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New Approaches to the Integration of Navigation Systems for Autonomous Unmanned Vehicles (UAV)

机译：集成无人驾驶车辆（UAV）导航系统的新方法

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The article presents an overview of the theoretical and experimental work related to unmanned aerial vehicles (UAVs) motion parameters estimation based on the integration of video measurements obtained by the on-board optoelectronic camera and data from the UAV’s own inertial navigation system (INS). The use of various approaches described in the literature which show good characteristics in computer simulations or in fairly simple conditions close to laboratory ones demonstrates the sufficient complexity of the problems associated with adaption of camera parameters to the changing conditions of a real flight. In our experiments, we used computer simulation methods applying them to the real images and processing methods of videos obtained during real flights. For example, it was noted that the use of images that are very different in scale and in the aspect angle from the observed images in flight makes it very difficult to use the methodology of singular points. At the same time, the matching of the observed and reference images using rectilinear segments, such as images of road sections and the walls of the buildings look quite promising. In addition, in our experiments we used the projective transformation matrix computation from frame to frame, which together with the filtering estimates for the coordinate and angular velocities provides additional possibilities for estimating the UAV position. Data on the UAV position determining based on the methods of video navigation obtained during real flights are presented. New approaches to video navigation obtained using the methods of conjugation rectilinear segments, characteristic curvilinear elements and segmentation of textured and colored regions are demonstrated. Also the application of the method of calculating projective transformations from frame-to-frame is shown which gives estimates of the displacements and rotations of the apparatus and thereby serves to the UAV position estimation by filtering. Thus, the aim of the work was to analyze various approaches to UAV navigation using video data as an additional source of information about the position and velocity of the vehicle.
机译：这篇文章概述了与基于无人机光电运动摄像机的视频测量结果和来自无人机自身惯性导航系统（INS）的数据相结合的无人飞行器（UAV）运动参数估计的理论和实验工作。文献中描述的各种方法的使用在计算机仿真中或在接近实验室的相当简单的条件下显示出良好的特性，这证明了与使摄像机参数适应实际飞行的变化条件相关的问题足够复杂。在我们的实验中，我们使用了将计算机模拟方法应用于真实图像的方法以及在真实飞行过程中获得的视频的处理方法。例如，注意到，与在飞行中观察到的图像在比例和纵横比上非常不同的图像的使用使得非常难于使用奇异点的方法。同时，使用直线段对观测图像和参考图像进行匹配，例如道路断面和建筑物墙壁的图像，看起来很有希望。另外，在我们的实验中，我们使用了逐帧的投影变换矩阵计算，它与坐标和角速度的滤波估计一起为估计无人机位置提供了更多的可能性。介绍了基于在真实飞行中获得的视频导航方法确定的无人机位置数据。演示了使用共轭直线段，特征曲线元素以及带纹理和彩色区域分割方法获得的视频导航新方法。还示出了计算从帧到帧的投影变换的方法的应用，该方法给出了设备的位移和旋转的估计，从而通过过滤用于UAV位置估计。因此，这项工作的目的是分析使用视频数据作为有关车辆位置和速度的附加信息源的无人机导航的各种方法。

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