Online as-Built Building Information Model Update for Robotic Monitoring in Construction Sites

Today, automated techniques for the update of as-built Building Information Models (BIM) make use of offline algorithms restricting the update frequency to an extent where continuous monitoring becomes nearly impossible. To address this problem, we propose a new method for robotic monitoring that updates an as-built BIM in real-time by solving a Simultaneous Localization and Mapping (SLAM) problem where the map is represented as a collection of elements from the as-planned BIM. The suggested approach is based on the Rao-Blackwellized Particle Filter (RBPF) which enables explicit injection of prior knowledge from the building’s construction schedule, i.e., from a 4D BIM, or its elements’ spatial relations. In the methods section we describe the benefits of using an exact inverse sensor model that provides a measure for the existence probability of elements while considering the entire probabilistic existence belief map. We continue by outlining robustification techniques that include both geometrical and temporal dimensions and present how we account for common pose and shape mistakes in constructed elements. Additionally, we show that our method reduces to the standard Monte Carlo Localization (MCL) in known areas. We conclude by presenting simulation results of the proposed method and comparing it to adjacent alternatives.