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The research of unmanned aerial vehicles’(UAVs’)autonomy navigation and landing guidance with computer vision has important signifcance.However,because of the image blurring,the position of the cooperative points cannot be obtained accurately,and the pose estimation algorithms based on the feature points have low precision.In this research,the pose estimation algorithm of UAV is proposed based on feature lines of the cooperative object for autonomous landing.This method uses the actual shape of the cooperative-target on ground and the principle of vanishing line.Roll angle is calculated from the vanishing line.Yaw angle is calculated from the location of the target in the image.Finally,the remaining extrinsic parameters are calculated by the coordinates transformation.Experimental results show that the pose estimation algorithm based on line feature has a higher precision and is more reliable than the pose estimation algorithm based on points feature.Moreover,the error of the algorithm we proposed is small enough when the UAV is near to the landing strip,and it can meet the basic requirements of UAV’s autonomous landing.
The research of unmanned aerial vehicles ’(UAVs’) autonomy navigation and landing guidance with computer vision has important signifcance. However, because of the image blurring, the position of the cooperative points can not be accurately, and the pose estimation algorithms based on the feature points have low precision. this research, the pose estimation algorithm of UAV is proposed based on feature lines of the cooperative object for autonomous landing. This method uses the actual shape of the cooperative-target on ground and the principle of vanishing line. Roll angle is calculated from the vanishing line. Yaw angle is calculated from the location of the target in the image. Finally, the remaining extrinsic parameters are calculated by the coordinates transformation. Experimental results show that the pose estimation algorithm based on line feature has a higher precision and is more reliable than the pose estimation algorithm based on points feature. Moreover, the error of the algor ithm we proposed is small enough when the UAV is near to the landing strip, and it can meet the basic requirements of UAV’s autonomous landing.