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Visual navigation of the UAVs on the basis of 3D natural landmarks

机译:基于3D自然地标的无人机视觉导航

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This work considers the tracking of the UAV (unmanned aviation vehicle) on the basis of onboard observations of natural landmarks including azimuth and elevation angles. It is assumed that UAV's cameras are able to capture the angular position of reference points and to measure the angles of the sight line. Such measurements involve the real position of UAV in implicit form, and therefore some of nonlinear filters such as Extended Kalman filter (EKF) or others must be used in order to implement these measurements for UAV control. Recently it was shown that modified pseudomeasurement method may be used to control UAV on the basis of the observation of reference points assigned along the UAV path in advance. However, the use of such set of points needs the cumbersome recognition procedure with the huge volume of on-board memory. The natural landmarks serving as such reference points which may be determined on-line can significantly reduce the on-board memory and the computational difficulties. The principal difference of this work is the usage of the 3D reference points coordinates which permits to determine the position of the UAV more precisely and thereby to guide along the path with higher accuracy which is extremely important for successful performance of the autonomous missions. The article suggests the new RANSAC for ISOMETRY algorithm and the use of recently developed estimation and control algorithms for tracking of given reference path under external perturbation and noised angular measurements.
机译:这项工作基于对船上自然地标(包括方位角和仰角)的观察,考虑了对无人机(无人飞行器)的跟踪。假定无人机的摄像机能够捕获参考点的角位置并能够测量视线的角度。这种测量涉及隐式形式的无人机的实际位置,因此必须使用某些非线性滤波器(如扩展卡尔曼滤波器(EKF)或其他非线性滤波器)来实现用于无人机控制的这些测量。最近显示出,可以基于预先沿UAV路径分配的参考点的观察,使用改进的伪测量方法来控制UAV。然而,使用这样的点集需要繁琐的识别过程,并且具有大量的板载存储器。可以在线确定的用作此类参考点的自然界标可以显着减少板载内存和计算难度。这项工作的主要区别在于使用3D参考点坐标,该坐标允许更精确地确定无人机的位置,从而以更高的精度沿路径引导,这对于成功执行自主任务极为重要。本文提出了一种新的用于ISOMETRY的RANSAC算法,并建议使用最新开发的估计和控制算法来跟踪外部扰动和噪声角度测量下的给定参考路径。

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