3D point cloud registration based on a purpose-designed similarity measure
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RESEARCH
Open Access
3D point cloud registration based on a purpose-designed similarity measure Carlos Torre-Ferrero*, José R Llata, Luciano Alonso, Sandra Robla and Esther G Sarabia
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 freeform 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. Keywords: laser scanner, 3D point cloud, descriptor, similarity measure, coarse alignment, 3D registration
1. Introduction The alignment of two point clouds is quite a frequent task, both in 3D modeling and in object recognition. Similarly, the need for automating certain applications, such as computer-aided manufacturing or bin-picking, has necessitated the use of 3D information about the parts being manipulated. This information can be sensed by 3D acquisition methods [1], such as laser scanners or time-of-flight cameras, which provide a range image for every different pose of the object. Finding the rigid transformation producing a suitable alignment of the resulting point clouds, without having a previous estimate, is a problem that has been approached using different strategies [2,3]. Although no solution has prevailed as the most accepted, algorithms based on intrinsic properties have been more widely applied due to their generality. These algorithms extract shape descriptors [4-15], curves [16,17], structures [18,19], or graphs [20] from both point clouds (sometimes meshes are used instead) in order to compare them. If several correspondences are found, then a
* Correspondence: [email protected] Electronics Technology, Systems and Automation Engineering Department, University of Cantabria, Av. Los Castros s/n 39005 Santander (Cantabria), Spain
coarse transformation that aligns them in a suitable way can be calculated. On the other hand, the algorithms that use extrinsic properties will be subject to one important restriction: as they match properties that are relative to a coordinate system, the surfaces must be roughly aligned in order to establish point correspondences. Therefore, these algorithms (such as the ICP algorithm [21,22] and its variants [23]) are used to refine that initial transformation and obtain a more precise one. Sinc
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