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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep convolutional neural network based autonomous drone navigation


    Contributors:

    Conference:

    Thirteenth International Conference on Machine Vision ; 2020 ; Rome,Italy


    Published in:

    Proc. SPIE ; 11605


    Publication date :

    2021-01-04





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    AUTONOMOUS DRONE NAVIGATION BASED ON VISION

    FLISZAR DAVID G / PATTON RUSSELL DOUGLAS | European Patent Office | 2023

    Free access

    Autonomous drone navigation based on vision

    FLISZAR DAVID G / PATTON RUSSELL DOUGLAS | European Patent Office | 2025

    Free access

    Autonomous Systems: Autonomous Systems: Indoor Drone Navigation

    Iyer, Aswin / Narayan, Santosh / M, Naren et al. | ArXiv | 2023

    Free access


    Convolutional-Neural-Network-Based Autonomous Navigation of Hera Mission Around Didymos

    Kaluthantrige, Aurelio / Feng, Jinglang / Gil-Fernández, Jesús | AIAA | 2025