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A hierarchical vision-based localization of rotor unmanned aerial vehicles for autonomous landing

机译：基于分层视觉的用于自主着陆的旋翼无人机定位

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摘要

The vision-based localization of rotor unmanned aerial vehicles for autonomous landing is challenging because of the limited detection range. In this article, to extend the vision detection and measurement range, a hierarchical vision-based localization method is proposed for unmanned aerial vehicle autonomous landing. In such a hierarchical framework, the landing is defined into three phases: “Approaching,”“Adjustment,” and “Touchdown,” in which visual artificial features at different scales can be detected from the designed object pattern for unmanned aerial vehicle pose recovery. The corresponding feature detection and pose estimation algorithms are also presented. In the end, typical simulation and field experiments have been carried out to illustrate the proposed method. The results show that our hierarchical vision-based localization has the ability to a consecutive unmanned aerial vehicle localization in a wider working range from far to near, which is significant for autonomous landing.
机译：由于检测范围有限，用于自主降落的旋翼无人机的基于视觉的定位具有挑战性。在本文中，为了扩展视觉检测和测量范围，提出了一种基于分层视觉的无人机自动着陆定位方法。在这样的分级框架中，着陆被定义为三个阶段：“接近”，“调整”和“着陆”，其中可以从设计的物体模式中检测出不同比例的视觉人工特征，以进行无人机姿态恢复。还介绍了相应的特征检测和姿态估计算法。最后，通过典型的仿真和现场实验对所提出的方法进行了说明。结果表明，基于分层视觉的定位具有在从远到近的较宽工作范围内连续进行无人机定位的能力，这对于自主着陆非常重要。

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