Vision guided landing of an unmanned air vehicle

In this paper, we study the problem of using computer vision as a sensor to control the landing of an unmanned air vehicle (UAV). The vision problem we address is a special case of the general ego-motion estimation problem due to the fact that all feature points lie on a plane. We propose a new geometric estimation scheme for solving the differential version of the planar ego-motion estimation problem. The algorithm is computationally inexpensive and amenable for real-time implementation. We present a performance evaluation of the algorithm under different levels of image measurement noise and camera motions relative to the landing pad. We also present a full dynamic model of a UAV, discuss a nonlinear controller based on differential flatness, and show through simulation that the vision guided UAV performs stable landing maneuvers even under large levels of image measurement noise.