Semantic Optimal Robot Navigation Using Building Information on Construction Sites

The navigation of robotic systems in construction sites often relies on sensor data from the robot. While mapping and navigation protocols such as simultaneous localization and mapping (SLAM) are quite useful for navigation, they often require a preliminary mapping of the site, which is usually done manually. Waypoint generation for certain tasks, such as 3D scanning, cannot be done before obtaining said preliminary map, which can be tedious. Building information model (BIM) files contain rich semantic information about buildings; therefore, it is worth considering an approach where the information in BIM is leveraged to minimize the need for manual preliminary mapping of sites. This study proposes a methodology to get information from BIM—in the form of IFC files—to an autonomous robotic system (ARS) in the form of navigation maps, simulation environments, JSON files with useful semantic information, and proposed waypoints for stop-and-go missions. The schedule element present in IFC is used to generate obstacle maps relevant to the level of construction progress at the time the ARS is deployed. The results are validated with a case study of the entire process from the IFC file input to the waypoint generation for an ARS to complete a 3D reconstruction of an indoor space.

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Semantic Optimal Robot Navigation Using Building Information on Construction Sites

Cited by 4 publications

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Improving autonomous robotic navigation using IFC files

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