An augmented reality facet mapping technique for ray tracing applications.
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This research presents a novel spatial mapping technique that is capable of extracting the vector map of an indoor environment based on the images captured from a smartphone camera and the spatial maps captured from the Microsoft HoloLens. The extracted vector map follows the facet model concept and can be used as input in ray tracing algorithm. The ray tracing algorithm is used for visualizing and predicting the indoor wireless channels. The proposed solution offers three different algorithms, the first algorithm (Low cost 2D image to facet model algorithm) uses the edge and corner detection algorithms to compute the coordinates of the walls and doors of the indoor environment. The second algorithm (Minimum- maximum algorithm) computes the spatial map corner vertices by using the data processing techniques. The third algorithm (Spatial understanding algorithm) uses the Microsoft HoloLens Toolkit’s “spatial understanding” feature to compute the spatial maps for detecting and measuring the individual wall dimensions. Finally, using the corner coordinates, spatial corner vertices and individual wall dimensions from all the three algorithms, a simple 3D vector map is designed. The output of all the algorithms is a facet model that can be used by ray tracing algorithms which are embedded in Augmented Reality (AR) applications. The overall process provides a better human-to-network interface and an improved user experience that is expected to provide a new way for indoor network planning of residential 5G systems.