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无人机安全着陆是无人机研究的重点和难点,为了提高无人机在公路等简易机场着陆的安全性,提出了基于少量任意分布特征点估计无人机姿态和位置的计算机视觉算法。解算无人机着陆过程中相对跑道的位置和姿态是文章核心;首先利用N点算法解算出特征点在摄像机坐标系上的坐标,然后利用正交化算法求出摄像机坐标系和跑道坐标系之间的旋转矩阵和平移向量;针对特征点的成像过程容易受噪声影响的情况,引入了最小中值法,减小噪声的影响,提高算法的鲁棒性,并解算出姿态和位置;通过卡尔曼滤波方法,进一步提高位置和姿态的精度。仿真结果表明:所提算法满足无人机自主着陆的精度要求。
In order to improve the security of UAVs landing in airstrips and other airstrips, a computer vision algorithm based on a small number of randomly distributed feature points is proposed to estimate the attitude and position of UAVs. It is the core of the article to solve the position and attitude of the relative runway during the landing of the UAV. Firstly, the coordinates of the feature points on the camera coordinate system are calculated by using the N-point algorithm. Then, the camera coordinate system and the runway coordinate system The rotation matrix and the translation vector between the feature points are easily affected by the noise. The minimum median method is introduced to reduce the influence of noise, improve the robustness of the algorithm and solve the pose and position. Kalman filter method to further improve the accuracy of position and attitude. The simulation results show that the proposed algorithm meets the accuracy requirement of UAV autonomous landing.