Selection algorithm of random feature points based on vector constraints

The feature points of the extracted image are characterized by a large number and strong randomness when unmanned aerial vehicle (UAV) landing autonomously at an unknown zone by using vision. In order to overcome the problems that randomly selecting feature points for relative position and angle estimation leads to low precision estimation and poor stability, a selection algorithm of random feature points base on vector constraints is proposed. Firstly, geographic coordinates of the feature points are considered as an important factor which affects the equation precision through analyzing the position and attitude estimation equation. Secondly, the vector angle average degree, the mean of vector modulus and the maximum value of vector modulus, three kinds of constraint functions are introduced. And a selection strategy of random feature points based on vector constraints is developed. Finaly, the orthogonal iterative algorithm is used to evaluate the position and attitude estimation accuracy for the selected feature points. The experimental results show that the proposed algorithm has higher accuracy and stronger robustness compared to the method of randomly selecting feature points.