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Proceedings Paper

Automatic segmentation and model identification in unordered 3D point cloud
Author(s): Sung Joon Ahn; I. Effenberger; W. Rauh; Hyungsuck Cho; E. Westkämper
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Paper Abstract

Segmentation and object recognition in point cloud are of topical interest for computer and machine vision. In this paper, we present a very robust and computationally efficient interactive procedure between segmentation, outlier detection, and model fitting in 3D-point cloud. For an accurate and reliable estimation of the model parameters, we apply the orthogonal distance fitting algorithms for implicit curves and surfaces, which minimize the square sum of the geometric (Euclidean) error distances. The model parameters are grouped and simultaneously estimated in terms of form, position, and rotation parameters, hence, providing a very advantageous algorithmic feature for applications, e.g., robot vision, motion analysis, and coordinate metrology. To achieve a high automation degree of the overall procedures of the segmentation and object recognition in point cloud, we utilize the properties of implicit features. We give an application example of the proposed procedure to a point cloud containing multiple objects taken by a laser radar.

Paper Details

Date Published: 18 October 2002
PDF: 11 pages
Proc. SPIE 4902, Optomechatronic Systems III, (18 October 2002); doi: 10.1117/12.467726
Show Author Affiliations
Sung Joon Ahn, Fraunhofer-Institut für Produktionstechnik und Automatisierung (Germany)
I. Effenberger, Fraunhofer-Institut für Produktionstechnik und Automatisierung (Germany)
W. Rauh, Fraunhofer-Institut für Produktionstechnik und Automatisierung (Germany)
Hyungsuck Cho, Korea Advanced Institute of Science and Technology (South Korea)
E. Westkämper, Fraunhofer-Institut für Produktionstechnik und Automatisierung (Germany)


Published in SPIE Proceedings Vol. 4902:
Optomechatronic Systems III
Toru Yoshizawa, Editor(s)

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