Authors:
Polycarpo Souza Neto
;
José Marques Soares
;
Michela Mulas
and
George André Pereira Thé
Affiliation:
Department of Teleinformatics Engineering, Federal University of Ceara, Fortaleza CEP 60455-970, Brazil
Keyword(s):
Point Cloud Registration, Iterative Closest Point, Generalized ICP, Eigentropy, Omnivariance.
Abstract:
In 3D reconstruction applications, matching between corresponding point clouds is commonly resolved using variants of the Iterative Closest Point (ICP). However, ICP and its variants suffer from some limitations, functioning properly only for some contexts with well-behaved data distribution; outdoor scene, for example, poses many challenges. Indeed, the literature has suggested that the ability of some of these algorithms to find a match was reduced by the presence of geometric disorder in the scene, for example. This article presents a method based on the characterization of the eigentropy and omnivariance properties of clouds to indicate which variant of the ICP is best suited for each context considered here, namely, object or outdoor scene alignment. In addition to the context selector, we suggest a partitioning step prior to alignment, which in most cases allows for reduced computational cost. In summary, the proposal as a whole worked satisfactorily to the alignment as a multi
purpose registration technique, serving to pose correction of data from different contexts and thus being useful for computer vision and robotics applications.
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