Deep convolutional neural network based autonomous drone navigation

Karim Amer; Mohamed Samy; Mahmoud Shaker; Mohamed ElHelw

doi:10.1117/12.2587105

4 January 2021 Deep convolutional neural network based autonomous drone navigation

Karim Amer, Mohamed Samy, Mahmoud Shaker, Mohamed ElHelw

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 1160503 (2021) https://doi.org/10.1117/12.2587105
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

This paper presents a novel approach for aerial drone autonomous navigation along predetermined paths using only visual input form an onboard camera and without reliance on a Global Positioning System (GPS). It is based on using a deep Convolutional Neural Network (CNN) combined with a regressor to output the drone steering commands. Furthermore, multiple auxiliary navigation paths that form a ‘navigation envelope’ are used for data augmentation to make the system adaptable to real-life deployment scenarios. The approach is suitable for automating drone navigation in applications that exhibit regular trips or visits to same locations such as environmental and desertification monitoring, parcel/aid delivery and drone-based wireless internet delivery. In this case, the proposed algorithm replaces human operators, enhances accuracy of GPS-based map navigation, alleviates problems related to GPS-spoofing and enables navigation in GPS-denied environments. Our system is tested in two scenarios using the Unreal Engine-based AirSim [32] plugin for drone simulation with promising results of average cross track distance less than 1.4 meters and mean waypoints minimum distance of less than 1 meter.

Citation Download Citation

Karim Amer, Mohamed Samy, Mahmoud Shaker, and Mohamed ElHelw "Deep convolutional neural network based autonomous drone navigation", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 1160503 (4 January 2021); https://doi.org/10.1117/12.2587105

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