An Improved Iterative Closest Point Algorithm for Rigid Point Registration

2014 ◽  
pp. 255-263 ◽  
Author(s):  
Junfen Chen ◽  
Bahari Belaton
Keyword(s):  
Iterative Closest Point ◽  
Iterative Closest Point 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

Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm

2005 ◽  
Vol 23 (3) ◽  
pp. 299-309 ◽  
Author(s):  
Dmitry Chetverikov ◽  
Dmitry Stepanov ◽  
Pavel Krsek
Keyword(s):  
Iterative Closest Point ◽  
Point Sets ◽  
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>

scholarly journals A Stochastic Iterative Closest Point Algorithm (stochastICP)

2001 ◽  
pp. 762-769 ◽  
Author(s):  
G. P. Penney ◽  
P. J. Edwards ◽  
A. P. King ◽  
J. M. Blackall ◽  
P. G. Batchelor ◽  
...  
Keyword(s):  
Iterative Closest Point ◽  
Iterative Closest Point Algorithm

Two-Viewing Angle Ladar Data Registration Based on Improved Iterative Closest-Point Algorithm

2012 ◽  
Vol 32 (11) ◽  
pp. 1128007 ◽  
Author(s):  
赵明波 Zhao Mingbo ◽  
何峻 He Jun ◽  
罗小波 Luo Xiaobo ◽  
付强 Fu Qiang
Keyword(s):  
Iterative Closest Point ◽  
Viewing Angle ◽  
Data Registration ◽  
Iterative Closest Point Algorithm

An Iterative Closest Point Algorithm Based on Biunique Correspondence of Point Clouds for 3D Reconstruction

2015 ◽  
Vol 35 (5) ◽  
pp. 0515003 ◽  
Author(s):  
韦盛斌 Wei Shengbin ◽  
王少卿 Wang Shaoqing ◽  
周常河 Zhou Changhe ◽  
刘昆 Liu Kun ◽  
范鑫 Fan Xin
Keyword(s):  
3D Reconstruction ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Iterative Closest Point Algorithm
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