Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Change detection for indoor construction progress monitoring based on BIM, point clouds and uncertainties
Abstract Automatic construction progress documentation and metric evaluation of execution work in confined building interiors requires particularly reliable geometric evaluation and interpretation of statistically uncertain as-built point clouds. This paper presents a method for high-resolution change detection based on dense 3D point clouds from terrestrial laser scanning (TLS) and the discretization of space by voxels. In order to evaluate the metric accuracy of a BIM according to the Level of Accuracy (LOA) specification, the effects of laser range measurements on the occupancy of space are modeled with belief functions and evaluated using Dempster and Shafer's theory of evidence. The application is demonstrated on the point cloud data of multi temporal scanning campaigns of real indoor reconstructions. The results show that TLS point clouds are suitable to verify a given BIM up to LOA 40 if special attention is paid to the scanning geometry during the acquisition. The proposed method can be used to document construction progress, verify and even update the LOA status of a given BIM, confirming valid and BIM-compliant as-built models for further planning.
Highlights A new method for fusion of model uncertainty with uncertainties of point clouds. Consideration of indoor scanning geometry for accuracy assessment. Voxel based change detection and self-occlusion analysis. Experiments on progress evaluation on two real construction sites.
Change detection for indoor construction progress monitoring based on BIM, point clouds and uncertainties
Abstract Automatic construction progress documentation and metric evaluation of execution work in confined building interiors requires particularly reliable geometric evaluation and interpretation of statistically uncertain as-built point clouds. This paper presents a method for high-resolution change detection based on dense 3D point clouds from terrestrial laser scanning (TLS) and the discretization of space by voxels. In order to evaluate the metric accuracy of a BIM according to the Level of Accuracy (LOA) specification, the effects of laser range measurements on the occupancy of space are modeled with belief functions and evaluated using Dempster and Shafer's theory of evidence. The application is demonstrated on the point cloud data of multi temporal scanning campaigns of real indoor reconstructions. The results show that TLS point clouds are suitable to verify a given BIM up to LOA 40 if special attention is paid to the scanning geometry during the acquisition. The proposed method can be used to document construction progress, verify and even update the LOA status of a given BIM, confirming valid and BIM-compliant as-built models for further planning.
Highlights A new method for fusion of model uncertainty with uncertainties of point clouds. Consideration of indoor scanning geometry for accuracy assessment. Voxel based change detection and self-occlusion analysis. Experiments on progress evaluation on two real construction sites.
Change detection for indoor construction progress monitoring based on BIM, point clouds and uncertainties
Meyer, Theresa (Autor:in) / Brunn, Ansgar (Autor:in) / Stilla, Uwe (Autor:in)
15.06.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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