A UAV Image Stitching Method for Complex Urban Environments

Abstract. To address the issues of uneven feature point distribution, environmental interference, and insufficient real-time performance in UAV image stitching in complex urban environments, this paper proposes an improved ORB algorithm based on Gaussian scale-space optimization and dynamic grid division, combined with a global geometric consistency optimization strategy. First, local adaptive noise filtering and bilateral filtering are applied to enhance image quality. Then, multi-scale feature detection is achieved using a Gaussian scale-space pyramid, and dynamic grid division is employed to balance feature point distribution. Finally, a global energy function, including reprojection error and smoothness constraints, is constructed to iteratively optimize the homography matrix and suppress stitching distortions. Experimental results show that the proposed method achieves high processing speed on low-performance hardware platforms, improves feature point distribution uniformity to 0.89, and achieves stitching accuracy (RMSE) of 3.5 pixels, significantly outperforming ORB and SIFT algorithms, while remaining robust in dynamic occlusion and lighting variation scenarios. This method provides a lightweight and efficient solution for UAV image stitching in urban environments, supporting applications such as urban planning and disaster assessment. Future work will explore lightweight deep learning integration and edge computing acceleration to further improve dynamic scene adaptability.