Vision-Based Approach Angle and Height Estimation for UAV Landing

Abstract

In order to estimate the approach angle and relative height of Unmanned Aircraft Vehicle (UAV) which lands autonomously, a combinational approach of monocular vision and stereo vision is presented. From monocular sequences, vanishing line is extracted by Hough transform and RANSAC algorithm, and then approach angle of UAV is calculated through vanishing line geometry. From stereo sequences, feature-based matching is adopted to gain depth information by extracting Harris corner. With gained approach angle, height of UAV is obtained by 3-D reconstruction. Kalman filter model is built to obtain accurate height by analyzing motion characteristic of UAV. Experimental results show that the proposed algorithm can effectively estimate the approach angle and height, and converge quickly.