Recent years have seen tremendous advancements in the area of autonomous payload delivery via unmanned aerial vehicles, or drones. However, most of these works involve delivering the payload at a predetermined location using its GPS coordinates. By relying on GPS coordinates for navigation, the precision of payload delivery is restricted to the accuracy of the GPS network and the availability and strength of the GPS connection, which may be severely restricted by the weather condition at the time and place of operation. In this work we describe the development of a micro-class UAV and propose a novel navigation method that improves the accuracy of conventional navigation methods by incorporating a deep-learning-based computer vision approach to identify and precisely align the UAV with a target marked at the payload delivery position. This proposed method achieves a 500% increase in average horizontal precision over conventional GPS-based approaches.

翻译：近年来，无人机在自主载荷投递领域取得了巨大进展。然而，现有研究大多依赖全球定位系统（GPS）坐标在预定位置完成投递。这种基于GPS坐标的导航方式，其投递精度受限于GPS网络的精度、GPS连接的可用性及信号强度，而后者可能因作业时地点的天气条件而严重受限。本文研制了一种微型无人机，并提出了一种新型导航方法：通过融合基于深度学习的计算机视觉方法，识别并精确定位投递位置的目标标记，从而提升传统导航方法的精度。实验表明，与传统GPS方法相比，该方法的平均水平精度提高了500%。