Exploring Deep Learning-Based Visual Localization Techniques for UAVs in GPS-Denied Environments

• Published in: IEEE access Vol. 12; pp. 113049 - 113071
• Main Authors: Al-Jarrah, Omar Y., Shatnawi, Ahmed S., Shurman, Mohammad M., Ramadan, Omar A., Muhaidat, Sami
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Autonomous aerial vehicles; Deep learning; Global navigation satellite system; Global positioning systems; GPS; GPS-denied; Localization; Location awareness; Navigation; Navigation systems; Reviews; Satellite navigation systems; Signal reflection; Spoofing; Template matching; UAVs; Unmanned aerial vehicles; visual localization; Visualization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3440064

Unmanned Aerial Vehicles (UAVs) have proliferated across diverse domains. However, optimal UAV operations necessitate precise and reliable navigation systems. UAVs predominantly rely on the Global Navigation Satellite System (GNSS), such as the Global Positioning System (GPS), for navigation. Nevertheless, GNSS signals are susceptible to blockage, reflection, and spoofing, introducing significant risks, including navigation loss and potential UAV loss. This research investigates cutting-edge navigation solutions, emphasizing deep learning-based visual localization approaches tailored for UAVs. Our focus is on scenarios characterized by GPS-denied environments where GPS signals may be absent or unreliable. We provide a comprehensive review of contemporary deep learning-based visual localization approaches and compare them to traditional aerial visual localization methods, such as template matching and feature matching. This comparison highlights both the potential benefits and challenges associated with these approaches. Furthermore, we systematically evaluate and classify recent deep learning-based methods based on main criteria, including model type/architecture, reference imagery, operational context, and resultant accuracy levels. Our findings underscore the substantial promise inherent in various approaches while also shedding light on their unique deployment challenges. Finally, we discuss potential research directions, to inspire further innovations and progress in this domain. The ultimate goal is to develop more accurate, dependable, and secure navigation solutions for UAVs.