Abstract
The application of the drone in construction monitoring, anti-terrorism investigation, and emergency response in large buildings is the development trend of indoor navigation in the future. At present, the indoor three-dimensional (3D) map model based on Building Information Modeling (BIM) is mainly used for indoor pathfinding for pedestrians and ground mobile devices. However, it lacks the spatial description of the vertical dimension, which cannot meet the needs of the navigation application of an indoor drone. Therefore, we propose a pathfinding method for an indoor drone based on a BIM-semantic model. First, the semantic and geometric information in the BIM model is extracted and mapped to voxels to generate an indoor 3D map model called BI3DM. The model subdivides voxel types, which are suitable for subsequent pathfinding for a drone and even pedestrian. Then, a BI3DM-based pathfinding algorithm for an indoor drone is proposed. The algorithm optimizes the path for starting and target locations near the ground, and imports the drone size into the algorithm, which can obtain a more reasonable path. Finally, three types of BIM models are experimented with to verify the effectiveness of the study and its compatibility with pedestrians and ground mobile devices.
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