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Proceedings Paper

Geometric alignment for large point cloud pairs using sparse overlap areas
Author(s): Keisuke Fujimoto; Nobutaka Kimura; Fumiko Beniyama; Toshio Moriya; Yasuichi Nakayama
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Paper Abstract

We present a novel approach for geometric alignment of 3D sensor data. The Iterative Closest Point (ICP) algorithm is widely used for geometric alignment of 3D models as a point-to-point matching method when an initial estimate of the relative pose is known. However, the accuracy of the correspondence between point and point is difficult when the points are sparsely distributed. In addition, the searching cost is high because the ICP algorithm requires a search of the nearest-neighbor points at every minimization. In this paper, we describe a plane-to-plane registration method. We define the distance between two planes and estimate the translation parameter by minimizing the distance between the planes. The plane-to-plane method is able to register the set of scatter points which are sparsely distributed and the density is low with low cost. We tested this method with the large scatter points of a manufacturing plant and show the effectiveness of our proposed method.

Paper Details

Date Published: 19 January 2009
PDF: 9 pages
Proc. SPIE 7252, Intelligent Robots and Computer Vision XXVI: Algorithms and Techniques, 72520A (19 January 2009); doi: 10.1117/12.805765
Show Author Affiliations
Keisuke Fujimoto, The Univ. of Electro-Communications (Japan)
Nobutaka Kimura, Hitachi, Ltd. (Japan)
Fumiko Beniyama, Hitachi, Ltd. (Japan)
Toshio Moriya, Hitachi, Ltd. (Japan)
Yasuichi Nakayama, The Univ. of Electro-Communications (Japan)

Published in SPIE Proceedings Vol. 7252:
Intelligent Robots and Computer Vision XXVI: Algorithms and Techniques
David P. Casasent; Ernest L. Hall; Juha Röning, Editor(s)

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