ICPS-net: an end-to-end RGB-based indoor camera positioning system using deep convolutional neural networks

Ali Ghofrani; Rahil Mahdian Toroghi; Seyed Mojtaba Tabatabaie

doi:10.1117/12.2559285

31 January 2020 ICPS-net: an end-to-end RGB-based indoor camera positioning system using deep convolutional neural networks

Ali Ghofrani, Rahil Mahdian Toroghi, Seyed Mojtaba Tabatabaie

Author Affiliations +

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143323 (2020) https://doi.org/10.1117/12.2559285
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

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

Citation Download Citation

Ali Ghofrani, Rahil Mahdian Toroghi, and Seyed Mojtaba Tabatabaie "ICPS-net: an end-to-end RGB-based indoor camera positioning system using deep convolutional neural networks", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143323 (31 January 2020); https://doi.org/10.1117/12.2559285

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