scholarly journals Change Detection and Feature Extraction of Debris-Flow Initiation by Rock-Slope Failure Using Point Cloud Processing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaoying He ◽  
Zeqing Yu ◽  
John M. Kemeny ◽  
Ann Youberg ◽  
Yunkun Wang
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Point Cloud ◽  
Slope Failure ◽  
Rock Slope ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Debris Flow Initiation

Our understanding of debris-flow initiation by slope failure is restricted by the challenge of acquiring accurate geomorphic features of debris flows and the structural setting of the rock mass in the remote mountainous terrain. Point cloud data of debris flows in Sabino Canyon, Tucson, Arizona, July 2006, with initiation by joint-controlled rock slope were obtained using multitemporal LiDAR scanning. Topographic changes were detected by comparing historical LiDAR scanning data of this area since 2005 by adopting open-source CloudCompare software. The results showed persistent scour and erosion in the debris flows after 2006. Point cloud data of joint-controlled rock in the initiation zone were generated by the means of photogrammetry using Pix4D software. The joint planes, the dip direction and the dip value of the joint plane, the joint spacing, and the joint roughness were therefore acquired by point cloud processing. Our study contributes a foundation for analyzing the relationship between the rock features, the generation of slope failure, and the initiation of debris flows.

Point Cloud Data Acquisition Based on Reverse Engineering Technology

2012 ◽  
Vol 159 ◽  
pp. 186-190 ◽  
Author(s):  
Xian Sheng Ran ◽  
Li Lin ◽  
Han Bing Wei
Keyword(s):  
Reverse Engineering ◽  
Data Acquisition ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Engineering Technology ◽  
Cloud Data ◽  
Cloud Processing ◽  
Optical Scanner ◽  
Point Cloud Processing

A reverse engineering based point cloud data acquisition method is addressed. The most critical part of reverse engineering (RE) is data acquisition of the digital model, the quality of design is determined by point cloud data acquisition, which is related to the accuracy of design. The measurement of relative position of different parts has been a difficulty of data acquisition. In this paper, the ATOS optical scanner was used as an example to illustrate the principle of three-dimensional scanner, the positioning methods and procedure of point cloud processing. A case study of point cloud data acquisition of car body was used to illustrate three-point positioning principle, which improves the accuracy of measurement compare with traditional method.

scholarly journals Development of Improved Semi-Automated Processing Algorithms for the Creation of Rockfall Databases

2021 ◽  
Vol 13 (8) ◽  
pp. 1479
Author(s):  
Heather Schovanec ◽  
Gabriel Walton ◽  
Ryan Kromer ◽  
Adam Malsam
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Rock Slope ◽  
Terrestrial Laser Scanning ◽  
Multiscale Model ◽  
Current Data ◽  
Cloud Data ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
The Creation

While terrestrial laser scanning and photogrammetry provide high quality point cloud data that can be used for rock slope monitoring, their increased use has overwhelmed current data analysis methodologies. Accordingly, point cloud processing workflows have previously been developed to automate many processes, including point cloud alignment, generation of change maps and clustering. However, for more specialized rock slope analyses (e.g., generating a rockfall database), the creation of more specialized processing routines and algorithms is necessary. More specialized algorithms include the reconstruction of rockfall volumes from clusters and points and automatic classification of those volumes are both processing steps required to automate the generation of a rockfall database. We propose a workflow that can automate all steps of the point cloud processing workflow. In this study, we detail adaptions to commonly used algorithms for rockfall monitoring use cases, such as Multiscale Model to Model Cloud Comparison (M3C2). This workflow details the entire processing pipeline for rockfall database generation using terrestrial laser scanning.

scholarly journals EFFICIENT LIDAR POINT CLOUD DATA MANAGING AND PROCESSING IN A HADOOP-BASED DISTRIBUTED FRAMEWORK

2017 ◽  
Vol IV-4/W2 ◽  
pp. 121-124 ◽  
Author(s):  
C. Wang ◽  
F. Hu ◽  
D. Sha ◽  
X. Han
Keyword(s):  
Point Cloud ◽  
Lidar Data ◽  
City Management ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Distributed Framework ◽  
3D Lidar ◽  
Parallel Fashion

Light Detection and Ranging (LiDAR) is one of the most promising technologies in surveying and mapping，city management, forestry, object recognition, computer vision engineer and others. However, it is challenging to efficiently storage, query and analyze the high-resolution 3D LiDAR data due to its volume and complexity. In order to improve the productivity of Lidar data processing, this study proposes a Hadoop-based framework to efficiently manage and process LiDAR data in a distributed and parallel manner, which takes advantage of Hadoop’s storage and computing ability. At the same time, the Point Cloud Library (PCL), an open-source project for 2D/3D image and point cloud processing, is integrated with HDFS and MapReduce to conduct the Lidar data analysis algorithms provided by PCL in a parallel fashion. The experiment results show that the proposed framework can efficiently manage and process big LiDAR data.

scholarly journals POINTO - A LOW COST SOLUTION TO POINT CLOUD PROCESSING

2017 ◽  
Vol XLII-2/W8 ◽  
pp. 111-117 ◽  
Author(s):  
H. Houshiar ◽  
S. Winkler
Keyword(s):  
User Interfaces ◽  
Point Cloud ◽  
Low Cost ◽  
Point Clouds ◽  
3D Point Cloud ◽  
Cloud Data ◽  
Cloud Processing ◽  
3D Point Clouds ◽  
Point Cloud Processing ◽  
Access To Data

With advance in technology access to data especially 3D point cloud data becomes more and more an everyday task. 3D point clouds are usually captured with very expensive tools such as 3D laser scanners or very time consuming methods such as photogrammetry. Most of the available softwares for 3D point cloud processing are designed for experts and specialists in this field and are usually very large software packages containing variety of methods and tools. This results in softwares that are usually very expensive to acquire and also very difficult to use. Difficulty of use is caused by complicated user interfaces that is required to accommodate a large list of features. The aim of these complex softwares is to provide a powerful tool for a specific group of specialist. However they are not necessary required by the majority of the up coming average users of point clouds. In addition to complexity and high costs of these softwares they generally rely on expensive and modern hardware and only compatible with one specific operating system. Many point cloud customers are not point cloud processing experts or willing to spend the high acquisition costs of these expensive softwares and hardwares. In this paper we introduce a solution for low cost point cloud processing. Our approach is designed to accommodate the needs of the average point cloud user. To reduce the cost and complexity of software our approach focuses on one functionality at a time in contrast with most available softwares and tools that aim to solve as many problems as possible at the same time. Our simple and user oriented design improve the user experience and empower us to optimize our methods for creation of an efficient software. In this paper we introduce Pointo family as a series of connected softwares to provide easy to use tools with simple design for different point cloud processing requirements. PointoVIEWER and PointoCAD are introduced as the first components of the Pointo family to provide a fast and efficient visualization with the ability to add annotation and documentation to the point clouds.

scholarly journals Automatic Point Cloud Semantic Segmentation of Complex Railway Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 2332
Author(s):  
Daniel Lamas ◽  
Mario Soilán ◽  
Javier Grandío ◽  
Belén Riveiro
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Study Data ◽  
Cloud Data ◽  
Traffic Lights ◽  
Cloud Processing ◽  
3D Point Clouds ◽  
Point Cloud Processing

The growing development of data digitalisation methods has increased their demand and applications in the transportation infrastructure field. Currently, mobile mapping systems (MMSs) are one of the most popular technologies for the acquisition of infrastructure data, with three-dimensional (3D) point clouds as their main product. In this work, a heuristic-based workflow for semantic segmentation of complex railway environments is presented, in which their most relevant elements are classified, namely, rails, masts, wiring, droppers, traffic lights, and signals. This method takes advantage of existing methodologies in the field for point cloud processing and segmentation, taking into account the geometry and spatial context of each classified element in the railway environment. This method is applied to a 90-kilometre-long railway lane and validated against a manual reference on random sections of the case study data. The results are presented and discussed at the object level, differentiating the type of the element. The indicators F1 scores obtained for each element are superior to 85%, being higher than 99% in rails, the most significant element of the infrastructure. These metrics showcase the quality of the algorithm, which proves that this method is efficient for the classification of long and variable railway sections, and for the assisted labelling of point cloud data for future applications based on training supervised learning models.

USE OF 3D POINT CLOUD DATA FOR ROCK SLOPE ASSESSMENT

2018 ◽  
Author(s):  
Neil F. Olson ◽  
◽  
Corinne R. Disenhof ◽  
Krystle Pelham ◽  
Tayler Engel ◽  
...  
Keyword(s):  
Point Cloud ◽  
Rock Slope ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data

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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