Deep Convolutional Neural Network-Based Autonomous Drone Navigation
This addresses the problem of GPS-denied navigation for drone operators in applications like monitoring and delivery, though it is incremental as it builds on existing CNN methods.
The paper tackles autonomous drone navigation without GPS by using a deep CNN and regressor to output steering commands from visual input, achieving an average cross-track distance of less than 1.4 meters and mean waypoints minimum distance under 1 meter in simulation.
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.