Development of Semantic BIM for Supporting UAV-Based Reality Capture for Facilities Management Applications

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Reality capture (RC) of constructed facilities (e.g. buildings) is an important step towards developing reliableas-is models that can be used for facilities management. Traditionally, these models are developed based ondesign drawings and/or surveying data of the facilities. However, design drawings of existing facilities may bemissing or not reflecting the actual conditions of the facilities because of accumulated deterioration or becauseof maintenance and renovation activities that were not documented. On the other hand, conventional surveyingmethods are expensive, time consuming, and may not be safe in some cases (e.g. post-disaster inspection).Pegasus Research & Technologies (PRT) provides research and engineering services to the Canadian aerospaceand defense sector, such as simulation test-bed for Unmanned Aerial Vehicles (UAV) experimentation usingartificial intelligence. Recently, the service offering of PRT has grown to include RC for buildings facilitiesmanagement applications using a laser scanner mounted on the UAV. However, transforming the data collectedby the scanner to a useful Building Information Model (BIM) is a challenging task.This project assumes that a UAV equipped with a laser scanner is used for collecting the data needed forcreating a 3D model of the interior spaces in a building. Furthermore, it assumes that there is an algorithm forobject recognition/classification based on the sensory data collected by the UAV. The results of the recognitionare building components (e.g. wall, window, door, etc.) that are already defined in a semantic map. However, agap in this area is that there is currently no direct way of linking this semantic map with BIM. The project aimsto: (1) develop an interface between the semantic map and standard BIM classes; and (2) automatically create aBIM model based on the collected data in order to support the subsequent building inspection tasks.