Monitoring of indoor environments by change detection in point clouds

This paper describes a complete method for monitoring indoor environments. Three-dimensional (3D) point clouds are first acquired from the environment under investigation by means of a laser range scanner in order to obtain several 3D models to be compared. Input datasets are thus registered each other exploiting a reliable variant of the iterative closest point algorithm (ICP) assisted by the use of deletion masks. These terms work in cooperation with the resampling of the model surfaces to reduce significantly the errors in the estimation of the registration parameters. Once datasets are registered, deformation maps are displayed to help the user to detect changes within the environment. Deletion masks are again used to filter measurement artifacts from the comparison, thus highlighting only the actual alterations of the environment. Several experiments are performed for the analysis of an indoor environment, proving the capability of the proposed method to reliably estimate the presence of alterations.