发表机构
Universidad de Córdoba（科尔多瓦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对无人机着陆依赖的视觉基准标记操作范围有限问题，提出递归ArUco标记设计，通过改进位采样策略实现任意深度递归、遮挡下稳健检测及多唯一标识符字典，使无人机机队可同时在指定位置着陆。
AI 中文摘要
无人机越来越依赖视觉基准标记进行自主导航和精确着陆。然而，标准标记的操作范围有限，相机太远或太近时都无法检测到。虽然已经提出了递归和分形标记来解决这个问题，但现有方法要么要求标记中心保持可见，易受遮挡影响，要么递归深度和放置受限。我们提出了一种新颖的递归ArUco标记设计。该方法可将任何标准基准标记转换为任意深度的递归标记。检测时采用改进的位采样策略，在父标记的黑白位中嵌入完整标记。此方法保证了无限递归深度和即使部分遮挡时的稳健检测，不依赖标记中心可见。此外，通过在所有递归尺度上保持单个唯一标识符，提供了多个唯一着陆垫的广泛字典。这使得无人机机队能够同时运行，每个无人机在指定位置着陆，而现有分形和Harco标记因结构和字典限制不支持此功能。
英文摘要
Unmanned Aerial Vehicles (UAVs) increasingly rely on visual fiducial markers for autonomous navigation and precision landing. However, standard markers suffer from limited operational ranges, becoming undetectable when the camera is either too far or too close. While recursive and fractal markers have been proposed to address this issue, existing approaches either require the marker's center to remain visible, making them vulnerable to occlusion, or are limited in their recursion depth and placement. We propose a novel Recursive ArUco marker design. Our method allows any standard fiducial marker to be transformed into a recursive marker with an arbitrary depth. By employing a modified bit-sampling strategy during detection, we embed complete markers within both the black and white bits of the parent marker. This approach guarantees unlimited recursion depth and robust detection even with partial occlusion, as it does not rely on the marker's center being visible. Furthermore, by maintaining a single, unique identifier across all recursive scales, our proposal provides an extensive dictionary of multiple unique landing pads. This capability allows fleets of UAVs to operate simultaneously, with each drone landing at its designated location -- a feature not supported by existing Fractal and Harco markers due to their structural and dictionary constraints.
