Iterative closest point for accurate plane detection in unorganized point clouds

2021 ◽  
Vol 125 ◽  
pp. 103610 ◽  
Author(s):  
Cedrique Fotsing ◽  
Nareph Menadjou ◽  
Christophe Bobda
Keyword(s):  
Point Clouds ◽  
Iterative Closest Point

scholarly journals OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1563
Author(s):  
Ruibing Wu ◽  
Ziping Yu ◽  
Donghong Ding ◽  
Qinghua Lu ◽  
Zengxi Pan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Multiple Point ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Cloud Models ◽  
Comparison Results ◽  
Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP 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>

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

scholarly journals Iterative K-Closest Point Algorithms for Colored Point Cloud Registration

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5331
Author(s):  
Ouk Choi ◽  
Min-Gyu Park ◽  
Youngbae Hwang
Keyword(s):  
Real World ◽  
Point Cloud ◽  
Point Clouds ◽  
The Other ◽  
Iterative Closest Point ◽  
Depth Images ◽  
The Cost ◽  
Soft Matching ◽  
Colored Point ◽  
Refinement Algorithm

We present two algorithms for aligning two colored point clouds. The two algorithms are designed to minimize a probabilistic cost based on the color-supported soft matching of points in a point cloud to their K-closest points in the other point cloud. The first algorithm, like prior iterative closest point algorithms, refines the pose parameters to minimize the cost. Assuming that the point clouds are obtained from RGB-depth images, our second algorithm regards the measured depth values as variables and minimizes the cost to obtain refined depth values. Experiments with our synthetic dataset show that our pose refinement algorithm gives better results compared to the existing algorithms. Our depth refinement algorithm is shown to achieve more accurate alignments from the outputs of the pose refinement step. Our algorithms are applied to a real-world dataset, providing accurate and visually improved results.

Application of the 'Iterative Closest Point' Algorithm for Determination of Rotation Axis of a Turntable

2013 ◽  
Vol 199 ◽  
pp. 273-278
Author(s):  
Ireneusz Wróbel
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Rotation Axis ◽  
Rapid Development ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Mechanical Parts ◽  
Scanning Process ◽  
Iterative Closest Point Algorithm

Reverse engineering [ is a field of technology which has been under rapid development for several recent years. Optic scanners are basic devices used as reverse engineering tools. Point cloud describes the shape of a scanned object. Automatic turntable is a device which enables a scanning process from different viewing angles. In the paper, the algorithm is described which has been used for determination of rotation axis of a turntable. The obtained axis constitutes the base for an aggregation of particular point clouds into single resultant common cloud describing the shape of the scanned object. Usability of this algorithm for precise scanning of mechanical parts was validated, precision of shape replication was also evaluated.

Accurate Registration of 3D Point Clouds Based on Keypoint Extraction and Improved Iterative Closest Point Algorithm

2020 ◽  
Vol 57 (6) ◽  
pp. 061002
Author(s):  
彭真 Peng Zhen ◽  
吕远健 Lü Yuanjian ◽  
渠超 Qu Chao ◽  
朱大虎 Zhu Dahu
Keyword(s):  
Point Clouds ◽  
Iterative Closest Point ◽  
3D Point Clouds ◽  
Iterative Closest Point Algorithm

Merging the point clouds of the head and ear by using the iterative closest point method

2016 ◽  
Vol 1 (3) ◽  
pp. 305
Author(s):  
Ming Zhang ◽  
Roger Ball ◽  
Nathaniel J. Martin ◽  
Yan Luximon
Keyword(s):  
Point Clouds ◽  
Point Method ◽  
Iterative Closest Point ◽  
Closest Point Method

scholarly journals Vehicle pose estimation algorithm for parking automated guided vehicle

2020 ◽  
Vol 17 (1) ◽  
pp. 172988141989133
Author(s):  
Zhixiong Ning ◽  
Xin Wang ◽  
Jun Wang ◽  
Huafeng Wen
Keyword(s):  
Pose Estimation ◽  
Symmetry Plane ◽  
Point Clouds ◽  
Vehicle Body ◽  
Iterative Closest Point ◽  
Support Vector ◽  
Automated Guided Vehicle ◽  
Alignment Algorithm ◽  
Registration Method ◽  
Iterative Closest Point Algorithm

Parking automated guided vehicle is more and more widely used for efficient automatic parking and one of the tough challenges for parking automated guided vehicle is the problem of vehicle pose estimation. The traditional algorithms rely on the profile information of vehicle body and sensors are required to be mounted at the top of the vehicle. However, the sensors are always mounted at a lower place because the height of a parking automated guided vehicle is always beyond 0.2mm, where we can only get the vehicle wheel information and limited vehicle body information. In this article, a novel method is given based on the symmetry of wheel point clouds collected by 3-D lidar. Firstly, we combine cell-based method with support vector machine classifier to segment ground point clouds. Secondly, wheel point clouds are segmented from obstacle point clouds and their symmetry are corrected by iterative closest point algorithm. Then, we estimate the vehicle pose by the symmetry plane of wheel point clouds. Finally, we compare our method with registration method that combines sample consensus initial alignment algorithm and iterative closest point algorithm. The experiments have been carried out.

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.

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