Authors:
Sungho Suh
;
Hansang Cho
and
Donglok Kim
Affiliation:
Samsung Electro-Mechanics, Korea, Republic of
Keyword(s):
Point Cloud, 3D Affine Transformation, Height Map Matching.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image Understanding
;
Image-Based Modeling
;
Object Recognition
;
Pattern Recognition
;
Robotics
;
Software Engineering
Abstract:
3D point cloud data is used for 3D model acquisition, geometry processing and 3D inspection. Registration of
3D point cloud data is crucial for each field. The difference between 2D image registration and 3D point cloud
registration is that the latter requires several things to be considered: translation on each plane, rotation, tilt
and etc. This paper describes a method of registering 3D point cloud data with noise. The relationship between
the two sets of 3D point cloud data can be obtained by Affine transformation. In order to calculate 3D Affine
transformation matrix, corresponding points are required. To find the corresponding points, we use the height
map which is projected from 3D point cloud data onto XY plane. We formulate the height map matching as a
cost function and estimate the corresponding points. To find the proper 3D Affine transformation matrix, we
formulate a cost function which uses the relationship of the corresponding points. Also the proper 3D Affine
transform
ation matrix can be calculated by minimizing the cost function. The experimental results show that
the proposed method can be applied to various objects and gives better performance than the previous work.
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