scholarly journals A GLOBAL REGISTRATION ALGORITHM OF THE SINGLE-CLOSED RING MULTI-STATIONS POINT CLOUD

2018 ◽  
Vol XLII-3 ◽  
pp. 2093-2100
Author(s):  
R. Yang ◽  
L. Pan ◽  
Z. Xiang ◽  
H. Zeng
Keyword(s):  
Point Cloud ◽  
Simulation Experiment ◽  
Rotation Matrix ◽  
Registration Method ◽  
Registration Algorithm ◽  
Global Registration ◽  
Closed Ring ◽  
Definition Of ◽  
Station Point ◽  
Implementation Steps

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.

scholarly journals HEALPix-IA: A Global Registration Algorithm for Initial Alignment

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 427 ◽  
Author(s):  
Yongzhuo Gao ◽  
Zhijiang Du ◽  
Wei Xu ◽  
Mingyang Li ◽  
Wei Dong
Keyword(s):  
Point Cloud ◽  
Cross Correlation ◽  
Correlation Method ◽  
Real Data ◽  
Initial Guess ◽  
Convergence Time ◽  
Image Method ◽  
Initial Alignment ◽  
Registration Method ◽  
Global Registration

Methods of point cloud registration based on ICP algorithm are always limited by convergence rate, which is related to initial guess. A good initial alignment transformation can sharply reduce convergence time and raise efficiency. In this paper, we propose a global registration method to estimate the initial alignment transformation based on HEALPix (Hierarchical Equal Area isoLatitude Pixelation of a sphere), an algorithm for spherical projections. We adopt EGI (Extended Gaussian Image) method to map the normals of the point cloud and estimate the transformation with optimized point correspondence. Cross-correlation method is used to search the best alignment results in consideration of the accuracy and robustness of the algorithm. The efficiency and accuracy of the proposed algorithm were verified with created model and real data from various sensors in comparison with similar methods.

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

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.

A Structure-Based Registration Method for Terrestrial Laser Scanning Data

2015 ◽  
Vol 734 ◽  
pp. 608-616
Author(s):  
Jun Cheng ◽  
Ming Cheng ◽  
Yan Bin Lin ◽  
Cheng Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Optimization Problem ◽  
Terrestrial Laser Scanning ◽  
Computation Method ◽  
Registration Method ◽  
Global Registration ◽  
Novel Structure ◽  
Scene Structure ◽  
Evolution Computation

This paper presents a novel structure-based registration method for terrestrial laser scanning (TLS) data. The line support region (LSR), which fits the 3D line segment, is adopted to describe the scene structure and reduce geometric complexity. Then we employ an evolution computation method to solve the optimization problem of global registration. Our method can be further enhanced by iterative closest points (ICP) or other local registration methods. We demonstrate the robustness of our algorithm on several point cloud sets with varying extent of overlap and degree of noise.

scholarly journals Voxel Grid-Based Fast Registration of Terrestrial Point Cloud

2021 ◽  
Vol 13 (10) ◽  
pp. 1905
Author(s):  
Biao Xiong ◽  
Weize Jiang ◽  
Dengke Li ◽  
Man Qi
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Index Structure ◽  
Registration Method ◽  
Global Registration ◽  
Voxel Grid ◽  
Selection Of ◽  
Indoor And Outdoor ◽  
Registration Efficiency

Terrestrial laser scanning (TLS) is an important part of urban reconstruction and terrain surveying. In TLS applications, 4-point congruent set (4PCS) technology is widely used for the global registration of point clouds. However, TLS point clouds usually enjoy enormous data and uneven density. Obtaining the congruent set of tuples in a large point cloud scene can be challenging. To address this concern, we propose a registration method based on the voxel grid of the point cloud in this paper. First, we establish a voxel grid structure and index structure for the point cloud and eliminate uneven point cloud density. Then, based on the point cloud distribution in the voxel grid, keypoints are calculated to represent the entire point cloud. Fast query of voxel grids is used to restrict the selection of calculation points and filter out 4-point tuples on the same surface to reduce ambiguity in building registration. Finally, the voxel grid is used in our proposed approach to perform random queries of the array. Using different indoor and outdoor data to compare our proposed approach with other 4-point congruent set methods, according to the experimental results, in terms of registration efficiency, the proposed method is more than 50% higher than K4PCS and 78% higher than Super4PCS.

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 Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

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.

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