A modified iterative closest point algorithm for shape registration

2016 ◽  
Author(s):  
Dmitrii Tihonkih ◽  
Artyom Makovetskii ◽  
Vladislav Kuznetsov
Keyword(s):  
Iterative Closest Point ◽  
Shape Registration ◽  
Iterative Closest Point Algorithm

Improvements to the Iterative Closest Point Algorithm for Shape Registration in Manufacturing

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Tsz-Ho Kwok ◽  
Kai Tang
Keyword(s):  
Stability Analysis ◽  
Selection Method ◽  
Iterative Closest Point ◽  
Normal Space ◽  
Point Selection ◽  
Icp Algorithm ◽  
Shape Registration ◽  
Normal Vectors ◽  
Iterative Closest Point Algorithm ◽  
The Stability

Iterative closest point (ICP) is a popular algorithm used for shape registration while conducting inspection during a production process. A crucial key to the success of the ICP is the choice of point selection method. While point selection can be customized for a particular application using its prior knowledge, normal-space sampling (NSS) is commonly used when normal vectors are available. Normal-based approach can be further improved by stability analysis—called covariance sampling. The stability analysis should be accurate to ensure the correctness of covariance sampling. In this paper, we go deep into the details of covariance sampling, and propose a few improvements for stability analysis. We theoretically and experimentally show that these improvements are necessary for further success in covariance sampling. Experimental results show that the proposed method is more efficient and robust for the ICP algorithm.

Robust iterative closest point algorithm with bounded rotation angle for 2D registration

2016 ◽  
Vol 195 ◽  
pp. 172-180 ◽  
Author(s):  
Chunjia Zhang ◽  
Shaoyi Du ◽  
Juan Liu ◽  
Yongxin Li ◽  
Jianru Xue ◽  
...  
Keyword(s):  
Rotation Angle ◽  
Iterative Closest Point ◽  
Iterative Closest Point Algorithm

scholarly journals ITERATIVE CLOSEST POINT ALGORITHM FOR ACCURATE REGISTRATION OF COARSELY REGISTERED POINT CLOUDS WITH CITYGML MODELS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 201-208 ◽  
Author(s):  
S. Goebbels ◽  
R. Pohle-Fröhlich ◽  
P. Pricken
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Iteration Step ◽  
Iterative Closest Point ◽  
Target Point ◽  
Standard Tool ◽  
Iterative Closest Point Algorithm ◽  
Test Scenarios ◽  
Plane Mode ◽  
City Models

<p><strong>Abstract.</strong> The Iterative Closest Point algorithm (ICP) is a standard tool for registration of a source to a target point cloud. In this paper, ICP in point-to-plane mode is adopted to city models that are defined in CityGML. With this new point-to-model version of the algorithm, a coarsely registered photogrammetric point cloud can be matched with buildings’ polygons to provide, e.g., a basis for automated 3D facade modeling. In each iteration step, source points are projected to these polygons to find correspondences. Then an optimization problem is solved to find an affine transformation that maps source points to their correspondences as close as possible. Whereas standard ICP variants do not perform scaling, our algorithm is capable of isotropic scaling. This is necessary because photogrammetric point clouds obtained by the structure from motion algorithm typically are scaled randomly. Two test scenarios indicate that the presented algorithm is faster than ICP in point-to-plane mode on sampled city models.</p>

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