scholarly journals Combination of Terrestrial Laser Scanner and Unmanned Aerial Vehicle Technology in The Manufacture of Building Information Model

2021 ◽  
Vol 5 (2) ◽  
pp. 520-525
Author(s):  
Sawitri Subiyanto ◽  
Nurhadi Bashit ◽  
Naftalie Dinda Rianty ◽  
Aulia Darmaputri Savitri
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Rapid Development ◽  
Laser Scanner ◽  
Information Model ◽  
Terrestrial Laser Scanner ◽  
Building Information Model ◽  
Cloud Data ◽  
Building Information ◽  
Aerial Vehicle

The rapid development of the construction world in Indonesia has led to an increase in supporting technology that is more effective and efficient. The Building Information Model (BIM) technology that begins with the creation of an as-built 3D model, this model describes the existing condition of the building. The Terrestrial Laser Scanner (TLS) method can provide a point cloud with a decent point density, but there are still areas of the building that aren't covered, such as the roof. To be more complete and detailed, additional data is needed using an Unmanned Aerial Vehicle (UAV). The results of the combination of TLS and UAV complement each other so that the results of the point cloud can form more detailed buildings. BIM may be built by combining these two data sets, allowing for the three-dimensional depiction of assets in buildings. The registration results for TLS point cloud data have a fairly good value where the overlap value is 44.9% (minimum 30%), balance is 41.2% (minimum 20%), points < 6mm is 98.9% (minimum 90%). The measurement results using the UAV have an RMSE GCP value of 0.266m and an RMSE ICP of 0.455m. Merging the results of TLS and UAV measurements is done using 3DReshaper software with four align points. The final result of making the BIM model is obtained level of detail (LOD) 3 where room models such as columns, floors, stairs, and walls are well depicted, while asset models such as furniture are also depicted although they are still simple objects.

scholarly journals Phased Reverse Engineering Framework for Sustainable Cultural Heritage Archives Using Laser Scanning and BIM: The Case of the Hwanggungwoo (Seoul, Korea)

2020 ◽  
Vol 12 (19) ◽  
pp. 8108
Author(s):  
Namhyuk Ham ◽  
Baek-Il Bae ◽  
Ok-Kyung Yuh
Keyword(s):  
Cultural Heritage ◽  
Reverse Engineering ◽  
Point Cloud ◽  
Laser Scanning ◽  
Information Model ◽  
Point Cloud Data ◽  
Building Information Model ◽  
Cloud Data ◽  
Building Information

This study proposed a phased reverse engineering framework to construct cultural heritage archives using laser scanning and a building information model (BIM). This framework includes acquisition of point cloud data through laser scanning. Unlike previous studies, in this study, a standard for authoring BIM data was established through comparative analysis of existing archives and point cloud data, and a method of building valuable BIM data as an information model was proposed. From a short-term perspective, additional archives such as member lists and drawings can be extracted from BIM data built as an information model. In addition, from a long-term perspective, a scenario for using the cultural heritage archive consisting of historical records, point cloud data, and BIM data was presented. These scenarios were verified through a case study. In particular, through the BIM data building and management method, relatively very light BIM data (499 MB) could be built based on point cloud data (more than 917 MB), which is a large amount of data.

scholarly journals An Automated Rectification Method for Unmanned Aerial Vehicle LiDAR Point Cloud Data Based on Laser Intensity

2019 ◽  
Vol 11 (7) ◽  
pp. 811 ◽  
Author(s):  
Xianfeng Zhang ◽  
Renqiang Gao ◽  
Quan Sun ◽  
Junyi Cheng
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Intensity ◽  
Laser Scanner ◽  
Angular Error ◽  
Measurement Unit ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Aerial Vehicle ◽  
Intensity Images

Point cloud rectification is an efficient approach to improve the quality of laser point cloud data. Conventional rectification methods mostly relied on ground control points (GCPs), typical artificial ground objects, and raw measurements of the laser scanner which impede automation and adaptability in practice. This paper proposed an automated rectification method for the point cloud data that are acquired by an unmanned aerial vehicle LiDAR system based on laser intensity, with the goal to reduce the dependency of ancillary data and improve the automated level of the rectification process. First, laser intensity images were produced by interpolating the intensity data of all the LiDAR scanning strips. Second, a scale-invariant feature transform algorithm was conducted to extract two dimensional (2D) tie points from the intensity images; the pseudo tie points were removed by using a random sample consensus algorithm. Next, all the 2D tie points were transformed to three dimensional (3D) point cloud to derive 3D tie point sets. After that, the observation error equations were created with the condition of coplanar constraints. Finally, a nonlinear least square algorithm was applied to solve the boresight angular error parameters, which were subsequently used to correct the laser point cloud data. A case study in Shehezi, Xinjiang, China was implemented with our proposed method and the results indicate that our method is efficient to estimate the boresight angular error between the laser scanner and inertial measurement unit. After applying the results of the boresight angular error solution to rectify the laser point cloud, the planar root mean square error (RMSE) is 5.7 cm and decreased by 1.1 cm in average; the elevation RMSE is 1.4 cm and decreased by 0.8 cm in average. Comparing with the stepwise geometric method, our proposed method achieved similar horizontal accuracy and outperformed it in vertical accuracy of registration.

scholarly journals BUILDING INFORMATION MODELLING OF A MULTI STOREY BUILDING USING TERRESTRIAL LASER SCANNER AND VISUALISATION USING POTREE: AN OPEN SOURCE POINT CLOUD RENDERER

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 421-426
Author(s):  
A. Yeshwanth Kumar ◽  
M. A. Noufia ◽  
K. A. Shahira ◽  
A. M. Ramiya
Keyword(s):  
Open Source ◽  
Web Application ◽  
Point Cloud ◽  
Laser Scanner ◽  
Terrestrial Laser Scanner ◽  
Point Cloud Data ◽  
Building Information Modelling ◽  
Cloud Data ◽  
Building Information ◽  
Storey Building

Abstract. With the rapid development in infrastructure, the need to document man-made structures is in increasing demand and inevitable. Such a process of digital documentation of buildings is called Building Information Modelling (BIM). Conventional techniques of BIM involve manual drafting &amp; modelling using computer aided design, drafting &amp; modelling software. Although these techniques are more accurate, given the increase in the size and complexity of modern structures, it would be tedious and time consuming for such manual work. It is in this context LiDAR shows great potential to simplify this task. Laser scanning enables rapid mapping of a building with a high degree of spatial accuracy. Since the spatial point sampling distance of any LiDAR scanner is usually in the order of centimetres or millimetres, this has potential not only to generate high density scans of the building but also to identify even the smallest defects in a structure. This facilitates using LiDAR to study the serviceability of a building. In this project, the feasibility of using a terrestrial laser scanner (TLS) to scan a multi-storey building was investigated. Additionally, the reliability of Potree for visualising point cloud data was tested. Potree is an open-source WebGL based point cloud renderer. Potree enables us to render point clouds and visualise in a portable web application. This application is also capable of making measurements of high accuracy on the 3D model of the library. This could serve to be of great utility in surveying applications. The object of study was chosen as a six-storey building, each floor having differing layouts. Two of these storeys were below ground surface level which also proved to be a test for the reliability of TLS in challenging terrain. The building has a towering height and large footprint which made it a perfect candidate for this project. A total of 54 scans (44 interior scans and 10 exterior scans of the library) were acquired with each subsequent scan station not more than 10m apart from the previous one. This data was brought to the lab for further processing. The processing was carried out using open-source software packages (LAStools, CloudCompare, etc). After processing, the complete point cloud data had 483,292,994 points. In order to make the data easier to handle, spatial sub-sampling of the data was done after which the final point cloud had 87,789,548 points. Finally, this sub-sampled point cloud was published using the open source Potree Converter into an interactive web application.

scholarly journals Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 201
Author(s):  
Michael Bekele Maru ◽  
Donghwan Lee ◽  
Kassahun Demissie Tola ◽  
Seunghee Park
Keyword(s):  
Optical Sensors ◽  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Depth Camera ◽  
Terrestrial Laser Scanner ◽  
Cloud Data ◽  
3D Point Clouds ◽  
3D Information

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

scholarly journals Experimental Study of Possibilities for Developing a Building Information Model of a Laser-Scanned Physical Object

2021 ◽  
Vol 906 (1) ◽  
pp. 012051
Author(s):  
Danail Nedyalkov
Keyword(s):  
Point Cloud ◽  
Information Model ◽  
Physical Object ◽  
Technical Documentation ◽  
Object Point ◽  
Building Information Model ◽  
The Real ◽  
Sufficient Detail ◽  
Energy Behavior ◽  
Building Information

Abstract The methodological approach when using a scanned physical object to build a building information model (BIM) is based on laser scanning technology and aims to create technical documentation of existing buildings, most often with the status of historically significant sites. The BIM technology can be used as an integral part for the creation of the documentation in the process of construction and of the new sites, as well as their administrative and managerial control in the process of their construction and operation. The essence of the experiment is to model space in a parametric three-dimensional model (BIM) in the ArchiCAD program, using a laser-scanned physical object (point cloud). The cloud obtained from the laser scan contains detailed spatial information, which is used in the basis of creation of a construction information model (BIM) and control during the development of the model. The laser-scanned physical object (point cloud) contains the same geometric information as the construction information model (BIM), but with a much smaller amount of data, the file size is visible - point cloud - 30.41 MB, BIM - 9.83 MB). The advantages of BIM over the point cloud is to give the ability to edit the model, to study the energy behavior of the model, to create construction and technical documentation of the scanned object, as well as to disclose the ability to fill in technical data and parameters based on the map and cadastral basis. By means of the density of the point cloud (parameter of the equipment used - laser scanner) of the scanned object, information is obtained and used with sufficient detail and accuracy about the physical data of the real object and this is the basis for the full and comprehensive content of BIM. Based on the sufficient detail created in the BIM for the physical object, it is possible for its combinability and its actual use in the real environment.

scholarly journals An Automatic Tree Skeleton Extracting Method Based on Point Cloud of Terrestrial Laser Scanner

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Ronghao Li ◽  
Guochao Bu ◽  
Pei Wang
Keyword(s):  
Topological Structure ◽  
Point Cloud ◽  
Laser Scanner ◽  
High Accuracy ◽  
Terrestrial Laser Scanner ◽  
Peach Tree ◽  
Point Cloud Data ◽  
Breadth First Search ◽  
Cloud Data ◽  
Real Tree

Tree skeleton could describe the shape and topological structure of a tree, which are useful to forest researchers. Terrestrial laser scanner (TLS) can scan trees with high accuracy and speed to acquire the point cloud data, which could be used to extract tree skeletons. An adaptive extracting method of tree skeleton based on the point cloud data of TLS was proposed in this paper. The point cloud data were segmented by artificial filtration and k-means clustering, and the point cloud data of trunk and branches remained to extract skeleton. Then the skeleton nodes were calculated by using breadth first search (BFS) method, quantifying method, and clustering method. Based on their connectivity, the skeleton nodes were connected to generate the tree skeleton, which would be smoothed by using Laplace smoothing method. In this paper, the point cloud data of a toona tree and peach tree were used to test the proposed method and for comparing the proposed method with the shortest path method to illustrate the robustness and superiority of the method. The experimental results showed that the shape of tree skeleton extracted was consistent with the real tree, which showed the method proposed in the paper is effective and feasible.

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