scholarly journals Special Issue on “Terrestrial Laser Scanning”: Editors’ Notes

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4569
Author(s):  
Joan R. Rosell-Polo ◽  
Eduard Gregorio ◽  
Jordi Llorens
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Special Issue ◽  
Alternative Technologies ◽  
Cloud Processing ◽  
3D Point Clouds ◽  
Point Cloud Processing

In this editorial, we provide an overview of the content of the special issue on “Terrestrial Laser Scanning”. The aim of this Special Issue is to bring together innovative developments and applications of terrestrial laser scanning (TLS), understood in a broad sense. Thus, although most contributions mainly involve the use of laser-based systems, other alternative technologies that also allow for obtaining 3D point clouds for the measurement and the 3D characterization of terrestrial targets, such as photogrammetry, are also considered. The 15 published contributions are mainly focused on the applications of TLS to the following three topics: TLS performance and point cloud processing, applications to civil engineering, and applications to plant characterization.

scholarly journals COMPARING MULTI-SOURCE PHOTOGRAMMETRIC DATA DURING THE EXAMINATION OF VERTICALITY IN A MONUMENTAL TOWER

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 475-480
Author(s):  
J. Markiewicz ◽  
D. Zawieska ◽  
P. Podlasiak
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Aerial Vehicles ◽  
Geometric Quality

This paper presents an analysis of source photogrammetric data in relation to the examination of verticality in a monumental tower. In the proposed data processing methodology, the geometric quality of the point clouds relating to the monumental tower of the castle in Iłżawas established by using terrestrial laser scanning (Z+F 5006h, Leica C10), terrestrial photographs and digital images sourced via unmanned aerial vehicles (UAV) (Leica Aibot X6 Hexacopter). Tests were performed using the original software, developed by the authors, which allows for the automation of 3D point cloud processing. The software also facilitates the verification of the verticality of the tower and the assessment of the quality of utilized data.

scholarly journals Deformation Analysis of a Composite Bridge during Proof Loading Using Point Cloud Processing

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4332 ◽  
Author(s):  
Patryk Ziolkowski ◽  
Jakub Szulwic ◽  
Mikolaj Miskiewicz
Keyword(s):  
Remote Sensing ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Measurement Methods ◽  
Added Value ◽  
Translation Method ◽  
Cloud Processing ◽  
Point Cloud Processing

Remote sensing in structural diagnostics has recently been gaining attention. These techniques allow the creation of three-dimensional projections of the measured objects, and are relatively easy to use. One of the most popular branches of remote sensing is terrestrial laser scanning. Laser scanners are fast and efficient, gathering up to one million points per second. However, the weakness of terrestrial laser scanning is the troublesome processing of point clouds. Currently, many studies deal with the subject of point cloud processing in various areas, but it seems that there are not many clear procedures that we can use in practice, which indicates that point cloud processing is one of the biggest challenges of this issue. To tackle that challenge we propose a general framework for studying the structural deformations of bridges. We performed an advanced object shape analysis of a composite foot-bridge, which is subject to spatial deformations during the proof loading process. The added value of this work is the comprehensive procedure for bridge evaluation, and adaptation of the spheres translation method procedure for use in bridge engineering. The aforementioned method is accurate for the study of structural element deformation under monotonic load. The study also includes a comparative analysis between results from the spheres translation method, a total station, and a deflectometer. The results are characterized by a high degree of convergence and reveal the highly complex state of deformation more clearly than can be concluded from other measurement methods, proving that laser scanning is a good method for examining bridge structures with several competitive advantages over mainstream measurement methods.

scholarly journals Systematic Approach for Tunnel Deformation Monitoring with Terrestrial Laser Scanning

2021 ◽  
Vol 13 (17) ◽  
pp. 3519
Author(s):  
Dongfeng Jia ◽  
Weiping Zhang ◽  
Yanping Liu
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
High Accuracy ◽  
Deformation Monitoring ◽  
Reference Plane ◽  
Shield Tunnel ◽  
Cloud Processing ◽  
Point Cloud Processing

The use of terrestrial laser scanning (TLS) point clouds for tunnel deformation measurement has elicited much interest. However, general methods of point-cloud processing in tunnels are still under investigation, given the high accuracy and efficiency requirements in this area. This study discusses a systematic method of analyzing tunnel deformation. Point clouds from different stations need to be registered rapidly and with high accuracy before point-cloud processing. An orientation method of TLS in tunnels that uses a positioning base made in the laboratory is proposed for fast point-cloud registration. The calibration methods of the positioning base are demonstrated herein. In addition, an improved moving least-squares method is proposed as a way to reconstruct the centerline of a tunnel from unorganized point clouds. Then, the normal planes of the centerline are calculated and are used to serve as the reference plane for point-cloud projection. The convergence of the tunnel cross-section is analyzed, based on each point cloud slice, to determine the safety status of the tunnel. Furthermore, the results of the deformation analysis of a particular shield tunnel site are briefly discussed.

scholarly journals A sensor skid for precise 3D modeling of production lines

2014 ◽  
Vol II-5 ◽  
pp. 117-122 ◽  
Author(s):  
J. Elseberg ◽  
D. Borrmann ◽  
J. Schauer ◽  
A. Nüchter ◽  
D. Koriath ◽  
...  
Keyword(s):  
3D Modeling ◽  
Production Line ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Production Lines ◽  
3D Point Clouds ◽  
Laser Scanners ◽  
Rapid Characterization

Motivated by the increasing need of rapid characterization of environments in 3D, we designed and built a sensor skid that automates the work of an operator of terrestrial laser scanners. The system combines terrestrial laser scanning with kinematic laser scanning and uses a novel semi-rigid SLAMmethod. It enables us to digitize factory environments without the need to stop production. The acquired 3D point clouds are precise and suitable to detect objects that collide with items moved along the production line.

scholarly journals Deformation monitoring using laser scanned point clouds and BIM

2018 ◽  
Vol 245 ◽  
pp. 01002 ◽  
Author(s):  
Vladimir Badenko ◽  
Dmitry Volgin ◽  
Sergey Lytkin
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Deformation Monitoring ◽  
Future Research ◽  
Cloud Processing ◽  
3D Point Clouds ◽  
Point Cloud Processing ◽  
Informational Model ◽  
Technology Gaps

Laser scanning is an essential method for monitoring of the operation of buildings or structures. It involves creating as-is BIM from point clouds obtained from laser scanning. In this article we present our workflow for the generation of information model from 3D point clouds of concrete tetrapod blocks on navigable structure C-1. Point cloud processing method for making informational model for long term monitoring is described. As a result of the research BIM model with each tetrapod was created for deformational monitoring in the comparison with next year model. Finally, we identify and discuss technology gaps that need to be addressed in future research.

scholarly journals Semi-automatic extraction of sectional view from point clouds – The case of Ottmarsheim's abbey-church

2014 ◽  
Vol XL-5 ◽  
pp. 343-348 ◽  
Author(s):  
T. Landes ◽  
S. Bidino ◽  
R. Guild
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Cutting Plane ◽  
Mean Deviation ◽  
Surfaces Of Revolution ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Heritage Buildings ◽  
Time Required

Today, elevations or sectional views of buildings are often produced from terrestrial laser scanning. However, due to the amount of data to process and because usually 2D maps are required by customers, the 3D point cloud is often degraded into 2D slices. In a sectional view, not only the portions of the objet which are intersected by the cutting plane but also edges and contours of other parts of the object which are visible behind the cutting plane are represented. To avoid the tedious manual drawing, the aim of this work is to propose a semi-automatic approach for creating sectional views by point cloud processing. The extraction of sectional views requires in a first step the segmentation of the point cloud into planar and non-planar entities. Since in cultural heritage buildings, arches, vaults, columns can be found, the position and the direction of the sectional view must be taken into account before contours extraction. Indeed, the edges of surfaces of revolution depend on the chosen view. The developed extraction approach is detailed based on point clouds acquired inside and outside churches. The resulting sectional view has been evaluated in a qualitative and quantitative way by comparing it with a reference sectional view made by hand. A mean deviation of 3 cm between both sections proves that the proposed approach is promising. Regarding the processing time, despite a few manual corrections, it has saved 40% of the time required for manual drawing.

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.

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 A SERVICE-ORIENTED INDOOR POINT CLOUD PROCESSING PIPELINE

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 339-346
Author(s):  
V. Stojanovic ◽  
M. Trapp ◽  
R. Richter ◽  
J. Döllner
Keyword(s):  
Built Environment ◽  
Mobile Devices ◽  
Point Cloud ◽  
Point Clouds ◽  
Semantic Enrichment ◽  
Cloud Processing ◽  
Processing Pipeline ◽  
3D Point Clouds ◽  
Point Cloud Processing ◽  
Service Oriented

Abstract. Visualization of point clouds plays an important role in understanding the context of the digital representation of the built environment. Modern commodity mobile devices (e.g., smartphones and tablets), are capable of capturing representations in the form of 3D point clouds, with their depth-sensing and photogrammetry capabilities. Points clouds enable the encoding of important spatial and physical features of the built environment they represent. However, once captured, point clouds need to be processed before they can be used for further semantic enrichment and decision making. An integrated pipeline for such processes is crucial for use in larger and more complex enterprise systems and data analysis platforms, especially within the realm of Facility Management (FM) and Real Estate 4.0. We present and discuss a prototypical implementation for a service-oriented point cloud processing pipeline. The presented processing features focus on detecting and visualizing spatial deviations between as-is versus as-designed representations. We discuss the design and implementation of these processing features, and present experimental results. The presented approach can be used as a lightweight software component for processing indoor point clouds captured using commodity mobile devices, as well as primary deviation analysis, and also provides a processing link for further semantic enrichment of base-data for Building Information Modeling (BIM) and Digital Twin (DT) applications.

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