State of Research in Automatic As-Built Modelling

Pătrăucean, Viorica; Armeni, Iro; Nahangi, Mohammad; Yeung, Jamie; Brilakis, Ioannis; Haas, Carl T.

doi:10.22260/isarc2015/0024

Cited by 38 publications

(42 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Tailoring the model to reality time and again is also essential when monitoring the progress of construction projects and registering what has (or has not) been built according to plan or specifications [25]. Hence, it is not enough to create a semantically rich model of the industrial object, but this model should capture its actual as-is state [22].…”

Section: Related Work and Technology Trendsmentioning

confidence: 99%

Engineering Support Systems for Industrial Machines and Plants

Nonaka

Nakano

Ohya

et al. 2017

Advances in Through-Life Engineering Services

View full text Add to dashboard Cite

In the business of industrial machines and plants, rapid and detailed estimates for planning installation or replacement of equipment, or maintenance work are key requirements for meeting the demands for greater reliability and lower costs, and for maintaining safe and secure operation. These engineering services should be supported by specific computer-aided methods. When replacing equipment at complex buildings or plants with high equipment density, the existing state of the installation locations and transportation routes for old and new equipment need to be properly measured. We have met this need by developing a specific object recognition technology based on 3D measurement, and by developing high-speed calculation technology of optimal routes for installation parts. This article provides an overview of these development projects with some real business application results.

show abstract

Section: Related Work and Technology Trendsmentioning

confidence: 99%

Engineering Support Systems for Industrial Machines and Plants

Nonaka

Nakano

Ohya

et al. 2017

Advances in Through-Life Engineering Services

View full text Add to dashboard Cite

show abstract

“…Most of the related work mainly engage in the rebuilding of as-built BIM from point clouds (Pătrăucean et al, 2015;Xiong et al, 2013;Tang et al, 2010) or the comparison between as-built and as-planed BIM for process monitoring (Golparvar-Fard et al, 2011;Tuttas et al, 2014a;Rankohi and Waugh, 2014). On the basis of Scanvs-BIM system, some applications are also developed using point clouds to trail specified construction objects like Mechanical, Electrical and Plumbing (MEP) components (Bosché et al, 2015) and temporary or secondary objects like shoring, rebar (Turkan et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

Classification of Photogrammetric Point Clouds of Scaffolds for Construction Site Monitoring Using Subspace Clustering and Pca

Xu¹,

Tuttas²,

Heogner³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:This paper presents an approach for the classification of photogrammetric point clouds of scaffolding components in a construction site, aiming at making a preparation for the automatic monitoring of construction site by reconstructing an as-built Building Information Model (as-built BIM). The points belonging to tubes and toeboards of scaffolds will be distinguished via subspace clustering process and principal components analysis (PCA) algorithm. The overall workflow includes four essential processing steps. Initially, the spherical support region of each point is selected. In the second step, the normalized cut algorithm based on spectral clustering theory is introduced for the subspace clustering, so as to select suitable subspace clusters of points and avoid outliers. Then, in the third step, the feature of each point is calculated by measuring distances between points and the plane of local reference frame defined by PCA in cluster. Finally, the types of points are distinguished and labelled through a supervised classification method, with random forest algorithm used. The effectiveness and applicability of the proposed steps are investigated in both simulated test data and real scenario. The results obtained by the two experiments reveal that the proposed approaches are qualified to the classification of points belonging to linear shape objects having different shapes of sections. For the tests using synthetic point cloud, the classification accuracy can reach 80%, with the condition contaminated by noise and outliers. For the application in real scenario, our method can also achieve a classification accuracy of better than 63%, without using any information about the normal vector of local surface.

show abstract

“…In the last years, laser scanner has become an efficient instrument for collecting three-dimensional and high quality data of the as-built status of large-scale infrastructure and intense efforts have been made towards the as-built modelling from point clouds (Haala and Kada, 2010;Volk et al, 2014;Patraucean et al, 2015). Most of existing literature deals with the reconstruction of building components, spaces and openings while less attention is paid to obstacle detection.…”

Section: Introductionmentioning

confidence: 99%

Indoor Navigation From Point Clouds: 3d Modelling and Obstacle Detection

Díaz-Vilariño¹,

Bogusławski²,

Khoshelham³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:In the recent years, indoor modelling and navigation has become a research of interest because many stakeholders require navigation assistance in various application scenarios. The navigational assistance for blind or wheelchair people, building crisis management such as fire protection, augmented reality for gaming, tourism or training emergency assistance units are just some of the direct applications of indoor modelling and navigation.Navigational information is traditionally extracted from 2D drawings or layouts. Real state of indoors, including opening position and geometry for both windows and doors, and the presence of obstacles is commonly ignored.In this work, a real indoor-path planning methodology based on 3D point clouds is developed. The value and originality of the approach consist on considering point clouds not only for reconstructing semantically-rich 3D indoor models, but also for detecting potential obstacles in the route planning and using these for readapting the routes according to the real state of the indoor depictured by the laser scanner.

show abstract

State of Research in Automatic As-Built Modelling

Cited by 38 publications

References 22 publications

Engineering Support Systems for Industrial Machines and Plants

Engineering Support Systems for Industrial Machines and Plants

Classification of Photogrammetric Point Clouds of Scaffolds for Construction Site Monitoring Using Subspace Clustering and Pca

Indoor Navigation From Point Clouds: 3d Modelling and Obstacle Detection

Contact Info

Product

Resources

About