19 January 2009 Geometric alignment for large point cloud pairs using sparse overlap areas
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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.
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Keisuke Fujimoto, Nobutaka Kimura, Fumiko Beniyama, Toshio Moriya, Yasuichi Nakayama, "Geometric alignment for large point cloud pairs using sparse overlap areas", Proc. SPIE 7252, Intelligent Robots and Computer Vision XXVI: Algorithms and Techniques, 72520A (19 January 2009); doi: 10.1117/12.805765; https://doi.org/10.1117/12.805765
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