In this paper, a position measurement system, including drogue’s landmark detection and position computation for autonomous aerial refueling of unmanned aerial vehicles, is proposed. A multitask parallel deep convolution neural network (MPDCNN) is designed to detect the landmarks of the drogue target. In MPDCNN, two parallel convolution networks are used, and a fusion mechanism is proposed to accomplish the effective fusion of the drogue’s two salient parts’ landmark detection. Considering the drogue target’s geometric constraints, a position measurement method based on monocular vision is proposed. An effective fusion strategy, which fuses the measurement results of drogue’s different parts, is proposed to achieve robust position measurement. The error of landmark detection with the proposed method is 3.9%, and it is obviously lower than the errors of other methods. Experimental results on the two KUKA robots platform verify the effectiveness and robustness of the proposed position measurement system for aerial refueling.

中文翻译：

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基于单目视觉的无人机自主空中加油鲁棒地标检测和位置测量

本文提出了一种位置测量系统，包括锥管的路标检测和用于无人机自动空中加油的位置计算。设计了多任务并行深度卷积神经网络（MPDCNN）以检测锥靶的标志。在MPDCNN中，使用了两个并行的卷积网络，并提出了一种融合机制来完成锥子两个显着部分的界标检测的有效融合。考虑到锥靶的几何约束，提出了一种基于单眼视觉的位置测量方法。为了实现鲁棒的位置测量，提出了一种有效的融合策略，该策略融合了锥子不同部分的测量结果。提出的方法进行地标检测的误差为3.9％，明显低于其他方法的误差。在两个KUKA机器人平台上的实验结果验证了所提出的空中加油位置测量系统的有效性和鲁棒性。