Precise Depth Estimation by Calculating Affine Transformation Parameters

When estimating depth from unmanned aerial vehicle (UAV) consecutive frames, usually only narrow baseline stereo is satisfied. So, the estimation needs a higher image matching accuracy often not achieved in real applications. To tackle this problem, we propose a precise and robust depth estimation scheme: first, by multiview geometry theory, we prove that two corresponding local patches in two consecutive frames satisfy an affine transformation no matter how high the UAV flies and whether it is parallel to the ground or not; second, normal curvatures, local Fourier moment constraints, and spatial smoothness constraints are combined to calculate the affine transformation matrix, and the stepwise least-squares fitting-based PC (SLSF-PC) method is used to calculate translation vectors because it can suppress the illumination effect; third, the normal and the position of the 3-D patch corresponding to two image patches are calculated by the obtained affine transformation matrix and translation vector. The method is very precise and robust due to the combination of 2-D image features and 3-D differential geometry characters such as curvatures and normals. The developed method is tested using real UAV images and images of scale model scenes, confirming the superior performance of the proposed method against the state-of-the-art.