Rapid Method for Point Clouds Registration Based on Reference Points

2011 ◽  
Vol 48-49 ◽  
pp. 873-876
Author(s):  
Hou Jun Yang ◽  
Wei Zhong Zhang ◽  
Xiao Lie Liu
Keyword(s):  
Rapid Method ◽  
Point Clouds ◽  
Reference Points ◽  
Experimental Result ◽  
Model Parameters ◽  
Registration Accuracy ◽  
Computation Efficiency ◽  
Dense Point ◽  
Coarse Registration ◽  
Fine Registration

Incremental point clouds registration is studied in this paper. A rapid method for point clouds registration based on reference points is proposed, which consists of the coarse registration and fine registration. Firstly, a set of reference points is applied as an assistant utility to measure the object. The transformation parameters are estimated by using the reference points only for coarse registration, and then dense point clouds data will be transformed to the same coordinate system. Secondly, taking the coarse registration results as the initial value, the improved Interactive Closest Point (ICP) algorithm is used in fine registration the original corresponding points are established rapidly by using the k-d tree searching algorithm. Finally, Preview Model Parameters Evaluation Random Sample Consensus (PERANSAC) algorithm is utilized to remove outliers. The experimental result shows that this method in finding original corresponding points can greatly improve the computation efficiency and also improve the registration accuracy.

scholarly journals Super Edge 4-Points Congruent Sets-Based Point Cloud Global Registration

2021 ◽  
Vol 13 (16) ◽  
pp. 3210
Author(s):  
Shikun Li ◽  
Ruodan Lu ◽  
Jianya Liu ◽  
Liang Guo
Keyword(s):  
Three Dimensional ◽  
Initial Point ◽  
Point Clouds ◽  
Search Space ◽  
Frame Rate ◽  
Registration Method ◽  
Dense Point ◽  
Coarse Registration ◽  
Fine Registration ◽  
Original Point

With the acceleration in three-dimensional (3D) high-frame-rate sensing technologies, dense point clouds collected from multiple standpoints pose a great challenge for the accuracy and efficiency of registration. The combination of coarse registration and fine registration has been extensively promoted. Unlike the requirement of small movements between scan pairs in fine registration, coarse registration can match scans with arbitrary initial poses. The state-of-the-art coarse methods, Super 4-Points Congruent Sets algorithm based on the 4-Points Congruent Sets, improves the speed of registration to a linear order via smart indexing. However, the lack of reduction in the scale of original point clouds limits the application. Besides, the coplanarity of registration bases prevents further reduction of search space. This paper proposes a novel registration method called the Super Edge 4-Points Congruent Sets to address the above problems. The proposed algorithm follows a three-step procedure, including boundary segmentation, overlapping regions extraction, and bases selection. Firstly, an improved method based on vector angle is used to segment the original point clouds aiming to thin out the scale of the initial point clouds. Furthermore, overlapping regions extraction is executed to find out the overlapping regions on the contour. Finally, the proposed method selects registration bases conforming to the distance constraints from the candidate set without consideration about coplanarity. Experiments on various datasets with different characteristics have demonstrated that the average time complexity of the proposed algorithm is improved by 89.76%, and the accuracy is improved by 5 mm on average than the Super 4-Points Congruent Sets algorithm. More encouragingly, the experimental results show that the proposed algorithm can be applied to various restrictive cases, such as few overlapping regions and massive noise. Therefore, the algorithm proposed in this paper is a faster and more robust method than Super 4-Points Congruent Sets under the guarantee of the promised quality.

scholarly journals COARSE POINT CLOUD REGISTRATION BY EGI MATCHING OF VOXEL CLUSTERS

2016 ◽  
Vol III-5 ◽  
pp. 97-103 ◽  
Author(s):  
Jinhu Wang ◽  
Roderik Lindenbergh ◽  
Yueqian Shen ◽  
Massimo Menenti
Keyword(s):  
Laser Scanning ◽  
Principal Component ◽  
Point Clouds ◽  
Data Sampling ◽  
Surface Geometry ◽  
Registration Method ◽  
Input Point ◽  
Coarse Registration ◽  
Point Distance ◽  
Fine Registration

Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6 mm and 9.5 mm respectively, which are adequate for fine registration.

Verfahren zur Registrierung von Laserscannerdaten in Waldbeständen | Methods for registration laser scanner point clouds in forest stands

2011 ◽  
Vol 162 (6) ◽  
pp. 178-185 ◽  
Author(s):  
Anne Bienert ◽  
Katharina Pech ◽  
Hans-Gerd Maas
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Forest Stand ◽  
Geometric Transformation ◽  
Complex Object ◽  
Tree Detection ◽  
Airborne Laser ◽  
Coarse Registration ◽  
Fine Registration

Laser scanning is a fast and efficient 3-D measurement technique to capture surface points describing the geometry of a complex object in an accurate and reliable way. Besides airborne laser scanning, terrestrial laser scanning finds growing interest for forestry applications. These two different recording platforms show large differences in resolution, recording area and scan viewing direction. Using both datasets for a combined point cloud analysis may yield advantages because of their largely complementary information. In this paper, methods will be presented to automatically register airborne and terrestrial laser scanner point clouds of a forest stand. In a first step, tree detection is performed in both datasets in an automatic manner. In a second step, corresponding tree positions are determined using RANSAC. Finally, the geometric transformation is performed, divided in a coarse and fine registration. After a coarse registration, the fine registration is done in an iterative manner (ICP) using the point clouds itself. The methods are tested and validated with a dataset of a forest stand. The presented registration results provide accuracies which fulfill the forestry requirements.

scholarly journals Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images

2020 ◽  
Vol 10 (4) ◽  
pp. 1275
Author(s):  
Zizhuang Wei ◽  
Yao Wang ◽  
Hongwei Yi ◽  
Yisong Chen ◽  
Guoping Wang
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Aerial Images ◽  
Experimental Result ◽  
Fusion Procedure ◽  
3D Geometry ◽  
Dense Point ◽  
Semantic Point ◽  
Projection Error

Semantic modeling is a challenging task that has received widespread attention in recent years. With the help of mini Unmanned Aerial Vehicles (UAVs), multi-view high-resolution aerial images of large-scale scenes can be conveniently collected. In this paper, we propose a semantic Multi-View Stereo (MVS) method to reconstruct 3D semantic models from 2D images. Firstly, 2D semantic probability distribution is obtained by Convolutional Neural Network (CNN). Secondly, the calibrated cameras poses are determined by Structure from Motion (SfM), while the depth maps are estimated by learning MVS. Combining 2D segmentation and 3D geometry information, dense point clouds with semantic labels are generated by a probability-based semantic fusion method. In the final stage, the coarse 3D semantic point cloud is optimized by both local and global refinements. By making full use of the multi-view consistency, the proposed method efficiently produces a fine-level 3D semantic point cloud. The experimental result evaluated by re-projection maps achieves 88.4% Pixel Accuracy on the Urban Drone Dataset (UDD). In conclusion, our graph-based semantic fusion procedure and refinement based on local and global information can suppress and reduce the re-projection error.

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.

scholarly journals INFLUENCE OF CO-ALIGNMENT PROCEDURES ON THE CO-REGISTRATION ACCURACY OF MULTI-EPOCH SFM POINTS CLOUDS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 231-238
Author(s):  
M. Saponaro ◽  
A. Capolupo ◽  
G. Caporusso ◽  
E. Tarantino
Keyword(s):  
High Performance ◽  
Research Work ◽  
Point Clouds ◽  
Scalar Fields ◽  
Registration Accuracy ◽  
3D Point Clouds ◽  
Digital Elevation ◽  
Remotely Piloted Aircraft ◽  
Dense Point ◽  
Traditional Approaches

Abstract. The well-established spread of Remotely Piloted Aircraft Systems (RPAS) as high-performance devices in the acquisition of huge datasets has found a fertile field in the geomorphological change detection in coastal areas. The ability to retrieve image datasets with multi-epoch frequency makes them effectively incisive for planning ongoing monitoring. Considering the wide accessibility to multiple Structure-from-Motion (SfM)-3D point clouds, it follows the need for their proper management to identify a profitable co-registration approach valid for a proper comparison among them. In most cases the co-registration is inherited from the same georeferencing; in other cases, it can be done manually. Unfortunately, these methodologies are time consuming and often do not properly consider geometric errors on the models. The purpose of this research work was therefore to analyse an alternative method such as the co-alignment of sparse point clouds. Given the independently or co-aligned processed multi-epoch datasets, mean errors (ME) and root-mean-square error (RMSE) on Check Points (CPs) were evaluated by adopting different georeferencing strategies. Lastly, by first generating dense point clouds and from these the Digital Elevation Models (DEMs), scalar fields regarding DEM of Differences (DoD) were computed and allowed to localize any uncertainties δz among the estimated elevations. A cloud-to-cloud comparison was obtained using the M3C2 algorithm to extrapolate systematic georeferencing errors and the local deviation between models, an evidence of how the method can affect the detectable changes. The co-alignment methodology showed encouraging results proving to be a valid alternative to more traditional approaches.

scholarly journals COARSE POINT CLOUD REGISTRATION BY EGI MATCHING OF VOXEL CLUSTERS

2016 ◽  
Vol III-5 ◽  
pp. 97-103
Author(s):  
Jinhu Wang ◽  
Roderik Lindenbergh ◽  
Yueqian Shen ◽  
Massimo Menenti
Keyword(s):  
Laser Scanning ◽  
Principal Component ◽  
Point Clouds ◽  
Data Sampling ◽  
Surface Geometry ◽  
Registration Method ◽  
Input Point ◽  
Coarse Registration ◽  
Point Distance ◽  
Fine Registration

Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6 mm and 9.5 mm respectively, which are adequate for fine registration.

scholarly journals AUTOMATIC REGISTRATION OF APPROXIMATELY LEVELED POINT CLOUDS OF URBAN SCENES

2015 ◽  
Vol II-3/W5 ◽  
pp. 145-150 ◽  
Author(s):  
A. Moussa ◽  
N. Elsheimy
Keyword(s):  
Full Range ◽  
Point Clouds ◽  
Search Space ◽  
Search Problem ◽  
Data Sets ◽  
Urban Scenes ◽  
Small Height ◽  
Coarse Registration ◽  
Fully Automatic ◽  
Fine Registration

Registration of point clouds is a necessary step to obtain a complete overview of scanned objects of interest. The majority of the current registration approaches target the general case where a full range of the registration parameters search space is assumed and searched. It is very common in urban objects scanning to have leveled point clouds with small roll and pitch angles and with also a small height differences. For such scenarios the registration search problem can be handled faster to obtain a coarse registration of two point clouds. In this paper, a fully automatic approach is proposed for registration of approximately leveled point clouds. The proposed approach estimates a coarse registration based on three registration parameters and then conducts a fine registration step using iterative closest point approach. The approach has been tested on three data sets of different areas and the achieved registration results validate the significance of the proposed approach.

scholarly journals ESTIMATES OF CHANGES IN THE RELIEF OF THE COASTAL ZONE DURING ABRASION USING REMOTE SENSING DATA: METHODOLOGICAL APPROACHES AND PRELIMINARY RESULTS

2020 ◽  
Vol 2 (5) ◽  
pp. 124-130
Author(s):  
A.S. Travnikova ◽  
◽  
S.A. Misirov ◽  
S.V. Berdnikov ◽  
L.M. Mestetskiy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Experimental Studies ◽  
Three Dimensional ◽  
Remote Sensing Data ◽  
Point Clouds ◽  
Test Site ◽  
3D Models ◽  
Reference Points ◽  
Instrumental Method ◽  
Dense Point

The article offers a method for assessing changes in the relief of the Azov sea coastline and the localization of areas of erosion using discrete surface models obtained from remote sensing of the Earth using unmanned aerial vehicles (UAVs). This problem arises because of the need to monitor the dynamics of the coastline: due to the activation of various natural and man-made processes, there is an intensive destruction of the shores of the seas of Russia. Existing modern methods of land topographic survey do not allow you to quickly get information about changes in the state of the coastline or are expensive, and the large extent of the zone subject to erosion makes the traditional instrumental approach of measuring at reference points very labor-intensive. Also, the data obtained by the instrumental method reflects the problem point-by-point, rather than along the entire coastline. In this paper, we developed an algorithm and software for building a three-dimensional terrain model (using Delaunay triangulation) based on the so-called “dense point cloud” obtained when shooting terrain from an unmanned aerial vehicle (UAV). we proposed and programmatically implemented an algorithm for comparing (subtracting) two 3D models based on surveys performed by the same camera, but at different times of the day, in different seasons, and at different heights with an interval of 2 years, to identify significant changes in terrain in the area of the coastal slope, caused by abrasive and collapse processes. Experimental studies of the developed approach were conducted at the test site (500 by 300 m in size) on the southern shore of the Taganrog Bay. As a result of the considered experimental studies of comparing two 3D terrain models based on dense point clouds, additional working hypotheses (steps) that need to be solved were formulated to identify significant differences due to the destruction of the coast

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