A Fast Global Registration Algorithm Based on Correcting Point Cloud Principal Component Coordinate System

Aimed at the global registration problem of the single-closed ring multi-stations point cloud, a formula in order to calculate the error of rotation matrix was constructed according to the definition of error. The global registration algorithm of multi-station point cloud was derived to minimize the error of rotation matrix. And fast-computing formulas of transformation matrix with whose implementation steps and simulation experiment scheme was given. Compared three different processing schemes of multi-station point cloud, the experimental results showed that the effectiveness of the new global registration method was verified, and it could effectively complete the global registration of point cloud.

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Iterative automatic global registration algorithm for multi-view point cloud of underground tunnel space

Measurement and Control ◽

10.1177/00202940211003935 ◽

2021 ◽

Vol 54 (3-4) ◽

pp. 385-395

Author(s):

Ming Guo ◽

Bingnan Yan ◽

Guoli Wang ◽

Pingjun Nie ◽

Deng Pan ◽

...

Keyword(s):

Point Cloud ◽

Iteration Process ◽

Point Cloud Data ◽

Subway Tunnel ◽

Registration Accuracy ◽

Point Cloud Registration ◽

Underground Tunnel ◽

Cloud Data ◽

Registration Algorithm ◽

Global Registration

Aiming at the narrow and long tunnel structure, few internal features, and a large amount of point cloud data, the existing registration algorithms and commercial software registration results are not ideal, an iterative global registration algorithm is proposed for massive underground tunnel point cloud registration, which is composed of local initial pose acquisition and global adjustment. Firstly, the feature point coordinates in the point cloud are extracted, and then the station-by-station registration is performed according to the Rodrigues matrix. Finally, the registration result is considered as the initial value of the parameter, and the global adjustment of all observations is carried out. The observation values are weighted by the selection weight iteration method and the weights are constantly modified in the iteration process until the threshold conditions are met and the iteration stops. In this paper, the experimental data, made up of 85 stations of point cloud data, are from the Xiamen subway tunnel, which is about 1300 m long. When the accumulated error of station-to-station registration is too large, several stations are regarded as partial wholes, and the optimal registration is achieved through multiple global adjustments, and the registration accuracy is within 5 mm. Experimental results confirm the feasibility and effectiveness of the algorithm, which provides a new method for point cloud registration of underground space tunnel.

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High-precision detection method for large and complex steel structures based on global registration algorithm and automatic point cloud generation

Measurement ◽

10.1016/j.measurement.2020.108765 ◽

2021 ◽

Vol 172 ◽

pp. 108765

Author(s):

Ming Guo ◽

Mengxi Sun ◽

Deng Pan ◽

Ming Huang ◽

Bingnan Yan ◽

...

Keyword(s):

High Precision ◽

Point Cloud ◽

Detection Method ◽

Steel Structures ◽

Registration Algorithm ◽

Global Registration

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

Journal of Computer-Aided Design & Computer Graphics ◽

10.3724/sp.j.1089.2018.16472 ◽

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

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Parametric Surface Modelling for Tea Leaf Point Cloud Based on Non-Uniform Rational Basis Spline Technique

Sensors ◽

10.3390/s21041304 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1304

Author(s):

Wenchao Wu ◽

Yongguang Hu ◽

Yongzong Lu

Keyword(s):

Point Cloud ◽

Principal Component ◽

Point Clouds ◽

Parametric Surface ◽

B Spline ◽

Surface Modelling ◽

Modelling Method ◽

3D Architecture ◽

Tea Leaf ◽

Fitting Error

Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1 mm, MV < 2 mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud.

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Matching Point Clouds with STL Models by Using the Principle Component Analysis and a Decomposition into Geometric Primitives

Applied Sciences ◽

10.3390/app11052268 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2268

Author(s):

Erika Straková ◽

Dalibor Lukáš ◽

Zdenko Bobovský ◽

Tomáš Kot ◽

Milan Mihola ◽

...

Keyword(s):

Point Cloud ◽

Principal Component ◽

Point Clouds ◽

Component Analysis ◽

Language Models ◽

Computational Time ◽

Dominant Eigenvalue ◽

Eigenvalues And Eigenvectors ◽

Correct Model ◽

Geometric Primitives

While repairing industrial machines or vehicles, recognition of components is a critical and time-consuming task for a human. In this paper, we propose to automatize this task. We start with a Principal Component Analysis (PCA), which fits the scanned point cloud with an ellipsoid by computing the eigenvalues and eigenvectors of a 3-by-3 covariant matrix. In case there is a dominant eigenvalue, the point cloud is decomposed into two clusters to which the PCA is applied recursively. In case the matching is not unique, we continue to distinguish among several candidates. We decompose the point cloud into planar and cylindrical primitives and assign mutual features such as distance or angle to them. Finally, we refine the matching by comparing the matrices of mutual features of the primitives. This is a more computationally demanding but very robust method. We demonstrate the efficiency and robustness of the proposed methodology on a collection of 29 real scans and a database of 389 STL (Standard Triangle Language) models. As many as 27 scans are uniquely matched to their counterparts from the database, while in the remaining two cases, there is only one additional candidate besides the correct model. The overall computational time is about 10 min in MATLAB.

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

Materials ◽

10.3390/ma14061563 ◽

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.

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Emergency Landing Spot Detection Algorithm for Unmanned Aerial Vehicles

Remote Sensing ◽

10.3390/rs13101930 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1930

Author(s):

Gabriel Loureiro ◽

André Dias ◽

Alfredo Martins ◽

José Almeida

Keyword(s):

Point Cloud ◽

Principal Component ◽

Detection Algorithm ◽

Cloud Data ◽

Safety Issues ◽

Spot Detection ◽

Cloud Clusters ◽

Aerial Vehicle ◽

Incoming Light ◽

Landing Spot

The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area’s roughness, and the spot’s slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.

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Multi-View Laser Point Cloud Global Registration for a Single Object

Sensors ◽

10.3390/s18113729 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3729 ◽

Cited By ~ 3

Author(s):

Shuai Wang ◽

Hua-Yan Sun ◽

Hui-Chao Guo ◽

Lin Du ◽

Tian-Jian Liu

Keyword(s):

Spatial Distribution ◽

Objective Function ◽

Point Cloud ◽

Point Clouds ◽

Low Rank ◽

Single Object ◽

Loop Closure Detection ◽

Global Registration ◽

Distribution Features ◽

Better Than

Global registration is an important step in the three-dimensional reconstruction of multi-view laser point clouds for moving objects, but the severe noise, density variation, and overlap ratio between multi-view laser point clouds present significant challenges to global registration. In this paper, a multi-view laser point cloud global registration method based on low-rank sparse decomposition is proposed. Firstly, the spatial distribution features of point clouds were extracted by spatial rasterization to realize loop-closure detection, and the corresponding weight matrix was established according to the similarities of spatial distribution features. The accuracy of adjacent registration transformation was evaluated, and the robustness of low-rank sparse matrix decomposition was enhanced. Then, the objective function that satisfies the global optimization condition was constructed, which prevented the solution space compression generated by the column-orthogonal hypothesis of the matrix. The objective function was solved by the Augmented Lagrange method, and the iterative termination condition was designed according to the prior conditions of single-object global registration. The simulation analysis shows that the proposed method was robust with a wide range of parameters, and the accuracy of loop-closure detection was over 90%. When the pairwise registration error was below 0.1 rad, the proposed method performed better than the three compared methods, and the global registration accuracy was better than 0.05 rad. Finally, the global registration results of real point cloud experiments further proved the validity and stability of the proposed method.

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