An automatic registration algorithm for the scattered point clouds based on the curvature feature

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

45 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)53-60
Journal / PublicationOptics and Laser Technology
Volume46
Issue number1
Publication statusPublished - Mar 2013

Abstract

Object modeling by the registration of multiple range images has important applications in reverse engineering and computer vision. In order to register multi-view scattered point clouds, a novel curvature-based automatic registration algorithm is proposed in this paper, which can solve the registration problem with partial overlapping point clouds. For two sets of scattered point clouds, the curvature of each point is estimated by using the quadratic surface fitting method. The feature points that have the maximum local curvature variations are then extracted. The initial matching points are acquired by computing the Hausdorff distance of curvature, and then the circumference shape feature of the local surface is used to obtain the accurate matching points from the initial matching points. Finally, the rotation and translation matrix are estimated by the quaternion, and an iterative algorithm is used to improve the registration accuracy. Experimental results show that the algorithm is effective.© 2012 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Circumference feature, Curvature, Registration