A Coarse Registration Algorithm Between 3D Point Cloud and CAD Model of Non-cooperative Object for Space Manipulator

2019 ◽  
pp. 616-626
Author(s):  
Qimeng Tan ◽  
Delun Li ◽  
Congcong Bao ◽  
Ming Chen ◽  
Yun Zhang
Keyword(s):  
Point Cloud ◽  
Cad Model ◽  
3D Point Cloud ◽  
Space Manipulator ◽  
Registration Algorithm ◽  
Coarse Registration

scholarly journals LARGE SCALE 3D POINT CLOUD MODELING FROM CAD DATABASE IN COMPLEX INDUSTRIAL ENVIRONMENTS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 391-398
Author(s):  
M. Corsia ◽  
T. Chabardès ◽  
H. Bouchiba ◽  
A. Serna
Keyword(s):  
Real World ◽  
Computer Aided Design ◽  
Point Cloud ◽  
Large Scale ◽  
Region Growing ◽  
Cad Model ◽  
3D Point Cloud ◽  
Multi Scale ◽  
Cad Models ◽  
Cloud Modeling

Abstract. In this paper, we present a method to build Computer Aided Design (CAD) representations of dense 3D point cloud scenes by queries in a large CAD model database. This method is applied to real world industrial scenes for infrastructure modeling. The proposed method firstly relies on a region growing algorithm based on novel edge detection method. This algorithm is able to produce geometrically coherent regions which can be agglomerated in order to extract the objects of interest of an industrial environment. Each segment is then processed to compute relevant keypoints and multi-scale features in order to be compared to all CAD models from the database. The best fitting model is estimated together with the rigid six degree of freedom (6 DOF) transformation for positioning the CAD model on the 3D scene. The proposed novel keypoints extractor achieves robust and repeatable results that captures both thin geometrical details and global shape of objects. Our new multi-scale descriptor stacks geometrical information around each keypoint at short and long range, allowing non-ambiguous matching for object recognition and positioning. We illustrate the efficiency of our method in a real-world application on 3D segmentation and modeling of electrical substations.

Registration Algorithm for Three-Dimensional Point Cloud Data

2015 ◽  
Vol 741 ◽  
pp. 237-240
Author(s):  
Li Lun Huang ◽  
Wen Guo Li ◽  
Qi Le Yang ◽  
Ying Chun Chen
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Singular Value ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Icp Algorithm ◽  
Cloud Data ◽  
Registration Algorithm ◽  
Mathematical Algorithms ◽  
Value Decomposition

The principle of registration of the 3D point cloud data and the current algorithms are compared, and ICP algorithm is chosen since its fast convergence speed, high precision, and simple objective function. On the basis of ICP algorithm, singular value decomposition and four-array method are analysed by programming program, and all the mathematical algorithms is transformed into programming language by Matlab software.

scholarly journals Pairwise Registration Algorithm for Large-Scale Planar Point Cloud Used in Flatness Measurement

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4860
Author(s):  
Zichao Shu ◽  
Songxiao Cao ◽  
Qing Jiang ◽  
Zhipeng Xu ◽  
Jianbin Tang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Three Dimensional ◽  
Planar Point ◽  
Registration Algorithm ◽  
Geometric Consistency ◽  
Coarse Registration ◽  
Pairwise Registration ◽  
Flatness Measurement

In this paper, an optimized three-dimensional (3D) pairwise point cloud registration algorithm is proposed, which is used for flatness measurement based on a laser profilometer. The objective is to achieve a fast and accurate six-degrees-of-freedom (6-DoF) pose estimation of a large-scale planar point cloud to ensure that the flatness measurement is precise. To that end, the proposed algorithm extracts the boundary of the point cloud to obtain more effective feature descriptors of the keypoints. Then, it eliminates the invalid keypoints by neighborhood evaluation to obtain the initial matching point pairs. Thereafter, clustering combined with the geometric consistency constraints of correspondences is conducted to realize coarse registration. Finally, the iterative closest point (ICP) algorithm is used to complete fine registration based on the boundary point cloud. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of boundary extraction and registration performance.

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