Landmark recognition for UAV autonomous landing based on vision
基于视觉的无人机自主着陆地标识别方法

In the traditional method of recognizing the landmark during the process of UAV autonomous landing, conformed the threshold through lots of experiments. In order to solve this problem, this paper studied a kind of method based on affine invariant moments and SVM classifier. First of all, it designed a new landmark combined with 6 circles. Second, considering the fact that the UAV in flight could take distorted landmark images, it extracted the affine invariant moments as features. Finally, it put affine invariant moments into SVM classifier to complete landmark recognition. It compared the proposed method with Hu invariant moment and BP neural network. The experimental results show that combination of affine invariant moment and SVM classifier improve the accuracy and decrease test time of UAV landing landmark classification. Therefore, the classification method based on affine invariant moments and SVM classifier has a certain degree of practicality in the landmark recognition.