Vision-Based 3-D Localization of UAV Using Deep Image Matching

Unmanned aerial vehicles (UAVs) have revolutionized various industries by providing efficient and automated flight capabilities. However, reliance on GPS and traditional navigation systems poses challenges in scenarios where signal interference or failures occur. In this research, we present a novel computer-vision-based method to enhance UAV navigation, enabling accurate height and location estimation. Our approach utilizes a sophisticated network that leverages a pair of images to estimate UAV height. The pyramid stereo-matching network is employed to extract robust image features and generate a disparity map. Subsequently, a custom network processes and convolves these data, employing diverse computer vision techniques to achieve precise height estimation. To evaluate the effectiveness of our proposed method, we collected a comprehensive dataset by conducting flights with a Phantom 4 Pro drone over the NUST Main campus, H-12 Islamabad. The dataset encompasses images captured at 10 different heights, spanning from 100 to 280 m, with flights evenly spaced 20 m apart. In rigorous evaluations, our approach demonstrates promising results compared to existing methods. By liberating UAVs from reliance on GPS, this vision-based 3-D localization technique holds immense potential to ensure successful flights even in challenging environments.