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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Real-Time Navigation for Drogue-Type Autonomous Aerial Refueling Using Vision-Based Deep Learning Detection
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Real-Time Navigation for Drogue-Type Autonomous Aerial Refueling Using Vision-Based Deep Learning Detection

机译：基于视觉的深度学习检测，滴灌型自治空气加油的实时导航

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摘要

This article develops a deep learning object detector to provide accurate six-degree-of-freedom (DoF) information of the drogue relative to a monocular camera onboard a flying unmanned aerial vehicle. An object detector helps to provide the needed information for an autonomous vehicle to dock and refuel without the need for human intervention. This object detector can detect eight different beacons by training on 8746 images of a mock drogue. Once these beacons were detected, a nonlinear least squares algorithm that uses the collinearity equations as a system model takes the beacon's location on the captured image to provide an accurate six-DoF navigation solution. These navigation solutions from the object detector were evaluated on multiple metrics and then compared to navigation solutions provided by a VICON motion tracking system. Finally, Monte Carlo analysis, using the collinearity equations as a system model, evaluates an object detector's performance with various noise levels.
机译：本文开发了深度学习对象探测器，以便在飞行无人驾驶车辆上的单眼摄像机上提供精确的六维自由度（DOF）信息。 对象检测器有助于为自主车辆提供所需的信息，以便在不需要人类干预的情况下停靠和加油。 该对象探测器可以通过培训在8746个模拟醉酒的图像上训练来检测八种不同的信标。 一旦检测到这些信标，就像系统模型一样使用共线性方程的非线性最小二乘算法在捕获的图像上采用信标的位置，以提供精确的六进度导航解决方案。 对象检测器的这些导航解决方案在多个度量上进行评估，然后与由VICON运动跟踪系统提供的导航解决方案进行比较。 最后，Monte Carlo分析使用Conslinity方程作为系统模型，评估对象检测器的性能，具有各种噪声水平。

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