利用云台控制的轻型固定翼无人机移动地形目标的实时视觉跟踪

Prashant Kumar, S. Sonkar, A. K. Ghosh, Deepu Philip
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引用次数: 5

摘要

近年来,无人驾驶飞行器(uav)在城市交通监控中的应用越来越广泛。然而,在固定翼无人机的实际实施过程中面临的挑战包括补偿设备振动、实时视频传输滞后、从地面控制站(GCS)向无人机发送命令时跟踪小物体尺寸,反之亦然。无人机在不同的飞行机动过程中不断变化的位置和方向导致跟踪目标从视野中消失。本文的主要目标是开发一种用于小型无人固定翼飞机实时目标跟踪和目标定位的框架控制算法。提出的算法处理两种场景,第一种场景在给定坐标上提供万向节指向,而第二种场景包含万向节指向,例如将光轴与图像平面上的某一点对齐。所提出的解决方案是将相机头在方位角和仰角上转向物体。建议的替代方案包括以方位角和仰角向目标旋转摄像机头,并根据摄像机与目标的距离、全球定位系统(GPS)和惯性测量单元(IMU)(摄像机定位和方向)信息计算出合适的角度。首先利用MATLAB/Simulink对该算法进行仿真,采用与实际飞机无人机相同的参数,并在固定翼无人机上进一步实现。结果表明了该算法的适用性和鲁棒目标跟踪的后续工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time vision-based tracking of a moving terrain target from Light Weight Fixed Wing UAV using gimbal control
In recent years, the use of unmanned aerial vehicles (UAVs) is widespread in urban traffic monitoring. However, the challenges faced while practical implementation from a fixed-wing UAV's involves compensating for the device vibration, real-time video transmission lag, small object sizes tracking while sending a command from Ground Control Station (GCS) to UAV and vice versa. The constantly changing position and orientation of UAV during different flight maneuvers lead to the loss of tracked objects from the field of view. The paper's primary objective is to develop a gimbal control algorithm for real-time object tracking and object location from a small-unmanned fixed-wing aircraft. The proposed algorithm deals with two scenarios, the first scenario offers gimbal pointing at a given coordinate, while second incorporates gimbal pointing such as to align the optical axis with a certain point in the image plane. The solution proposed is to turn the camera head in azimuth and elevation angles towards the object. The alternative suggested includes rotating the camera head in azimuth and elevation angles towards the object, and the suitable angles were calculated based on the camera's distance from the object, Global Positioning System (GPS) and Inertial Measurement Unit (IMU) (camera positioning and orientation) information. The algorithm was first simulated using MATLAB/Simulink using the same parameter of the actual aircraft UAV and further implemented in fixed-wing UAV. The result advocates the applicability of the algorithm and the future work to be done for robust object tracking.
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