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Research on Application of Computer Vision Assist Technology in High-precision UAV Navigation and Positioning

机译:计算机视觉辅助技术在高精度无人机导航与定位中的应用研究

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Traditional UAV human-computer interaction requires specialized equipment and professional training. Convenient and novel interaction methods are often more popular. Using ordinary cameras, the UAV gesture control system based on computer vision and deep learning is studied. Under this background, the paper proposes a new method for the navigation information extraction of UAV autonomous landing terminal based on monocular vision / INS integrated navigation; it only needs to detect two feature points on the runway and then combine the attitude of the inertial navigation system Angle information, you can directly calculate the position vector of the aircraft relative to the airport; because the straight line has obvious observability, for the feature points that are difficult to extract, you can detect the two runway edge lines and a landing threshold line, and then take the intersection of the threshold line and the edge of the runway is used as the feature point; the algorithm is deduced in detail based on the geometric principle of imaging. Finally, the landing experiment shows that the extraction and conversion of the method is feasible.
机译:传统的无人机人机交互需要专门的设备和专业的培训。方便而新颖的交互方法通常更受欢迎。使用普通相机,研究了基于计算机视觉和深度学习的无人机姿态控制系统。在此背景下,提出了一种基于单目视觉/ INS组合导航的无人机自主着陆终端导航信息提取新方法。只需要在跑道上检测两个特征点,然后结合惯性导航系统的姿态角度信息,就可以直接计算出飞机相对于机场的位置矢量;由于该直线具有明显的可观察性,对于难以提取的特征点,可以先检测出两条跑道边缘线和一条着陆阈值线,然后以该阈值线与跑道边缘的交点为准。特征点;根据成像的几何原理详细推导了该算法。最后,着陆实验表明该方法的提取和转换是可行的。

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