  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 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.
