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Indoor positioning aims at navigation inside areas with no GPS-dataavailability and could be employed in many applications such as augmentedreality, autonomous driving specially inside closed areas and tunnels. In thispaper, a deep neural network-based architecture has been proposed to addressthis problem. In this regard, a tandem set of convolutional neural networks, aswell as a Pix2Pix GAN network have been leveraged to perform as the sceneclassifier, 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 taskeasier and more robust against scene small variations, whilst the accuracy ofthe positioning is remarkably well, for both Cartesian position and quaternioninformation of the camera.

LiDAR ICPS-net: Indoor Camera Positioning based-on Generative Adversarial Network for RGB to Point-Cloud Translation