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3D point cloud registration based on a purpose-designed similarity measure

Abstract: This article introduces a novel approach for finding a rigid transformation that coarsely aligns two 3D point clouds. The algorithm performs an iterative comparison between 2D descriptors by using a purpose-designed similarity measure in order to find correspondences between two 3D point clouds sensed from different positions of a free-form object. The descriptors (named with the acronym CIRCON) represent an ordered set of radial contours that are extracted around an interest-point within the point cloud. The search for correspondences is done iteratively, following a cell distribution that allows the algorithm to converge toward a candidate point. Using a single correspondence an initial estimation of the Euclidean transformation is computed and later refined by means of a multiresolution approach. This coarse alignment algorithm can be used for 3D modeling and object manipulation tasks such as "Bin Picking" when free-form objects are partially occluded or present symmetries.

 Autoría: Torre-Ferrero C., Llata J., Alonso L., Robla S., Sarabia E.,

 Fuente: Eurasip Journal on Advances in Signal Processing, 2012, 57

 Editorial: SpringerOpen

 Fecha de publicación: 06/03/2012

 Nº de páginas: 15

 Tipo de publicación: Artículo de Revista

 DOI: 10.1186/1687-6180-2012-57

 ISSN: 1687-6172,1687-6180

 Proyecto español: DPI2006-15313