scholarly journals Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1520
Author(s):  
Emon Kumar Dey ◽  
Fayez Tarsha Kurdi ◽  
Mohammad Awrangjeb ◽  
Bela Stantic
Keyword(s):  
Boundary Point ◽  
Point Cloud ◽  
Ground Truth ◽  
Normal Vector ◽  
Accuracy Evaluation ◽  
Point Cloud Data ◽  
Ground Truth Data ◽  
Heterogeneous Distribution ◽  
Cloud Data ◽  
Input Point

Existing approaches that extract buildings from point cloud data do not select the appropriate neighbourhood for estimation of normals on individual points. However, the success of these approaches depends on correct estimation of the normal vector. In most cases, a fixed neighbourhood is selected without considering the geometric structure of the object and the distribution of the input point cloud. Thus, considering the object structure and the heterogeneous distribution of the point cloud, this paper proposes a new effective approach for selecting a minimal neighbourhood, which can vary for each input point. For each point, a minimal number of neighbouring points are iteratively selected. At each iteration, based on the calculated standard deviation from a fitted 3D line to the selected points, a decision is made adaptively about the neighbourhood. The selected minimal neighbouring points make the calculation of the normal vector accurate. The direction of the normal vector is then used to calculate the inside fold feature points. In addition, the Euclidean distance from a point to the calculated mean of its neighbouring points is used to make a decision about the boundary point. In the context of the accuracy evaluation, the experimental results confirm the competitive performance of the proposed approach of neighbourhood selection over the state-of-the-art methods. Based on our generated ground truth data, the proposed fold and boundary point extraction techniques show more than 90% F1-scores.

scholarly journals Algorithm for Generating 3D Geometric Representation Based on Indoor Point Cloud Data

2020 ◽  
Vol 10 (22) ◽  
pp. 8073
Author(s):  
Min Woo Ryu ◽  
Sang Min Oh ◽  
Min Ju Kim ◽  
Hun Hee Cho ◽  
Chang Baek Son ◽  
...  
Keyword(s):  
Point Cloud ◽  
Indoor Environment ◽  
Three Dimensional ◽  
Satellite System ◽  
Normal Vector ◽  
Geometric Representation ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Data Process ◽  
Global Navigation Satellite

This study proposes a new method to generate a three-dimensional (3D) geometric representation of an indoor environment by refining and processing an indoor point cloud data (PCD) captured through backpack laser scanners. The proposed algorithm comprises two parts to generate the 3D geometric representation: data refinement and data processing. In the refinement section, the inputted indoor PCD are roughly segmented by applying random sample consensus (RANSAC) to raw data based on an estimated normal vector. Next, the 3D geometric representation is generated by calculating and separating tangent points on segmented PCD. This study proposes a robust algorithm that utilizes the topological feature of the indoor PCD created by a hierarchical data process. The algorithm minimizes the size and the uncertainty of raw PCD caused by the absence of a global navigation satellite system and equipment errors. The result of this study shows that the indoor environment can be converted into 3D geometric representation by applying the proposed algorithm to the indoor PCD.

scholarly journals A posture measurement approach for an articulated manipulator by RGB-D cameras

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141983813
Author(s):  
Haobin Shi ◽  
Meng Xu ◽  
Kao-Shing Hwang ◽  
Chia-Hung Hung
Keyword(s):  
Point Cloud ◽  
Original Method ◽  
Consensus Method ◽  
Average Error ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Sample Standard Deviation ◽  
Cylindrical Model ◽  
Cloud Data ◽  
Posture Measurement

The objective of this article aims at the safety problems where robots and operators are highly coupled in a working space. A method to model an articulated robot manipulator by cylindrical geometries based on partial cloud points is proposed in this article. Firstly, images with point cloud data containing the posture of a robot with five resolution links are captured by a pair of RGB-D cameras. Secondly, the process of point cloud clustering and Gaussian noise filtering is applied to the images to separate the point cloud data of three links from the combined images. Thirdly, an ideal cylindrical model fits the processed point cloud data are segmented by the random sample consensus method such that three joint angles corresponding to three major links are computed. The original method for calculating the normal vector of point cloud data is the cylindrical model segmentation method, but the accuracy of posture measurement is low when the point cloud data is incomplete. To solve this problem, a principal axis compensation method is proposed, which is not affected by number of point cloud cluster data. The original method and the proposed method are used to estimate the three joint angular of the manipulator system in experiments. Experimental results show that the average error is reduced by 27.97%, and the sample standard deviation of the error is reduced by 54.21% compared with the original method for posture measurement. The proposed method is 0.971 piece/s slower than the original method in terms of the image processing velocity. But the proposed method is still feasible, and the purpose of posture measurement is achieved.

Research on Earthwork Calculating Methods Based on 3D Laser Scanning Technology

2012 ◽  
Vol 204-208 ◽  
pp. 618-621
Author(s):  
Bao Xing Zhou ◽  
Jian Ping Yue ◽  
Jin Li
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Accuracy Evaluation ◽  
Point Cloud Data ◽  
3D Laser Scanning ◽  
Cloud Data ◽  
Calculating Method ◽  
Validation Experiment ◽  
High Resolution Reconstruction

Terrestrial laser scanner (TLS) can provide the measurement of a large number of physical points distributed on the observed surface. A fast earthwork calculating method is proposed based on the redundant number of acquired points, which leads to a very accurate and high resolution reconstruction of the observed surfaces. This paper describes the three main steps of the method, namely the acquisition of the earthwork data based on TLS, the pre-processing of point cloud data, the earthwork calculation and accuracy evaluation based on point cloud data. Furthermore, it illustrates the performance of the proposed method with a validation experiment.

scholarly journals THE RESEARCH ON ACCURACY EVALUATION METHOD OF THE TOMOGRAPHIC SAR THREE-DIMENSIONAL RECONSTRUCTION OF URBAN BUILDING BASED ON TERRESTRIAL LIDAR POINT CLOUD

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 617-621
Author(s):  
L. Li ◽  
L. Pang ◽  
X. D. Zhang ◽  
H. Liu
Keyword(s):  
Point Cloud ◽  
Evaluation Method ◽  
Three Dimensional ◽  
Accuracy Evaluation ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Terrestrial Lidar ◽  
Cloud Data ◽  
Dimensional Precision ◽  
Urban Buildings

Muti-baseLine SAR tomography can be used on 3D reconstruction of urban building based on SAR images acquired. In the near future, it is expected to become an important technical tool for urban multi-dimensional precision monitoring. For the moment，There is no effective method to verify the accuracy of tomographic SAR 3D point cloud of urban buildings. In this paper, a new method based on terrestrial Lidar 3D point cloud data to verify the accuracy of the tomographic SAR 3D point cloud data is proposed, 3D point cloud of two can be segmented into different facadeds. Then facet boundary extraction is carried out one by one, to evaluate the accuracy of tomographic SAR 3D point cloud of urban buildings. The experience select data of Pangu Plaza to analyze and compare, the result of experience show that the proposed method that evaluating the accuracy of tomographic SAR 3D point clou of urban building based on lidar 3D point cloud is validity and applicability

scholarly journals Point Cloud Classification Algorithm Based on the Fusion of the Local Binary Pattern Features and Structural Features of Voxels

2021 ◽  
Vol 13 (16) ◽  
pp. 3156
Author(s):  
Yong Li ◽  
Yinzheng Luo ◽  
Xia Gu ◽  
Dong Chen ◽  
Fang Gao ◽  
...  
Keyword(s):  
Point Cloud ◽  
Local Binary Pattern ◽  
Random Forest Classifier ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Color Information ◽  
Cloud Data ◽  
Cloud Classification ◽  
Local Difference ◽  
Point Cloud Classification

Point cloud classification is a key technology for point cloud applications and point cloud feature extraction is a key step towards achieving point cloud classification. Although there are many point cloud feature extraction and classification methods, and the acquisition of colored point cloud data has become easier in recent years, most point cloud processing algorithms do not consider the color information associated with the point cloud or do not make full use of the color information. Therefore, we propose a voxel-based local feature descriptor according to the voxel-based local binary pattern (VLBP) and fuses point cloud RGB information and geometric structure features using a random forest classifier to build a color point cloud classification algorithm. The proposed algorithm voxelizes the point cloud; divides the neighborhood of the center point into cubes (i.e., multiple adjacent sub-voxels); compares the gray information of the voxel center and adjacent sub-voxels; performs voxel global thresholding to convert it into a binary code; and uses a local difference sign–magnitude transform (LDSMT) to decompose the local difference of an entire voxel into two complementary components of sign and magnitude. Then, the VLBP feature of each point is extracted. To obtain more structural information about the point cloud, the proposed method extracts the normal vector of each point and the corresponding fast point feature histogram (FPFH) based on the normal vector. Finally, the geometric mechanism features (normal vector and FPFH) and color features (RGB and VLBP features) of the point cloud are fused, and a random forest classifier is used to classify the color laser point cloud. The experimental results show that the proposed algorithm can achieve effective point cloud classification for point cloud data from different indoor and outdoor scenes, and the proposed VLBP features can improve the accuracy of point cloud classification.

scholarly journals A NOVEL TIE POINT BASED STRATEGY FOR POINT CLOUD AND IMAGERY DATA FINE REGISTRATION

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 331-334
Author(s):  
M. Eslami ◽  
M. Saadatseresht
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Object Space ◽  
Cloud Data ◽  
Conditional Equation ◽  
Novel Strategy ◽  
Orientation Parameters ◽  
Fine Registration

Abstract. Laser scanner generated point cloud and photogrammetric imagery are complimentary data for many applications and services. Misalignment between imagery and point cloud data is a common problem, which causes to inaccurate products and procedures. In this paper, a novel strategy is proposed for coarse to fine registration between close-range imagery and terrestrial laser scanner point cloud data. In such a case, tie points are extracted and matched from photogrammetric imagery and preprocessing is applied on generated tie points to eliminate non-robust ones. At that time, for every tie point, two neighbor pixels are selected and matched in all overlapped images. After that, coarse interior orientation parameters (IOPs) and exterior orientation parameters (EOPs) of the images are employed to reconstruct object space points of the tie point and its two neighbor pixels. Then, corresponding nearest points to the object space photogrammetric points are estimated in the point cloud data. Estimated three point cloud points are used to calculate a plane and its normal vector. Theoretically, every object space tie point should be located on the aforementioned plane, which is used as conditional equation alongside the collinearity equation to fine register the photogrammetric imagery network. Attained root mean square error (RMSE) results on check points, have been shown less than 2.3 pixels, which shows the accuracy, completeness and robustness of the proposed method.

scholarly journals PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3703
Author(s):  
Dongyang Cheng ◽  
Dangjun Zhao ◽  
Junchao Zhang ◽  
Caisheng Wei ◽  
Di Tian
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Noise Removal ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Optimization Strategy ◽  
K Nearest Neighbor ◽  
Cloud Data ◽  
Run Speed

Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method. The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids. Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to obtain the true ground data by using the normal vector as a distinctive feature. To improve the performance of noise removal, we propose an elaborate K nearest neighbor (KNN)-based segmentation method via an optimization strategy. Finally, the denoised data of the wall and ground are spliced for further 3D reconstruction. The experimental results show that the proposed method is efficient at noise removal and is superior to several traditional methods in terms of both denoising performance and run speed.

scholarly journals Efficient point cloud data processing in shipbuilding: Reformative component extraction method and registration method

2014 ◽  
Vol 1 (3) ◽  
pp. 202-212 ◽  
Author(s):  
Jingyu Sun ◽  
Kazuo Hiekata ◽  
Hiroyuki Yamato ◽  
Norito Nakagaki ◽  
Akiyoshi Sugawara
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Accuracy Evaluation ◽  
Point Cloud Data ◽  
Continuous Part ◽  
Registration Method ◽  
Shipbuilding Industry ◽  
Seed Point ◽  
Cloud Data ◽  
Cloud Data Processing

Abstract To survive in the current shipbuilding industry, it is of vital importance for shipyards to have the ship components' accuracy evaluated efficiently during most of the manufacturing steps. Evaluating components' accuracy by comparing each component's point cloud data scanned by laser scanners and the ship's design data formatted in CAD cannot be processed efficiently when (1) extract components from point cloud data include irregular obstacles endogenously, or when (2) registration of the two data sets have no clear direction setting. This paper presents reformative point cloud data processing methods to solve these problems. K-d tree construction of the point cloud data fastens a neighbor searching of each point. Region growing method performed on the neighbor points of the seed point extracts the continuous part of the component, while curved surface fitting and B-spline curved line fitting at the edge of the continuous part recognize the neighbor domains of the same component divided by obstacles' shadows. The ICP (Iterative Closest Point) algorithm conducts a registration of the two sets of data after the proper registration's direction is decided by principal component analysis. By experiments conducted at the shipyard, 200 curved shell plates are extracted from the scanned point cloud data, and registrations are conducted between them and the designed CAD data using the proposed methods for an accuracy evaluation. Results show that the methods proposed in this paper support the accuracy evaluation targeted point cloud data processing efficiently in practice.

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