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

Points Registration for Roadside LiDAR Sensors

2019 ◽  
Vol 2673 (9) ◽  
pp. 627-639 ◽  
Author(s):  
Jianqing Wu ◽  
Hao Xu ◽  
Wei Liu
Keyword(s):  
Ground Surface ◽  
Cost Effective ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Connected Vehicle ◽  
Point Selection ◽  
Registration Method ◽  
Data Registration ◽  
The Difference ◽  
Iterative Closest Point Algorithm

Roadside LiDAR deployment provides a solution to obtain the real-time high-resolution micro traffic data of unconnected road users for the connected-vehicle road network. Single roadside LiDAR sensor has a lot of limitations considering the scant coverage and the difficulty of handling object occlusion issue. Multiple roadside LiDAR sensors can provide a larger coverage and eliminate the object occlusion issue. To combine different LiDAR sensors, it is necessary to integrate the point clouds into the same coordinate system. The existing points registration methods serving mapping scans or autonomous sensing systems could not be directly used for roadside LiDAR sensors considering the different feature of point clouds and the spare points in the cost-effective roadside LiDAR sensors. This paper developed an approach for roadside LiDAR points registration. The developed points-aggregation-based partial iterative closest point algorithm (PA-PICP) is a semi-automatic points registration method, which contains two major parts: XY data registration and Z adjustment. A semi-automatic key point selection method was introduced. The partial iterative closest point was applied to minimize the difference between different LiDARs in the XY plane. The intersection of ground surface between different LiDARs was used for Z-axis adjustment. The performance of the developed procedure was evaluated with field-collected LiDAR data. The results showed the effectiveness and accuracy of data integration using PA-PICP was greatly improved compared with points registration using the traditional iterative closest point. The case studies also showed that the occlusion issue can be fixed after PA-PICP points registration.

LIE GROUP FRAMEWORK OF ITERATIVE CLOSEST POINT ALGORITHM FOR n-D DATA REGISTRATION

2009 ◽  
Vol 23 (06) ◽  
pp. 1201-1220 ◽  
Author(s):  
SHIHUI YING ◽  
JIGEN PENG ◽  
SHAOYI DU ◽  
HONG QIAO
Keyword(s):  
Mathematical Model ◽  
Lie Group ◽  
Optimization Problem ◽  
Global Minimum ◽  
Group Representation ◽  
Iterative Closest Point ◽  
Geometric Transformation ◽  
Data Sets ◽  
Data Registration ◽  
Iterative Closest Point Algorithm

The iterative closet point (ICP) method is a dominant method for data registration that has attracted extensive attention. In this paper, a unified mathematical model of ICP based on Lie group representation is established. Under the framework, the registration problem is formulated into an optimization problem over a certain Lie group. In order to simplify the model and to reduce the dimension of parameter space, the translation part of geometric transformation is eliminated by calibrating the centers of two data sets under registration. As a result, a fast algorithm by solving an iterative linear system is designed for the optimization problem on Lie groups. Moreover, PCA and ICA methods are jointly applied to estimate the initial registration to achieve the global minimum. Finally, several illustrations and comparison experiments are presented to test the performance of the proposed 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>

New iterative closest point algorithm for isotropic scaling registration of point sets with noise

2016 ◽  
Vol 38 ◽  
pp. 207-216 ◽  
Author(s):  
Shaoyi Du ◽  
Juan Liu ◽  
Bo Bi ◽  
Jihua Zhu ◽  
Jianru Xue
Keyword(s):  
Iterative Closest Point ◽  
Point Sets ◽  
Iterative Closest Point Algorithm

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
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