  Indoor positioning aims at navigation inside areas with no GPS-data
availability and could be employed in many applications such as augmented
reality, autonomous driving specially inside closed areas and tunnels. In this
paper, a deep neural network-based architecture has been proposed to address
this problem. In this regard, a tandem set of convolutional neural networks, as
well as a Pix2Pix GAN network have been leveraged to perform as the scene
classifier, scene RGB image to point cloud converter, and position regressor,
respectively. The proposed architecture outperforms the previous works,
including our recent work, in the sense that it makes data generation task
easier and more robust against scene small variations, whilst the accuracy of
the positioning is remarkably well, for both Cartesian position and quaternion
information of the camera.
