Impacts of Point Cloud Density Reductions on Extracting Road Geometric Features from mobile LiDAR data

2021 ◽  
Author(s):  
Suliman A Gargoum ◽  
Karim El-Basyouny
Keyword(s):  
Point Cloud ◽  
Original Data ◽  
Geometric Features ◽  
Cloud Data ◽  
Density Reduction ◽  
Sight Distance ◽  
Point Density ◽  
Slope Estimation ◽  
Low Sensitivity ◽  
Higher Sensitivity

Density of point cloud data varies depending on several different factors. Nonetheless, the extent to which changes in density could impact the accuracy of extracting roadway geometric features from the data is unknown. This paper investigates the impacts of point density reduction on the extraction of four critical geometric features. The density of the data was first reduced, and the different features were extracted at different levels of point density. The information obtained at lower point density was compared to what was obtained using the at 100% point density. It was found that clearance assessments and sight distance assessments had low sensitivity to reductions in point density (i.e. reducing the point density to as low as 10% of the original data (30ppm2 on the pavement surface) had minor impacts on the assessments). In contrast, for cross section slope estimation and curve attribute estimation higher sensitivity to point density was observed.

scholarly journals Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Mingjie Dong ◽  
Wusheng Chou ◽  
Bin Fang
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Point Cloud ◽  
Nuclear Power ◽  
A Priori ◽  
Geometric Features ◽  
Water Tank ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Reactor Pool

In order to localize the Remotely Operated Vehicle (ROV) accurately in the reactor pool of the nuclear power plant, an underwater matching correction navigation algorithm based on geometric features using sonar point cloud data is proposed. At first, an Extended Kalman Filter (EKF) is used to compensate the motion induced distortion after the preprocessing of the sonar point cloud data. Then, the adjacent scanning point cloud data are fitted to be four different straight lines using Hough Transform and least square method. After that, the adjacent straight line is modified based on geometric features to get a standard rectangle. Since the working environment of the ROV is a rectangular shape with all dimensions known, it is used as a priori map. The matching rectangle is then used to compare with the a priori map to calculate the accurate position and orientation of the ROV. The obtained result is then applied as the measurement for the second EKF to obtain better localization accuracy. Experiments have been conducted in man-made water tank which is similar to the reactor pool of the nuclear power plant, and the results successfully verify the effectiveness of the proposed algorithm.

scholarly journals Feature Extraction from 3D Point Cloud Data Based on Discrete Curves

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Yi An ◽  
Zhuohan Li ◽  
Cheng Shao
Keyword(s):  
Feature Extraction ◽  
Autonomous Navigation ◽  
Point Cloud ◽  
Geometric Features ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Principal Curvatures ◽  
Cloud Data ◽  
Discrete Curves ◽  
Threshold Setting

Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from[0,1], which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.

scholarly journals GEOMETRIC FEATURES AND THEIR RELEVANCE FOR 3D POINT CLOUD CLASSIFICATION

2017 ◽  
Vol IV-1/W1 ◽  
pp. 157-164 ◽  
Author(s):  
M. Weinmann ◽  
B. Jutzi ◽  
C. Mallet ◽  
M. Weinmann
Keyword(s):  
Influencing Factors ◽  
Point Cloud ◽  
Geometric Features ◽  
3D Point Cloud ◽  
Point Sampling ◽  
Cloud Data ◽  
Semantic Classes ◽  
Automatic Interpretation ◽  
Point Cloud Classification ◽  
The Impact

In this paper, we focus on the automatic interpretation of 3D point cloud data in terms of associating a class label to each 3D point. While much effort has recently been spent on this research topic, little attention has been paid to the influencing factors that affect the quality of the derived classification results. For this reason, we investigate fundamental influencing factors making geometric features more or less relevant with respect to the classification task. We present a framework which consists of five components addressing point sampling, neighborhood recovery, feature extraction, classification and feature relevance assessment. To analyze the impact of the main influencing factors which are represented by the given point sampling and the selected neighborhood type, we present the results derived with different configurations of our framework for a commonly used benchmark dataset for which a reference labeling with respect to three structural classes (<i>linear structures, planar structures</i> and <i>volumetric structures</i>) as well as a reference labeling with respect to five semantic classes (<i>Wire, Pole/Trunk, Façade, Ground</i> and <i>Vegetation</i>) is available.

A New Method of Data Smoothing for Scan-Line Point Cloud in Reverse Engineering

2014 ◽  
Vol 1006-1007 ◽  
pp. 352-355
Author(s):  
Ping Zhao ◽  
Xue Wei Bai ◽  
Yong Kui Li ◽  
Ying Li ◽  
Chang Yi Lv
Keyword(s):  
Point Cloud ◽  
Median Filter ◽  
Research Result ◽  
Original Data ◽  
Filter Method ◽  
Data Smoothing ◽  
Cloud Data ◽  
Mean Filter ◽  
Scan Line ◽  
Line Point

Based on summarizing characteristics of three common kinds of filter method (median filter, mean filter and Gaussian filter) through theory analysis and experiments in Imageware software, a more effective method of smoothing point cloud data was proposed, and the algorithm was realized by programming in MATLAB. Experimental results show that the proposed method combines the advantages of the median filter and mean filter in together, achieves the purpose of smoothing, also ensures the shape of the original data. This research result will lay a foundation for subsequent reconstruction precision of curved surface.

scholarly journals POINT-BASED MORPHOLOGICAL OPENING WITH INPUT DATA RETRIEVAL

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 53-58
Author(s):  
J. Balado ◽  
P. van Oosterom ◽  
L. Díaz-Vilariño ◽  
H. Lorenzo
Keyword(s):  
Point Cloud ◽  
Real Data ◽  
Data Retrieval ◽  
Original Data ◽  
Traffic Sign ◽  
Cloud Data ◽  
Structuring Element ◽  
Complex Process ◽  
Working Principle ◽  
Morphological Opening

Abstract. Mathematical morphology is a technique recently applied directly for point cloud data. Its working principle is based on the removal and addition of points from an auxiliary point cloud that acts as a structuring element. However, in certain applications within a more complex process, these changes to the original data represent an unacceptable loss of information. The aim of this work is to provide a modification of the morphological opening to retain original points and attributes. The proposed amendment involved in the morphological opening: erosion followed by dilatation. In morphological erosion, the new eroded points are retained. In morphological dilation, the structuring element does not add its points directly, but uses the point positions to search through the previously eroded points and retrieve them for the dilated point cloud. The modification was tested on synthetic and real data, showing a correct performance at the morphological level, and preserving the precision of the original points and their attributes. Furthermore, the conservation is shown to be very relevant in two possible applications such as traffic sign segmentation and occluded edge detection.

Path planning method based on discontinuous grid partition algorithm of point cloud for in situ printing

2019 ◽  
Vol 25 (3) ◽  
pp. 602-613 ◽  
Author(s):  
Qin Lian ◽  
Xiao Li ◽  
Dichen Li ◽  
Heng Gu ◽  
Weiguo Bian ◽  
...  
Keyword(s):  
Path Planning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Planning Method ◽  
Content Type ◽  
Cloud Data ◽  
Grid Partition ◽  
Point Density ◽  
Path Point

Purpose Path planning is an important part of three-dimensional (3D) printing data processing technology. This study aims to propose a new path planning method based on a discontinuous grid partition algorithm of point cloud for in situ printing. Design/methodology/approach Three types of parameters (i.e. structural, process and path interruption parameters) were designed to establish the algorithm model with the path error and the computation amount as the dependent variables. The path error (i.e. boundary error and internal error) was further studied and the influence of each parameter on the path point density was analyzed quantitatively. The feasibility of this method was verified by skin in situ printing experiments. Findings Path point density was positively correlated with Grid_size and negatively related to other parameters. Point_space, Sparsity and Line_space had greater influence on path point density than Indentation and Grid_size. In skin in situ printing experiment, two layers of orthogonal printing path were generated, and the material was printed accurately in the defect, which proved the feasibility of this method. Originality/value This study proposed a new path planning method that converted 3D point cloud data to a printing path directly, providing a new path planning solution for in situ printing. The discontinuous grid partition algorithm achieved controllability of the path planning accuracy and computation amount that could be applied to different processes.

scholarly journals Comparison of Different Feature Sets for TLS Point Cloud Classification

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4206 ◽  
Author(s):  
Quan Li ◽  
Xiaojun Cheng
Keyword(s):  
Feature Selection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Geometric Features ◽  
Real World Data ◽  
Cloud Data ◽  
Cloud Classification ◽  
Color Features ◽  
Point Cloud Classification ◽  
Corrected Intensity

Point cloud classification is an essential requirement for effectively utilizing point cloud data acquired by Terrestrial laser scanning (TLS). Neighborhood selection, feature selection and extraction, and classification of points based on the respective features constitute the commonly used workflow of point cloud classification. Feature selection and extraction has been the focus of many studies, and the choice of different features has had a great impact on classification results. In previous studies, geometric features were widely used for TLS point cloud classification, and only a few studies investigated the potential of both intensity and color on classification using TLS point cloud. In this paper, the geometric features, color features, and intensity features were extracted based on a supervoxel neighborhood. In addition, the original intensity was also corrected for range effect, which is why the corrected intensity features were also extracted. The different combinations of these features were tested on four real-world data sets. Experimental results demonstrate that both color and intensity features can complement the geometric features to help improve the classification results. Furthermore, the combination of geometric features, color features, and corrected intensity features together achieves the highest accuracy in our test.

scholarly journals COMPARISON OF POINT AND SEGMENT BASED POINT CLOUD CLASSIFICATION METHOD IN URBAN SCENES

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 461-465
Author(s):  
E. Hasanpour ◽  
M. Saadatseresht ◽  
E. G. Parmehr
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Classification Method ◽  
Geometric Features ◽  
Classification Result ◽  
Urban Scenes ◽  
Cloud Data ◽  
Random Forest Classification ◽  
Cloud Classification ◽  
Point Cloud Classification

Abstract. Point cloud classification is an essential requirement for effectively utilizing point cloud data acquired by different kind of sources such as Terrestrial Laser Scanning (TLS), Aerial LiDAR (Light Detection and Ranging), and Photogrammetry. Classification of point cloud is a process that points are separated into different point groups that each group has similar features. Point cloud classification can be done in three levels (point-based, segment-based, and object-based) and the choice of different level has significant impact on classification result. In this research, random forest classification method is utilized in which the point-wise and segment-wise spectral and geometric features are selected as the input of the classification. In our experiments, the results of point- and segment-based classification were compared. In addition, point-wise classification result for two different features (geometric with/without spectral features) has been compared and the results are presented. The experiments illustrated that segment based classification with both color and geometric features has the best overall accuracy of 83% especially near the object boundaries.

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