Point Cloud Adaptive Registration Algorithm Based on Color Information and Geometric Information

2020 ◽  
Vol 57 (20) ◽  
pp. 201015
Author(s):  
王勇 Wang Yong ◽  
黎春 Li Chun
Keyword(s):  
Point Cloud ◽  
Color Information ◽  
Geometric Information ◽  
Registration Algorithm

scholarly journals Color Point Cloud Registration Algorithm Based on Hue

2021 ◽  
Vol 11 (12) ◽  
pp. 5431
Author(s):  
Siyu Ren ◽  
Xiaodong Chen ◽  
Huaiyu Cai ◽  
Yi Wang ◽  
Haitao Liang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Error Function ◽  
Point Clouds ◽  
Tangent Plane ◽  
Geometric Error ◽  
Color Information ◽  
Geometric Information ◽  
Point Cloud Registration ◽  
Registration Algorithm ◽  
Lighting Conditions

ICP is a well-known method for point cloud registration but it only uses geometric information to do this, which will result in bad results in some similar structures. Adding color information when registering will improve the performance. However, color information of point cloud, such as gray, varies differently under different lighting conditions. Using gray as the color information to register can cause large errors and even wrong results. To solve this problem, we propose a color point cloud registration algorithm based on hue, which has good robustness at different lighting conditions. We extract the hue component according to the color information of point clouds and make the hue distribution of the tangent plane continuous. The error function consists of color and geometric error of two point clouds under the current transformation. We optimize the error function using the Gauss–Newton method. If the value of the error function is less than the preset threshold or the maximum number of iterations is reached, the current transformation relationship is required. We use RGB-D Scenes V2 dataset to evaluate our algorithm and the results show that the average recall of our algorithms is 8.63% higher than that of some excellent algorithms, and its RMSE of 14.3% is lower than that of the other compared algorithms.

scholarly journals Hue-assisted automatic registration of color point clouds

2014 ◽  
Vol 1 (4) ◽  
pp. 223-232 ◽  
Author(s):  
Hao Men ◽  
Kishore Pochiraju
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Local Geometry ◽  
Automatic Registration ◽  
Color Information ◽  
Registration Accuracy ◽  
Surface Color ◽  
Registration Algorithm ◽  
Sample Density ◽  
Occupancy Grids

Abstract This paper describes a variant of the extended Gaussian image based registration algorithm for point clouds with surface color information. The method correlates the distributions of surface normals for rotational alignment and grid occupancy for translational alignment with hue filters applied during the construction of surface normal histograms and occupancy grids. In this method, the size of the point cloud is reduced with a hue-based down sampling that is independent of the point sample density or local geometry. Experimental results show that use of the hue filters increases the registration speed and improves the registration accuracy. Coarse rigid transformations determined in this step enable fine alignment with dense, unfiltered point clouds or using Iterative Common Point (ICP) alignment techniques.

Point Cloud Registration Algorithm Combined Gaussian Mixture Model and Point-to-Plane Metric

2018 ◽  
Vol 30 (4) ◽  
pp. 642
Author(s):  
Guichao Lin ◽  
Yunchao Tang ◽  
Xiangjun Zou ◽  
Qing Zhang ◽  
Xiaojie Shi ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Point Cloud ◽  
Gaussian Mixture ◽  
Point Cloud Registration ◽  
Registration Algorithm

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 Individual retrieval based on oral cavity point cloud data and correntropy-based registration algorithm

2020 ◽  
Vol 14 (12) ◽  
pp. 2675-2681
Author(s):  
Wenting Cui ◽  
Jianyi Liu ◽  
Shaoyi Du ◽  
Yuying Liu ◽  
Teng Wan ◽  
...  
Keyword(s):  
Oral Cavity ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Registration Algorithm

scholarly journals FAST REGISTRATION OF TERRESTRIAL LIDAR POINT CLOUD AND SEQUENCE IMAGES

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 875-879
Author(s):  
J. Shao ◽  
W. Zhang ◽  
Y. Zhu ◽  
A. Shen
Keyword(s):  
Point Cloud ◽  
Engineering Application ◽  
Image Point ◽  
Color Information ◽  
Registration Accuracy ◽  
Registration Method ◽  
Terrestrial Lidar ◽  
The Rich ◽  
Ground Based Lidar

Image has rich color information, and it can help to promote recognition and classification of point cloud. The registration is an important step in the application of image and point cloud. In order to give the rich texture and color information for LiDAR point cloud, the paper researched a fast registration method of point cloud and sequence images based on the ground-based LiDAR system. First, calculating transformation matrix of one of sequence images based on 2D image and LiDAR point cloud; second, using the relationships of position and attitude information among multi-angle sequence images to calculate all transformation matrixes in the horizontal direction; last, completing the registration of point cloud and sequence images based on the collinear condition of image point, projective center and LiDAR point. The experimental results show that the method is simple and fast, and the stitching error between adjacent images is litter; meanwhile, the overall registration accuracy is high, and the method can be used in engineering application.

Point Cloud Registration Algorithm Based on Extended Point Feature Histogram Feature

2019 ◽  
Vol 56 (24) ◽  
pp. 241503
Author(s):  
汤慧 Tang Hui ◽  
周明全 Zhou Mingquan ◽  
耿国华 Geng Guohua
Keyword(s):  
Point Cloud ◽  
Point Cloud Registration ◽  
Registration Algorithm ◽  
Histogram Feature ◽  
Extended Point ◽  
Point Feature
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