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Evaluation of 3D feature descriptors for classification of surface geometries in point clouds

 
: Arbeiter, Georg; Fuchs, Steffen; Bormann, Richard; Fischer, Jan; Verl, Alexander

:
Preprint urn:nbn:de:0011-n-2252907 (1.9 MByte PDF)
MD5 Fingerprint: cc20f09a466e7ed9a8aee1ae0f5b2e63
© 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Erstellt am: 18.1.2013


Almeida, Anibal T. de (General Chair); Nunes, Urbano (General Chair) ; Institute of Electrical and Electronics Engineers -IEEE-; Robotics Society of Japan; IEEE Industrial Electronics Society:
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012. Conference Proceedings. Vol.3 : Celebrating 25 Years of IROS; Vilamoura, Portugal, 7th-12th of October 2012
Piscataway, NJ: IEEE, 2012
ISBN: 978-1-4673-1737-5 (Print)
ISBN: 978-1-4673-1735-1
ISBN: 978-1-4673-1736-8
S.1644-1650
International Conference on Intelligent Robots and Systems (IROS) <2012, Vilamoura>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPA ()
Punktwolke; 3D; 3D-Bildverarbeitung; point cloud; Oberflächengeometrie; Mustererkennung; Klassifikation

Abstract
This paper investigates existing methods for 3D point feature description with a special emphasis on their expressiveness of the local surface geometry. We choose three promising descriptors, namely Radius-Based Surface Descriptor (RSD), Principal Curvatures (PC) and Fast Point Feature Histograms (FPFH), and present an approach for each of them to show how they can be used to classify primitive local surfaces such as cylinders, edges or corners in point clouds. Furthermore these descriptor-classifier combinations have to hold an in-depth evaluation to show their discriminative power and robustness in real world scenarios. Our analysis incorporates detailed accuracy measurements on sparse and noisy point clouds representing typical indoor setups for mobile robot tasks and considers the resource consumption to assure real-time processing.

: http://publica.fraunhofer.de/dokumente/N-225290.html