scholarly journals Nonparametric-based estimation method for river cross-sections with point cloud data from UAV photography URiver-X version 1.0 -methodology development

2021 ◽  
Author(s):  
Taesam Lee ◽  
Kiyoung Sung
Keyword(s):  
Cross Section ◽  
Point Cloud ◽  
Polynomial Regression ◽  
Estimation Method ◽  
Point Cloud Data ◽  
Suitable Alternative ◽  
Cloud Data ◽  
Trapezoidal Shape ◽  
Abrupt Changes ◽  
The Cross

Abstract. Aerial surveying with unmanned aerial vehicles (UAVs) has been popularly employed in river management and flood monitoring. One of the major processes in UAV aerial surveying for river applications is to demarcate the cross-section of a river. From the photo images of aerial surveying, a point cloud dataset can be abstracted with the structure from motion (SfM) technique. To accurately demarcate the cross-section from the cloud points, an appropriate delineation technique is required to reproduce the characteristics of natural and manmade channels, including abrupt changes, bumps, and lined shapes, even though the basic shape of natural and manmade channels is a trapezoidal shape. Therefore, a nonparametric-based estimation technique, called the K-nearest neighbor local linear regression (KLR) model, was tested in the current study to demarcate the cross-section of a river with a point cloud dataset from aerial surveying. The proposed technique was tested with a simulated dataset based on trapezoidal channels and compared with the traditional polynomial regression model and another nonparametric technique, locally weighted scatterplot smoothing (LOWESS). Furthermore, the KLR model was applied to a real case study in the Migok-cheon stream, South Korea. The results indicate that the proposed KLR model can be a suitable alternative for demarcating the cross-section of a river with point cloud data from UAV aerial surveying by reproducing the critical characteristics of natural and manmade channels, including abrupt changes and small bumps, as well as the overall trapezoidal shape.

scholarly journals An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 172 ◽  
Author(s):  
Chunxiao Wang ◽  
Min Ji ◽  
Jian Wang ◽  
Wei Wen ◽  
Ting Li ◽  
...  
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Estimation Method ◽  
Point Clouds ◽  
Maximum Distance ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Data Segmentation ◽  
Different Types ◽  
Cloud Data Processing

Point cloud data segmentation, filtering, classification, and feature extraction are the main focus of point cloud data processing. DBSCAN (density-based spatial clustering of applications with noise) is capable of detecting arbitrary shapes of clusters in spaces of any dimension, and this method is very suitable for LiDAR (Light Detection and Ranging) data segmentation. The DBSCAN method needs at least two parameters: The minimum number of points minPts, and the searching radius ε. However, the parameter ε is often harder to determine, which hinders the application of the DBSCAN method in point cloud segmentation. Therefore, a segmentation algorithm based on DBSCAN is proposed with a novel automatic parameter ε estimation method—Estimation Method based on the average of k nearest neighbors’ maximum distance—with which parameter ε can be calculated on the intrinsic properties of the point cloud data. The method is based on the fitting curve of k and the mean maximum distance. The method was evaluated on different types of point cloud data: Airborne, and mobile point cloud data with and without color information. The results show that the accuracy values using ε estimated by the proposed method are 75%, 74%, and 71%, which are higher than those using parameters that are smaller or greater than the estimated one. The results demonstrate that the proposed algorithm can segment different types of LiDAR point clouds with higher accuracy in a robust manner. The algorithm can be applied to airborne and mobile LiDAR point cloud data processing systems, which can reduce manual work and improve the automation of data processing.

scholarly journals Extracting of Cross Section Profiles from Complex Point Cloud Data Sets

2019 ◽  
Vol 52 (10) ◽  
pp. 346-351 ◽  
Author(s):  
H. Setareh Kokab ◽  
R. Jill Urbanic
Keyword(s):  
Cross Section ◽  
Point Cloud ◽  
Data Sets ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Complex Point

Subway Tunnel Cross-Section Surveying Based on Ground 3D Laser Scanning Data

2014 ◽  
Vol 1079-1080 ◽  
pp. 296-299
Author(s):  
Xian Ge Cao ◽  
Jin Ling Yang ◽  
Xiang Lai Meng ◽  
Wei Cheng Zhang
Keyword(s):  
Cross Section ◽  
Laser Scanning ◽  
Point Cloud Data ◽  
3D Laser Scanning ◽  
Subway Tunnel ◽  
Cloud Data ◽  
Technical Requirements ◽  
The Cross ◽  
Tunnel Cross Section ◽  
Working Efficiency

Afterthe construction of subway main structure, in order to realize route adjustmentof alignment and gradient, it needs to survey the cross-section of subwaytunnel. Compared with conventional measuring methods, 3D laser scanning has thecharacteristics of non-contact measurement and can collect space 3D point clouddata with high density, this can improve the working efficiency for the subwaycross-section surveying. Based on the Leica Scanstation 2 scanners this paperanalyzed the 3D laser scanning point cloud data collection procedures and dataprocessing, expounded the subway cross-section surveying method based on pointcloud data; analyzed the feasibility of 3D laser scanning technology in theapplication of tunnel cross-section surveying based on the field validationdata. The results show that the cross-section measured by this method can meetthe technical requirements of route adjustment of alignment and gradient.

A Study on Data Acquisition from Sections of Virtual Coat Profile

2011 ◽  
Vol 230-232 ◽  
pp. 1204-1209
Author(s):  
Ji Hong Xu ◽  
Xiao Lin Dai ◽  
Shu Ping Gao
Keyword(s):  
Experimental Method ◽  
Cross Section ◽  
Point Cloud ◽  
Source Code ◽  
Geometric Algorithms ◽  
Point Cloud Data ◽  
Main Program ◽  
Data Format ◽  
Cloud Data ◽  
Body Scanner

Data was obtained through scanning manikin and coats separated by using [TC]2 3D body scanner. The method, using [TC]2 scanner as the experimental method and through double converting the scanned data format to get torso geometric section sets, was analyzed. Main program source code of Torso was provided in this paper. Geometric algorithms of point cloud data and curve data in there sections was provided based on the interception ways of horizontal sections, vertical sections and other random oblique sections toward torso geometric cross section.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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