Reference number : GC-2017-Q4-00738, amendment number : , agreement type : G, recipient legal name : Hammad, Amin (Concordia University)|Hammad, Amin (Université Concordia), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Reality capture (RC) of constructed facilities (e.g. buildings) is an important step towards developing reliable_x000D_
as-is models that can be used for facilities management. Traditionally, these models are developed based on_x000D_
design drawings and/or surveying data of the facilities. However, design drawings of existing facilities may be_x000D_
missing or not reflecting the actual conditions of the facilities because of accumulated deterioration or because_x000D_
of maintenance and renovation activities that were not documented. On the other hand, conventional surveying_x000D_
methods are expensive, time consuming, and may not be safe in some cases (e.g. post-disaster inspection)._x000D_
Pegasus Research & Technologies (PRT) provides research and engineering services to the Canadian aerospace_x000D_
and defense sector, such as simulation test-bed for Unmanned Aerial Vehicles (UAV) experimentation using_x000D_
artificial intelligence. Recently, the service offering of PRT has grown to include RC for buildings facilities_x000D_
management applications using a laser scanner mounted on the UAV. However, transforming the data collected_x000D_
by the scanner to a useful Building Information Model (BIM) is a challenging task._x000D_
This project assumes that a UAV equipped with a laser scanner is used for collecting the data needed for_x000D_
creating a 3D model of the interior spaces in a building. Furthermore, it assumes that there is an algorithm for_x000D_
object recognition/classification based on the sensory data collected by the UAV. The results of the recognition_x000D_
are building components (e.g. wall, window, door, etc.) that are already defined in a semantic map. However, a_x000D_
gap in this area is that there is currently no direct way of linking this semantic map with BIM. The project aims_x000D_
to: (1) develop an interface between the semantic map and standard BIM classes; and (2) automatically create a_x000D_
BIM model based on the collected data in order to support the subsequent building inspection tasks., program purpose french : Reality capture (RC) of constructed facilities (e.g. buildings) is an important step towards developing reliable_x000D_
as-is models that can be used for facilities management. Traditionally, these models are developed based on_x000D_
design drawings and/or surveying data of the facilities. However, design drawings of existing facilities may be_x000D_
missing or not reflecting the actual conditions of the facilities because of accumulated deterioration or because_x000D_
of maintenance and renovation activities that were not documented. On the other hand, conventional surveying_x000D_
methods are expensive, time consuming, and may not be safe in some cases (e.g. post-disaster inspection)._x000D_
Pegasus Research & Technologies (PRT) provides research and engineering services to the Canadian aerospace_x000D_
and defense sector, such as simulation test-bed for Unmanned Aerial Vehicles (UAV) experimentation using_x000D_
artificial intelligence. Recently, the service offering of PRT has grown to include RC for buildings facilities_x000D_
management applications using a laser scanner mounted on the UAV. However, transforming the data collected_x000D_
by the scanner to a useful Building Information Model (BIM) is a challenging task._x000D_
This project assumes that a UAV equipped with a laser scanner is used for collecting the data needed for_x000D_
creating a 3D model of the interior spaces in a building. Furthermore, it assumes that there is an algorithm for_x000D_
object recognition/classification based on the sensory data collected by the UAV. The results of the recognition_x000D_
are building components (e.g. wall, window, door, etc.) that are already defined in a semantic map. However, a_x000D_
gap in this area is that there is currently no direct way of linking this semantic map with BIM. The project aims_x000D_
to: (1) develop an interface between the semantic map and standard BIM classes; and (2) automatically create a_x000D_
BIM model based on the collected data in order to support the subsequent building inspection tasks., agreement title english : Development of Semantic BIM for Supporting UAV-Based Reality Capture for Facilities Management Applications, agreement title french : Development of Semantic BIM for Supporting UAV-Based Reality Capture for Facilities Management Applications, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada