A Matching Algorithm and Error Analysis on the Basis of Triangle Mesh

2011 ◽  
Vol 230-232 ◽  
pp. 968-972
Author(s):  
Li Min Zhu ◽  
Chun Guo Liu ◽  
Shao Hui Wang ◽  
Ming Zhe Li
Keyword(s):  
Work Piece ◽  
Cad Model ◽  
Shape Error ◽  
Triangle Mesh ◽  
Matching Method ◽  
Initial Value ◽  
Icp Algorithm ◽  
Matching Algorithm ◽  
Cloud Data ◽  
Fine Registration

In reverse engineering, in order to analyze the accuracy of formed work-piece, the shape error between the formed work-piece and its CAD model need to be calculated. In this paper, a matching method on the basis of triangle mesh is proposed. This matching method includes three parts: pre-positioning, rough registration and fine registration. Firstly, the maximum projective plane of cloud data is designed to parallel to the xy plane through translation and rotation, and cloud data is translated by a vector which can be obtained with the point with the maximum z coordinate and the origin of coordinates. Secondly, cloud data is gradually rotated 360 degree around z axis confirm the angle making the error minimum. In this way, an initial value of ICP algorithm can be obtained, which can avoid the local convergence in the algorithm. Finally, ICP algorithm can be applied to calculate the surface error further. After matching, the error between work-piece and the CAD model can be calculated by interpolation based on triangle mesh. The results show that the matching method can obtain the higher matching accuracy.

scholarly journals An Experimental Study of a New Keypoint Matching Algorithm for Automatic Point Cloud Registration

2021 ◽  
Vol 10 (4) ◽  
pp. 204
Author(s):  
Ramazan Alper Kuçak ◽  
Serdar Erol ◽  
Bihter Erol
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Data Systems ◽  
Icp Algorithm ◽  
Matching Algorithm ◽  
Suggested Algorithm ◽  
Registration Process ◽  
Building Facade ◽  
Coarse Registration ◽  
Fine Registration

Light detection and ranging (LiDAR) data systems mounted on a moving or stationary platform provide 3D point cloud data for various purposes. In applications where the interested area or object needs to be measured twice or more with a shift, precise registration of the obtained point clouds is crucial for generating a healthy model with the combination of the overlapped point clouds. Automatic registration of the point clouds in the common coordinate system using the iterative closest point (ICP) algorithm or its variants is one of the frequently applied methods in the literature, and a number of studies focus on improving the registration process algorithms for achieving better results. This study proposed and tested a different approach for automatic keypoint detecting and matching in coarse registration of the point clouds before fine registration using the ICP algorithm. In the suggested algorithm, the keypoints were matched considering their geometrical relations expressed by means of the angles and distances among them. Hence, contributing the quality improvement of the 3D model obtained through the fine registration process, which is carried out using the ICP method, was our aim. The performance of the new algorithm was assessed using the root mean square error (RMSE) of the 3D transformation in the rough alignment stage as well as a-prior and a-posterior RMSE values of the ICP algorithm. The new algorithm was also compared with the point feature histogram (PFH) descriptor and matching algorithm, accompanying two commonly used detectors. In result of the comparisons, the superiorities and disadvantages of the suggested algorithm were discussed. The measurements for the datasets employed in the experiments were carried out using scanned data of a 6 cm × 6 cm × 10 cm Aristotle sculpture in the laboratory environment, and a building facade in the outdoor as well as using the publically available Stanford bunny sculpture data. In each case study, the proposed algorithm provided satisfying performance with superior accuracy and less iteration number in the ICP process compared to the other coarse registration methods. From the point clouds where coarse registration has been made with the proposed method, the fine registration accuracies in terms of RMSE values with ICP iterations are calculated as ~0.29 cm for Aristotle and Stanford bunny sculptures, ~2.0 cm for the building facade, respectively.

Registration of 3-D Shape with Free-Form Surface Using Imageware and Genetic Algorithm

2012 ◽  
Vol 472-475 ◽  
pp. 317-322 ◽  
Author(s):  
Xiao Yi Wang ◽  
Jing Chen ◽  
Jiang Zhu ◽  
Yoshio Saito ◽  
Tomohisa Tanaka
Keyword(s):  
Genetic Algorithm ◽  
Point Cloud ◽  
Point Clouds ◽  
Free Form ◽  
Work Piece ◽  
Cad Model ◽  
Manufacturing Accuracy ◽  
Free Form Surface ◽  
Point Data ◽  
Fine Registration

Registration of 3-D shape is significant in quantizing the error between the part and its CAD model and evaluating the part's manufacturing accuracy. In the past, various improved methods of the iterative closest point (ICP) had been proposed in registration. However, without fine initial pose of point clouds, the ICP algorithm often could not converge to the best (or near best) solution. According to the characteristics of 3-D shape with free-form surface, a new method for registration of 3-D shape with free-form surface is given, by which there are not rigid requests in initial pose of point data and the 3-D shape model could be in arbitrary positions and orientations in space. To improve the efficiency and accuracy of solving, this method is divided into general registration and fine registration. General registration is to fit rapidly and roughly the measured point cloud to designing point cloud from CAD model by Imageware. Fine registration is to further accurately fit the two group points using genetic algorithm (GA). Case study is finally given for a work piece with free-form surface to show the effectiveness of the above method.

scholarly journals A Coarse-to-Fine Registration Approach for Point Cloud Data with Bipartite Graph Structure

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 263
Author(s):  
Munan Yuan ◽  
Xiru Li ◽  
Longle Cheng ◽  
Xiaofeng Li ◽  
Haibo Tan
Keyword(s):  
Bipartite Graph ◽  
Point Cloud ◽  
Graph Matching ◽  
Data Sets ◽  
Graph Structure ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Alignment Process ◽  
Coarse To Fine ◽  
Fine Registration

Alignment is a critical aspect of point cloud data (PCD) processing, and we propose a coarse-to-fine registration method based on bipartite graph matching in this paper. After data pre-processing, the registration progress can be detailed as follows: Firstly, a top-tail (TT) strategy is designed to normalize and estimate the scale factor of two given PCD sets, which can combine with the coarse alignment process flexibly. Secondly, we utilize the 3D scale-invariant feature transform (3D SIFT) method to extract point features and adopt fast point feature histograms (FPFH) to describe corresponding feature points simultaneously. Thirdly, we construct a similarity weight matrix of the source and target point data sets with bipartite graph structure. Moreover, the similarity weight threshold is used to reject some bipartite graph matching error-point pairs, which determines the dependencies of two data sets and completes the coarse alignment process. Finally, we introduce the trimmed iterative closest point (TrICP) algorithm to perform fine registration. A series of extensive experiments have been conducted to validate that, compared with other algorithms based on ICP and several representative coarse-to-fine alignment methods, the registration accuracy and efficiency of our method are more stable and robust in various scenes and are especially more applicable with scale factors.

scholarly journals OPTIMIZATION ALGORITHM OF TILTED IMAGE MATCHING BASED ON ADAPTIVE INITIAL OBJECT ASPECT

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 547-554
Author(s):  
C. Zhang ◽  
Y. Ge ◽  
Q. Zhang ◽  
B. Guo
Keyword(s):  
Least Squares ◽  
Image Matching ◽  
Normal Vector ◽  
Matching Method ◽  
Initial Value ◽  
Initial State ◽  
Matching Process ◽  
Low Degree ◽  
Verification Process ◽  
Initial Object

Abstract. When adopting the matching method of the least squares image based on object-patch to match tilted images, problems like the low degree of connection points for images with the discontinuity of depth or the discrepancy in elevation or low availability of aerotriangulation points would frequently appear. To address such problems, a tilted-image-matching algorithm based on an adaptive initial object-patch is proposed by this paper. By means of the existing initial values of the interior and exterior orientation elements of the tilted image and the information of object points generated in the matching process, the algorithm takes advantage of the method of multi-patch forward intersection and object variance partition so as to adaptively calculate the elevation of the object-patch and the initial value of the normal vector direction angle. Furthermore, this algorithm aims to solve the problem of difficulties in matching the tilted image with its corresponding points brought about by the low accuracy of the initial value of the tilted image when adopting the matching method of the least squares image based on object-patch to match the tilted image with high discrepancy in elevation. We adopt the algorithm as proposed in this paper and the least squares image matching method in which the initial state of the object-patch is horizontal to the object-patch respectively to conduct the verification process of comparing and matching two groups of tilted images. Finally, the effectiveness of the algorithm as proposed in this paper is verified by the testing results.

scholarly journals On-Site 4-in-1 Alignment: Visualization and Interactive CAD Model Retrofitting Using UAV, LiDAR’s Point Cloud Data, and Video

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3908 ◽  
Author(s):  
Pavan Kumar B. N. ◽  
Ashok Kumar Patil ◽  
Chethana B. ◽  
Young Ho Chai
Keyword(s):  
Computer Aided Design ◽  
Point Cloud ◽  
Laser Scanner ◽  
Video Frame ◽  
Cad Model ◽  
Point Cloud Data ◽  
Registration Method ◽  
Industrial Facilities ◽  
Cloud Data ◽  
Generic Framework

Acquisition of 3D point cloud data (PCD) using a laser scanner and aligning it with a video frame is a new approach that is efficient for retrofitting comprehensive objects in heavy pipeline industrial facilities. This work contributes a generic framework for interactive retrofitting in a virtual environment and an unmanned aerial vehicle (UAV)-based sensory setup design to acquire PCD. The framework adopts a 4-in-1 alignment using a point cloud registration algorithm for a pre-processed PCD alignment with the partial PCD, and frame-by-frame registration method for video alignment. This work also proposes a virtual interactive retrofitting framework that uses pre-defined 3D computer-aided design models (CAD) with a customized graphical user interface (GUI) and visualization of a 4-in-1 aligned video scene from a UAV camera in a desktop environment. Trials were carried out using the proposed framework in a real environment at a water treatment facility. A qualitative and quantitative study was conducted to evaluate the performance of the proposed generic framework from participants by adopting the appropriate questionnaire and retrofitting task-oriented experiment. Overall, it was found that the proposed framework could be a solution for interactive 3D CAD model retrofitting on a combination of UAV sensory setup-acquired PCD and real-time video from the camera in heavy industrial facilities.

An Optimal Matching Algorithm Based on Rough Localization and Exact Adjustment

2005 ◽  
Vol 291-292 ◽  
pp. 661-666 ◽  
Author(s):  
Y.W. Sun ◽  
J.T. Xu
Keyword(s):  
Computer Simulation ◽  
Global Optimization ◽  
Iterative Process ◽  
Matching Method ◽  
Matching Algorithm ◽  
Local Distortion ◽  
Normal Projection ◽  
Initial Transformation ◽  
Bounding Boxes

Aiming at the problems on estimate of the initial transformation, matching precision and global optimization in matching, this paper presents a matching method, which is based on rough localization and exact adjustment. By means of rotation, translation and coincidence of the minimum bounding boxes of surface and measured points, rough localization is realized, which produces a good estimate for the follow-up iterative algorithm. The closest points that were calculated via the normal projection of the sample points then establish the correspondence between two objects. An iterative process is used in the exact adjustment to ensure the global optimization of match. For reducing the effect of bad points or local distortion, a maximum distance criterion is adopted to refine the transformation between objects. A computer simulation is given to demonstrate that the algorithm is steady and effective.

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