Abstract Machine-learning-based navigation systems have become crucial for unmanned aerial vehicles (UAVs), particularly in environments affected by signal jamming and laser pointing. Traditional sensors such as global navigation satellite system sensors, lasers, and radar provide essential navigation data but have limitations, including the need for continuous pointing and vulnerability to interference. Advancements in imaging technology have rendered cameras a viable alternative. This article presents a real-time navigation solution for high-speed UAVs that minimize disruption from environmental dynamics. Key methodologies include (i) creating a virtual environment for model training, (ii) extracting UAV flight dynamics for simulation, (iii) applying and validating image-matching techniques, and (iv) developing a visual tracking algorithm for navigation between successive image-matching steps. The approach utilizes UAV-captured images alongside satellite images of the flight region. Experimental results and analyses demonstrate the effectiveness of various image-matching methods and visual tracking algorithms, showcasing the capabilities of the proposed solution compared with available techniques.
Güven et al. (Tue,) studied this question.
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