scholarly journals SCAN PROFILES BASED METHOD FOR SEGMENTATION AND EXTRACTION OF PLANAR OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 351-358 ◽  
Author(s):  
Hoang Long Nguyen ◽  
David Belton ◽  
Petra Helmholz
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Segmentation Method ◽  
Cloud Data ◽  
Processing Steps ◽  
Carrier Vehicle

The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.

scholarly journals SCAN PROFILES BASED METHOD FOR SEGMENTATION AND EXTRACTION OF PLANAR OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 351-358 ◽  
Author(s):  
Hoang Long Nguyen ◽  
David Belton ◽  
Petra Helmholz
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Segmentation Method ◽  
Cloud Data ◽  
Processing Steps ◽  
Carrier Vehicle

The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.

scholarly journals VISUALIZATION OF THE CONSTRUCTION OF ANCIENT ROMAN BUILDINGS IN OSTIA USING POINT CLOUD DATA

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 345-352
Author(s):  
Y. Hori ◽  
T. Ogawa
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Feature Matching ◽  
Three Dimensional ◽  
Point Clouds ◽  
Final Report ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Coverage Accuracy ◽  
New Research

The implementation of laser scanning in the field of archaeology provides us with an entirely new dimension in research and surveying. It allows us to digitally recreate individual objects, or entire cities, using millions of three-dimensional points grouped together in what is referred to as "point clouds". In addition, the visualization of the point cloud data, which can be used in the final report by archaeologists and architects, should usually be produced as a JPG or TIFF file. Not only the visualization of point cloud data, but also re-examination of older data and new survey of the construction of Roman building applying remote-sensing technology for precise and detailed measurements afford new information that may lead to revising drawings of ancient buildings which had been adduced as evidence without any consideration of a degree of accuracy, and finally can provide new research of ancient buildings. We used laser scanners at fields because of its speed, comprehensive coverage, accuracy and flexibility of data manipulation. Therefore, we “skipped” many of post-processing and focused on the images created from the meta-data simply aligned using a tool which extended automatic feature-matching algorithm and a popular renderer that can provide graphic results.

Design of Light-Small Mobile 3D Laser Measurement System

2013 ◽  
Vol 405-408 ◽  
pp. 3032-3036
Author(s):  
Yi Bo Sun ◽  
Xin Qi Zheng ◽  
Zong Ren Jia ◽  
Gang Ai
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Digital Camera ◽  
Scanning System ◽  
Point Cloud Data ◽  
3D Laser Scanning ◽  
Large Area ◽  
Cloud Data ◽  
Laser Scanning System

At present, most of the commercial 3D laser scanning measurement systems do work for a large area and a big scene, but few shows their advantage in the small area or small scene. In order to solve this shortage, we design a light-small mobile 3D laser scanning system, which integrates GPS, INS, laser scanner and digital camera and other sensors, to generate the Point Cloud data of the target through data filtering and fusion. This system can be mounted on airborne or terrestrial small mobile platform and enables to achieve the goal of getting Point Cloud data rapidly and reconstructing the real 3D model. Compared to the existing mobile 3D laser scanning system, the system we designed has high precision but lower cost, smaller hardware and more flexible.

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 MODEL FOR SEMANTICALLY RICH POINT CLOUD DATA

2017 ◽  
Vol IV-4/W5 ◽  
pp. 107-115 ◽  
Author(s):  
F. Poux ◽  
R. Neuville ◽  
P. Hallot ◽  
R. Billen
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Point Clouds ◽  
High Dimensional ◽  
Point Cloud Data ◽  
Model Adaptation ◽  
Spatial Concepts ◽  
Cloud Data ◽  
Classification Procedures ◽  
Source Of Information

This paper proposes an interoperable model for managing high dimensional point clouds while integrating semantics. Point clouds from sensors are a direct source of information physically describing a 3D state of the recorded environment. As such, they are an exhaustive representation of the real world at every scale: 3D reality-based spatial data. Their generation is increasingly fast but processing routines and data models lack of knowledge to reason from information extraction rather than interpretation. The enhanced smart point cloud developed model allows to bring intelligence to point clouds via 3 connected meta-models while linking available knowledge and classification procedures that permits semantic injection. Interoperability drives the model adaptation to potentially many applications through specialized domain ontologies. A first prototype is implemented in Python and PostgreSQL database and allows to combine semantic and spatial concepts for basic hybrid queries on different point clouds.

scholarly journals Reality Capture of Buildings Using 3D Laser Scanners

2021 ◽  
Author(s):  
Avar Almukhtar ◽  
Henry Abanda ◽  
Zaid O. Saeed ◽  
Joseph H.M. Tah
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Construction Project ◽  
Laser Scanner ◽  
Data File ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Laser Scanners ◽  
Project Data

The urgent need to improve performance in the construction industry has led to the adoption of many innovative technologies. 3D laser scanners are amongst the leading technologies being used to capture and process assets or construction project data for use in various applications. Due to its nascent nature, many questions are still unanswered about 3D laser scanning, which in turn contribute to the slow adaptation of the technology. Some of these include the role of 3D laser scanners in capturing and processing raw construction project data. How accurate is the 3D laser scanner or point cloud data? How does laser scanning fit with other wider emerging technologies such as Building Information Modelling (BIM)? This study adopts a proof-of-concept approach, which in addition to answering the afore-mentioned questions, illustrates the application of the technology in practice. The study finds that the quality of the data, commonly referred to as point cloud data is still a major issue as it depends on the distance between the target object and 3D laser scanner’s station. Additionally, the quality of the data is still very dependent on data file sizes and the computational power of the processing machine. Lastly, the connection between laser scanning and BIM approaches is still weak as what can be done with a point cloud data model in a BIM environment is still very limited. The aforementioned findings reinforce existing views on the use of 3D laser scanners in capturing and processing construction project data.

scholarly journals An Automatic Density Clustering Segmentation Method for Laser Scanning Point Cloud Data of Buildings

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Jianghong Zhao ◽  
Yan Dong ◽  
Siyu Ma ◽  
Huajun Liu ◽  
Shuangfeng Wei ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Segmentation Method ◽  
Cloud Data ◽  
Dbscan Algorithm ◽  
Density Clustering ◽  
Low Efficiency ◽  
Definition Of

Segmentation is an important step in point cloud data feature extraction and three-dimensional modelling. Currently, it is also a challenging problem in point cloud processing. There are some disadvantages of the DBSCAN method, such as requiring the manual definition of parameters and low efficiency when it is used for large amounts of calculation. This paper proposes the AQ-DBSCAN algorithm, which is a density clustering segmentation method combined with Gaussian mapping. The algorithm improves upon the DBSCAN algorithm by solving the problem of automatic estimation of the parameter neighborhood radius. The improved algorithm can carry out density clustering processing quickly by reducing the amount of computation required.

Design and Rapid Prototyping of S-Shape Push-Ups Frame Based on Laser Scanning

2015 ◽  
Vol 752-753 ◽  
pp. 1401-1405 ◽  
Author(s):  
Hong Jun Ni ◽  
Qing Qing Chen ◽  
Yi Pei ◽  
Yi Lv ◽  
Xing Xing Wang
Keyword(s):  
Rapid Prototyping ◽  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Laser Scanner ◽  
Point Cloud Data ◽  
Model Design ◽  
Cloud Data ◽  
Frame Model ◽  
Business Efficiency

Model design and rapid prototyping are utilized to manufacture push-ups frame. Point cloud data can be obtained by scanning parts with hand-held laser scanner, and imported into the Imageware to process. The varied points are removed, the missing points are repaired, and then the 3D model is designed through the Pro/E. Finally, the frame model is completed by rapid prototyping printers. The manufacturing period is shorten through the way of putting two technologies in the field of manufacturing together, the production requirements are met, and the business efficiency is improved.

The Advantages of Using Laser Scanners in Surveying in Protected Sites

2015 ◽  
pp. 382-402
Author(s):  
Gülhan Benli
Keyword(s):  
Protected Areas ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Laser Scanners ◽  
Point Data

Since the 2000s, terrestrial laser scanning, as one of the methods used to document historical edifices in protected areas, has taken on greater importance because it mitigates the difficulties associated with working on large areas and saves time while also making it possible to better understand all the particularities of the area. Through this technology, comprehensive point data (point clouds) about the surface of an object can be generated in a highly accurate three-dimensional manner. Furthermore, with the proper software this three-dimensional point cloud data can be transformed into three-dimensional rendering/mapping/modeling and quantitative orthophotographs. In this chapter, the study will present the results of terrestrial laser scanning and surveying which was used to obtain three-dimensional point clouds through three-dimensional survey measurements and scans of silhouettes of streets in Fatih in Historic Peninsula in Istanbul, which were then transposed into survey images and drawings. The study will also cite examples of the facade mapping using terrestrial laser scanning data in Istanbul Historic Peninsula Project.

scholarly journals Damage Detection Based on 3D Point Cloud Data Processing from Laser Scanning of Conveyor Belt Surface

2020 ◽  
Vol 13 (1) ◽  
pp. 55
Author(s):  
Paweł Trybała ◽  
Jan Blachowski ◽  
Ryszard Błażej ◽  
Radosław Zimroz
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Mining Operation ◽  
Cost Effective ◽  
Conveyor Belt ◽  
Inspection System ◽  
Substantial Part ◽  
Point Cloud Data ◽  
Cloud Data

Usually, substantial part of a mine haulage system is based on belt conveyors. Reliability of such system is significant in terms of mining operation continuity and profitability. Numerous methods for conveyor belt monitoring have been developed, although many of them require physical presence of the monitoring staff in the dangerous environment. In this paper, a remote sensing method for assessing a conveyor belt condition using the Terrestrial Laser Scanner (TLS) system has been described. For this purpose a methodology of semi-automatic processing of point cloud data for obtaining the belt geometry has been developed. The sample data has been collected in a test laboratory and processed with the proposed algorithms. Damaged belt surface areas have been successfully identified and edge defects were investigated. The proposed non-destructive testing methodology has been found to be suitable for monitoring the general condition of the conveyor belt and could be exceptionally successful and cost-effective if combined with an unmanned, robotic inspection system.

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