel, Rudolph; Mozos, Óscar Martínez; Burgard, Wolfram.
Data Analysis, Machine Learning and Applications - Proceedings of the 31st Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKI 2007. Kluwer Academic Publishers, 2008. p. 293-300 (Studies in Classification, Data Analysis, and Knowledge Organization).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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.

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Triebel R, Mozos ÓM, Burgard W. Collective classification for labeling of places and objects in 2D and 3D range data. In Data Analysis, Machine Learning and Applications - Proceedings of the 31st Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKI 2007. Kluwer Academic Publishers. 2008. p. 293-300. (Studies in Classification, Data Analysis, and Knowledge Organization). doi: 10.1007/978-3-540-78246-9_35

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

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