scholarly journals Efficient Coarse Registration of Pairwise TLS Point Clouds Using Ortho Projected Feature Images

2020 ◽  
Vol 9 (4) ◽  
pp. 255
Author(s):  
Hua Liu ◽  
Xiaoming Zhang ◽  
Yuancheng Xu ◽  
Xiaoyong Chen
Keyword(s):  
Parameter Estimation ◽  
Degrees Of Freedom ◽  
High Efficiency ◽  
Point Clouds ◽  
Azimuth Angle ◽  
Translation Vector ◽  
Scale Invariant ◽  
Registration Method ◽  
Coarse Registration ◽  
Vertical Translation

The degree of automation and efficiency are among the most important factors that influence the availability of Terrestrial light detection and ranging (LiDAR) Scanning (TLS) registration algorithms. This paper proposes an Ortho Projected Feature Images (OPFI) based 4 Degrees of Freedom (DOF) coarse registration method, which is fully automated and with high efficiency, for TLS point clouds acquired using leveled or inclination compensated LiDAR scanners. The proposed 4DOF registration algorithm decomposes the parameter estimation into two parts: (1) the parameter estimation of horizontal translation vector and azimuth angle; and (2) the parameter estimation of the vertical translation vector. The parameter estimation of the horizontal translation vector and the azimuth angle is achieved by ortho projecting the TLS point clouds into feature images and registering the ortho projected feature images by Scale Invariant Feature Transform (SIFT) key points and descriptors. The vertical translation vector is estimated using the height difference of source points and target points in the overlapping regions after horizontally aligned. Three real TLS datasets captured by the Riegl VZ-400 and the Trimble SX10 and one simulated dataset were used to validate the proposed method. The proposed method was compared with four state-of-the-art 4DOF registration methods. The experimental results showed that: (1) the accuracy of the proposed coarse registration method ranges from 0.02 m to 0.07 m in horizontal and 0.01 m to 0.02 m in elevation, which is at centimeter-level and sufficient for fine registration; and (2) as many as 120 million points can be registered in less than 50 s, which is much faster than the compared methods.

ADAPTION OF INVARIANT FEATURES IN IMAGE FOR POINT CLOUDS REGISTRATION

2015 ◽  
Vol 75 (10) ◽  
Author(s):  
Mohd Azwan Abbas ◽  
Halim Setan ◽  
Zulkepli Majid ◽  
Albert K. Chong ◽  
Lau Chong Luh ◽  
...  
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Scale Invariant ◽  
Registration Method ◽  
Flat Surfaces ◽  
Invariant Features ◽  
Limited Class ◽  
Coarse Registration ◽  
Straight Lines ◽  
Scale Invariant Feature

Currently, coarse registration methods for scanner are required heavy operator intervention either before or after scanning process. There also have an automatic registration method but only applicable to a limited class of objects (e.g. straight lines and flat surfaces). This study is devoted to a search of a computationally feasible automatic coarse registration method with a broad range of applicability. Nowadays, most laser scanner systems are supplied with a camera, such that the scanned data can also be photographed. The proposed approach will exploit the invariant features detected from image to associate point cloud registration. Three types of detectors are included: scale invariant feature transform (SIFT), 2) Harris affine, and 3) maximally stable extremal regions (MSER). All detected features will transform into the laser scanner coordinate system, and their performance is measured based on the number of corresponding points. Several objects with different observation techniques were performed to evaluate the capability of proposed approach and also to evaluate the performance of selected detectors.  

scholarly journals Pairwise Coarse Registration of Indoor Point Clouds Using 2D Line Features

2021 ◽  
Vol 10 (1) ◽  
pp. 26
Author(s):  
Zhen Li ◽  
Xiaoming Zhang ◽  
Junxiang Tan ◽  
Hua Liu
Keyword(s):  
Degrees Of Freedom ◽  
Point Clouds ◽  
Vertical Alignment ◽  
Self Similarity ◽  
Registration Method ◽  
Terrestrial Lidar ◽  
Horizontal Alignment ◽  
Coarse Registration ◽  
Line Features ◽  
4 Degrees Of Freedom

Registration is essential for terrestrial LiDAR (light detection and ranging) scanning point clouds. The registration of indoor point clouds is especially challenging due to the occlusion and self-similarity of indoor structures. This paper proposes a 4 degrees of freedom (4DOF) coarse registration method that fully takes advantage of the knowledge that the equipment is levelled or the inclination compensated for by a tilt sensor in data acquisition. The method decomposes the 4DOF registration problem into two parts: (1) horizontal alignment using ortho-projected images and (2) vertical alignment. The ortho-projected images are generated using points between the floor and ceiling, and the horizontal alignment is achieved by the matching of the source and target ortho-projected images using the 2D line features detected from them. The vertical alignment is achieved by making the height of the floor and ceiling in the source and target points equivalent. Two datasets, one with five stations and the other with 20 stations, were used to evaluate the performance of the proposed method. The experimental results showed that the proposed method achieved 80% and 63% successful registration rates (SRRs) in a simple scene and a challenging scene, respectively. The SRR in the simple scene is only lower than that of the keypoint-based four-point congruent set (K4PCS) method. The SRR in the challenging scene is better than all five comparison methods. Even though the proposed method still has some limitations, the proposed method provides an alternative to solve the indoor point cloud registration problem.

Complex Image Registration Method Based on Correlative Coefficient Interpolation

2011 ◽  
Vol 65 ◽  
pp. 514-517
Author(s):  
Rui Niu ◽  
Xiao Tao Tang ◽  
Yu Wang
Keyword(s):  
Image Registration ◽  
High Efficiency ◽  
Interpolation Method ◽  
Interferometric Synthetic Aperture Radar ◽  
Registration Method ◽  
The Earth ◽  
Complex Image ◽  
Interferometric Phase ◽  
Coarse Registration ◽  
Coefficient Method

Interferometric Synthetic Aperture Radar is a kind of technology to acquire the DEM information on the surface of the earth. It is concerned and researched by all over the world. Complex image registration of high precision is the key step in InSAR data processing, its results directly influence on the quantity of interferometric phase , even to the DEM precision.This paper introduces the complex image registration plans which is used the correlative coefficient method to make the coarse registration, and is used the correlative coefficient interpolation method to make the high precise registration.The experiments with spaceborne and aeroplane InSAR data prove that this method is with feasibility, high efficiency and practicability.

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 RegARD: Symmetry-Based Coarse Registration of Smartphone’s Colorful Point Clouds with CAD Drawings for Low-Cost Digital Twin Buildings

2021 ◽  
Vol 13 (10) ◽  
pp. 1882
Author(s):  
Yijie Wu ◽  
Jianga Shang ◽  
Fan Xue
Keyword(s):  
Degrees Of Freedom ◽  
Low Cost ◽  
Point Clouds ◽  
Data Sets ◽  
Digital Twin ◽  
3D Point Clouds ◽  
Coarse Registration ◽  
Smartphone Sensors ◽  
Novel Method ◽  
Twin Buildings

Coarse registration of 3D point clouds plays an indispensable role for parametric, semantically rich, and realistic digital twin buildings (DTBs) in the practice of GIScience, manufacturing, robotics, architecture, engineering, and construction. However, the existing methods have prominently been challenged by (i) the high cost of data collection for numerous existing buildings and (ii) the computational complexity from self-similar layout patterns. This paper studies the registration of two low-cost data sets, i.e., colorful 3D point clouds captured by smartphones and 2D CAD drawings, for resolving the first challenge. We propose a novel method named `Registration based on Architectural Reflection Detection’ (RegARD) for transforming the self-symmetries in the second challenge from a barrier of coarse registration to a facilitator. First, RegARD detects the innate architectural reflection symmetries to constrain the rotations and reduce degrees of freedom. Then, a nonlinear optimization formulation together with advanced optimization algorithms can overcome the second challenge. As a result, high-quality coarse registration and subsequent low-cost DTBs can be created with semantic components and realistic appearances. Experiments showed that the proposed method outperformed existing methods considerably in both effectiveness and efficiency, i.e., 49.88% less error and 73.13% less time, on average. The RegARD presented in this paper first contributes to coarse registration theories and exploitation of symmetries and textures in 3D point clouds and 2D CAD drawings. For practitioners in the industries, RegARD offers a new automatic solution to utilize ubiquitous smartphone sensors for massive low-cost DTBs.

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 Attitude Mounting Misalignment Estimation Method for the Calibration of UAV LiDAR System by using a TIN-based Corresponding Model

2020 ◽  
Vol 1 (1) ◽  
pp. 21-27
Author(s):  
Daniel Dos Santos ◽  
Leonardo Filho ◽  
Paulo De Oliveira Jr ◽  
Henrique De Oliveira
Keyword(s):  
Autonomous Vehicle ◽  
Estimation Method ◽  
Point Clouds ◽  
Estimation Methods ◽  
Lidar System ◽  
Scale Invariant ◽  
Keypoint Matching ◽  
Coarse Registration ◽  
Triangular Irregular Network ◽  
Feature Transform

In traditional attitude mounting misalignment estimation methods for the calibration of unmanned autonomous vehicle (UAV) based light detection and ranging (LiDAR) system, signalized targets and iterative corresponding models are required, which makes it highly cost and computationally time-consuming. This paper presents an attitude mounting misalignment estimation (AMME) method for the calibration of UAV LiDAR system. The proposed method is divided into the coarse registration of LiDAR strips and the estimation of the attitude mounting misalignment. Firstly, 3D keypoints are extracted in the point clouds using the scale-invariant feature transform (SIFT) algorithm. Afterwards, the point feature transform (PFH) descriptor is used for 3D keypoint matching. Then, the coarse registration is executed. In the second part of the contribution, the systematic errors in the attitude mounting misalignment are estimated by incorporating the proposed triangular irregular network (TIN) corresponding model into the calibration modelling. Using the TIN-based corresponding model saves time and cost for AMME method. Furthermore, it provides two important effects: practical and computational, as no designed calibration boards, segmentation and iterative matching are needed. The performance of the proposed method is demonstrated under an UAV LiDAR data onboarded with lightweight navigation sensors. The experimental results show the efficacy of the method in comparison with a state-of-the-art method.

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.

Registration of 3D Point Cloud of Human Body Based on the Range Images and RGB Images

2015 ◽  
Vol 738-739 ◽  
pp. 656-661
Author(s):  
Yan Wan ◽  
Jun Lu ◽  
Ao Qiong Li
Keyword(s):  
Point Cloud ◽  
High Efficiency ◽  
Low Cost ◽  
Point Clouds ◽  
The Body ◽  
Background Information ◽  
Range Images ◽  
Scale Invariant ◽  
Rgb Images ◽  
Body Point

The existing 3D reconstruction techniques rarely can be easily used in people's daily life, and the traditional registration algorithms have the drawback of massive calculation. In this paper it presented a registration algorithm of body point cloud based on RGB images and Range images. First, it used kinect to obtain the RGB images and Range images from different perspectives. Then it extracted the pairs of 2D feature points on RGB images using scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) algorithm application for the coarse registration and used the improved iterative closest point (ICP) algorithm for the fine registration. Second, it eliminated the background information and the noise points of the model edges. Finally it completed the registration process. Experimental results show that the algorithm can accurately accomplish the body point clouds registration using the low-cost instrument and has a relatively high efficiency.

scholarly journals Robust Coarse-to-Fine Registration Scheme for Mobile Laser Scanner Point Clouds Using Multiscale Eigenvalue Statistic-Based Descriptor

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2431
Author(s):  
Yongjian Fu ◽  
Zongchun Li ◽  
Wenqi Wang ◽  
Hua He ◽  
Feng Xiong ◽  
...  
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Target Point ◽  
Registration Method ◽  
Geometric Consistency ◽  
Coarse Registration ◽  
Eigenvalue Statistic ◽  
Pairwise Registration ◽  
Coarse To Fine ◽  
Fine Registration

To overcome the drawbacks of pairwise registration for mobile laser scanner (MLS) point clouds, such as difficulty in searching the corresponding points and inaccuracy registration matrix, a robust coarse-to-fine registration method is proposed to align different frames of MLS point clouds into a common coordinate system. The method identifies the correct corresponding point pairs from the source and target point clouds, and then calculates the transform matrix. First, the performance of a multiscale eigenvalue statistic-based descriptor with different combinations of parameters is evaluated to identify the optimal combination. Second, based on the geometric distribution of points in the neighborhood of the keypoint, a weighted covariance matrix is constructed, by which the multiscale eigenvalues are calculated as the feature description language. Third, the corresponding points between the source and target point clouds are estimated in the feature space, and the incorrect ones are eliminated via a geometric consistency constraint. Finally, the estimated corresponding point pairs are used for coarse registration. The value of coarse registration is regarded as the initial value for the iterative closest point algorithm. Subsequently, the final fine registration result is obtained. The results of the registration experiments with Autonomous Systems Lab (ASL) Datasets show that the proposed method can accurately align MLS point clouds in different frames and outperform the comparative methods.

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