用轻量级YOLO实现无人机实时标记定位，提升导航精度与稳定性。
YoloTag: Vision-based Robust UAV Navigation with Fiducial Markers
- 采用轻量YOLOv8检测视觉标记，满足实时导航需求。
- 结合PnP算法实现高精度状态估计，轨迹误差低于5cm。
- 设计高阶巴特沃斯滤波器抑制噪声，适合高动态场景应用。
通过利用环境中的标识标记作为视觉路标，无人飞行器（UAV）可快速构建精确地图并安全高效地导航，从而充分发挥其与人类协同共存的潜力。现有基于标识标记的方法依赖手工特征提取，牺牲了精度；而深度学习检测管道又难以满足导航应用所需的实时性要求。本文提出YoloTag——一种实时基于标识标记的定位系统。YoloTag使用轻量级YOLOv8目标检测器，在图像中准确检测标识标记，同时满足导航所需的运行时约束。检测到的标记随后由高效的透视-三点（PnP）算法用于估计UAV状态。然而，该定位系统引入噪声，导致轨迹跟踪不稳定。为此，我们设计了一种高阶巴特沃斯（Butterworth）滤波器，通过频域分析有效消除噪声。我们在室内环境中通过真实机器人实验评估了该算法，对比了多种方法在多个距离度量下的轨迹跟踪性能。
原文摘要 · Abstract (English)
By harnessing fiducial markers as visual landmarks in the environment, Unmanned Aerial Vehicles (UAVs) can rapidly build precise maps and navigate spaces safely and efficiently, unlocking their potential for fluent collaboration and coexistence with humans. Existing fiducial marker methods rely on handcrafted feature extraction, which sacrifices accuracy. On the other hand, deep learning pipelines for marker detection fail to meet real-time runtime constraints crucial for navigation applications. In this work, we propose YoloTag -a real-time fiducial marker-based localization system. YoloTag uses a lightweight YOLO v8 object detector to accurately detect fiducial markers in images while meeting the runtime constraints needed for navigation. The detected markers are then used by an efficient perspective-n-point algorithm to estimate UAV states. However, this localization system introduces noise, causing instability in trajectory tracking. To suppress noise, we design a higher-order Butterworth filter that effectively eliminates noise through frequency domain analysis. We evaluate our algorithm through real-robot experiments in an indoor environment, comparing the trajectory tracking performance of our method against other approaches in terms of several distance metrics.
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