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.

中文翻译：

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使用基于视觉的深度学习检测的 Drogue 型自主空中加油的实时导航

本文开发了一种深度学习对象检测器，以提供相对于飞行无人机上的单目相机的锥套的准确六自由度 (DoF) 信息。物体检测器有助于为自动驾驶汽车提供所需的信息，以便在无需人工干预的情况下停靠和加油。该物体检测器可以通过对模拟锥套的 8746 幅图像进行训练来检测八个不同的信标。一旦检测到这些信标，使用共线性方程作为系统模型的非线性最小二乘算法会在捕获的图像上获取信标的位置，以提供准确的六自由度导航解决方案。这些来自物体检测器的导航解决方案在多个指标上进行评估，然后与 VICON 运动跟踪系统提供的导航解决方案进行比较。最后，