Mobile Robot Mapping and Immersive Building Simulation

This paper discusses a framework for integrated Augmented Reality (AR) architecture for indoor thermal performance data visualization that utilizes a mobile robot to generate environment maps. It consists of three modules: robot mapping, Computational Fluid Dynamics (CFD) simulation, and AR visualization. The robot mapping module enables the modelling of spatial geometry using a mobile robot. In order to generate steady approximations to scanned 3D datasets, the paper presents a novel “Split and Merge Expectation-Maximization Patch Fitting” (SMEMPF) planar approximation method. The developed SMEMPF method extends the classical ExpectationMaximization (EM) algorithm. It allows for precise adjustment of patches independent from the initial model. The final result is a set of patches identifying planar macro structures that consist of a collection of supported tiles. These patches are utilized to model the spatial geometry under investigation. The CFD simulation module facilitates the prediction of building performance data based on the spatial data generated using the SMEMPF method. The AR visualization module assists in interactive, immersive visualization of CFD simulation results. Such an integrated AR architecture will facilitate rapid multi-room mobile AR visualizations.