A New Navigation System for Unmanned Aerial Vehicles in Global Positioning System-Denied Environments Based On Image Registration with Mutual Information and Deep Learning

Cagla Sahin and Imam Samil Yetik

Abstract: In this paper, we develop an alternative navigation system for Unmanned Aerial Vehicle (UAV) in Global Positioning Systems (GPS)-denied environment. We use two image inputs, one is acquired with an on-board camera placed on the UAV (which is the large-area image) and the other is from satellite images (which is small known image) with GPS information. We use a convolutional neural network (CNN) architecture based on Oxford’s Visual Geometry Group network (VGG-16) and utilize normalized variant mutual information between these two images to obtain position of the UAV. Satellite images are labelled and given to the UAV. When GPS information is lost, our algorithm starts to function and images from UAV camera are searched whether satellite image is seen by cameras on UAV image or not. If the UAV is in that area, our algorithm finds the GPS information from satellite image data.
Published in: Proceedings of the 2020 International Technical Meeting of The Institute of Navigation
January 21 - 24, 2020
Hyatt Regency Mission Bay
San Diego, California
Pages: 1127 - 1134
Cite this article: Sahin, Cagla, Yetik, Imam Samil, "A New Navigation System for Unmanned Aerial Vehicles in Global Positioning System-Denied Environments Based On Image Registration with Mutual Information and Deep Learning," Proceedings of the 2020 International Technical Meeting of The Institute of Navigation, San Diego, California, January 2020, pp. 1127-1134. https://doi.org/10.33012/2020.17203
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