Dense point feature generation algorithm based on monocular sequence images for depth measurement of unknown zone

It is essential to measure the flatness of an unknown zone for UAV landing in a complex terrain. Firstly, a depth calculation equation based on monocular sequence images is derived according to the pinhole imaging principle. Secondly, a dense point feature generation algorithm based on Delaunay triangulation is proposed to solve the problem that the large error of depth information reconstruction exists in sparse matching and the problem that high false match rate based on dense matching is high in the smooth region. Then, sub pixel Harris corner and scale invariant feature transform (SIFT) feature points are extracted and matched respectively in two frames which are selected from sequence images. After that, the two type feature points are fused under the conditions of Euclidean distance between them. So quasi dense feature points can be obtained. Finally, quasi dense feature points are Delaunay triangulated and dense feature points generating strategy is developed according to the variance of the three vertex pixel deviation in each triangulation triangle. Depth information of the whole unknown zone is calculated according to the proposed depth calculation equation. A simulation demonstration system is built by Vega Prime (VP) simulation and experimental results show that the relative depth measurement error of two objects whose height are 90 m and 55 m are less than 0.89% when the airborne camera is 400 m above the ground. The experimental results verify that the proposed algorithm has high accuracy.