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A novel plane extraction approach using supervised learning
Blekinge Institute of Technology, School of Computing.
Blekinge Institute of Technology, School of Computing.ORCID iD: 0000-0003-4327-117X
Blekinge Institute of Technology, School of Computing.
2013 (English)In: Machine Vision and Applications, ISSN 0932-8092, E-ISSN 1432-1769, Vol. 24, no 6, 1229-1237 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel approach for the classification of planar surfaces in an unorganized point clouds. A feature-based planner surface detection method is proposed which classifies a point cloud data into planar and non-planar points by learning a classification model from an example set of planes. The algorithm performs segmentation of the scene by applying a graph partitioning approach with improved representation of association among graph nodes. The planarity estimation of the points in a scene segment is then achieved by classifying input points as planar points which satisfy planarity constraint imposed by the learned model. The resultant planes have potential application in solving simultaneous localization and mapping problem for navigation of an unmanned-air vehicle. The proposed method is validated on real and synthetic scenes. The real data consist of five datasets recorded by capturing three-dimensional(3D) point clouds when a RGBD camera is moved in five different indoor scenes. A set of synthetic 3D scenes are constructed containing planar and non-planar structures. The synthetic data are contaminated with Gaussian and random structure noise. The results of the empirical evaluation on both the real and the simulated data suggest that the method provides a generalized solution for plane detection even in the presence of the noise and non-planar objects in the scene. Furthermore, a comparative study has been performed between multiple plane extraction methods.

Place, publisher, year, edition, pages
Springer , 2013. Vol. 24, no 6, 1229-1237 p.
Keyword [en]
Autonomous navigation, Planar surfaces, Point cloud, UAV navigation
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-6899DOI: 10.1007/s00138-013-0482-4ISI: 000321871600009Local ID: oai:bth.se:forskinfo00C017285ADB1573C1257B2F00380EDFOAI: oai:DiVA.org:bth-6899DiVA: diva2:834453
Available from: 2013-09-10 Created: 2013-03-15 Last updated: 2017-06-12Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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