Indoor positioning and navigation inside an area with no GPS-data availability is a challenging problem. There areapplications such as augmented reality, autonomous driving, navigation of drones inside tunnels, in which indoorpositioning gets crucial. In this paper, a tandem architecture of deep network-based systems, for the first time to ourknowledge, is developed to address this problem. This structure is trained on the scene images being obtained throughscanning of the desired area segments using photogrammetry. A CNN structure based on EfficientNet is trained as aclassifier of the scenes, followed by a MobileNet CNN structure which is trained to perform as a regressor. The proposedsystem achieves amazingly fine precisions for both Cartesian position and quaternion information of the camera.
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