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SUAVE: Integrating UAV video using a 3D model

机译:SUAVE:使用3D模型集成无人机视频

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摘要

Controlling a team of Unmanned Aerial Vehicles (UAV) requires the operator to perform continuous surveillance and path planning. The operator's situation awareness is likely to degrade as an increasing number of surveillance videos must be viewed and integrated. The Picture-in-Picture display (PiP) provides one solution for integrating multiple UAV camera video by allowing the operator to view the video feed in the context of surrounding terrain. The experimental SUAVE (Simple Unmanned Areal Vehicle Environment) display extends PiP methods by sampling imagery from the video stream to texture a 3D map of the terrain. The operator can then inspect this imagery using world in miniature (WIM) or fly-through methods. We investigate the properties and advantages of SUAVE in the context of a search mission with 11 UAVs finding a strong advantage for finding targets While performance is expected to improve with increasing numbers of UAVs we did not find differences in performance between models generated by 11 UAVs and those employing 22 UAVs.
机译:控制一支无人驾驶飞行器(UAV)的团队要求运营商进行连续监控和路径规划。运营商的情况意识可能会降低,因为必须查看和整合越来越多的监视视频。图片中的图片显示(PIP)提供一个解决方案,用于通过允许操作员在周围地形的上下文中查看视频源来集成多个UV摄像机视频。实验Suave(简单的无人驾驶员环境)显示通过从视频流中采样图像来延伸PIP方法,以纹理地形的3D地图。然后,操作员可以使用微型(WIM)或飞行方法的世界检查此图像。我们调查Suave在搜索任务的背景下的性质和优势,11个无人机在寻找目标的强大优势时,在越来越多的无人机越来越多的情况下,我们没有发现11个UVS生成的模型之间的性能差异。那些雇用22个无人机的人。

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