In recent years, unmanned aerial vehicles (UAVs), commonly known as drones, have gained increasing interest in both academia and industries. The evolution of UAV technologies, such as artificial intelligence, component miniaturization, and computer vision, has decreased their cost and increased availability for diverse applications and services. Remarkably, the integration of computer vision with UAVs provides cutting-edge technology for visual navigation, localization, and obstacle avoidance, making them capable of autonomous operations. However, their limited capacity for autonomous navigation makes them unsuitable for global positioning system (GPS)-blind environments. Recently, vision-based approaches that use cheaper and more flexible visual sensors have shown considerable advantages in UAV navigation owing to the rapid development of computer vision. Visual localization and mapping, obstacle avoidance, and path planning are essential components of visual navigation. The goal of this study was to provide a comprehensive review of vision-based UAV navigation techniques. Existing techniques have been categorized and extensively reviewed with regard to their capabilities and characteristics. Then, they are qualitatively compared in terms of various aspects. We have also discussed open issues and research challenges in the design and implementation of vision-based navigation techniques for UAVs.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Vision-Based Navigation Techniques for Unmanned Aerial Vehicles: Review and Challenges


    Beteiligte:


    Erscheinungsdatum :

    2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Vision-based navigation of unmanned aerial vehicles

    Courbon, Jonathan / Mezouar, Youcef / Guenard, Nicolas et al. | Tema Archiv | 2010



    A vision-based navigation system for Unmanned Aerial Vehicles (UAVs)

    Al-Kaff, Abdulla Hussein / Armingol Moreno, José María / Escalera Hueso, Arturo de la | BASE | 2019

    Freier Zugriff