IFC-based generation of semantic obstacle maps for autonomous robotic systems

Abstract

Autonomous Robotic Systems (ARSs) in the construction industry usually have to perform preliminary mapping of construction environments before deployment. For large and complex sites, this can be time-consuming. With Building Information Modeling (BIM), a lot of information is already available about sites. This study proposes a method to make that information available to ARSs to streamline autonomous tasks and remove the need for mapping. This is achieved by automatically generating semantic and color-coded obstacle maps from IFC files. The results are obstacle maps that can be used for autonomous navigation that remove the need for mapping.