Change detection for indoor construction progress monitoring based on BIM, point clouds and uncertainties

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.