发表机构
Marine Data Science Kiel University; Kiel University; cs.uni-kiel.de（基尔大学海洋数据科学; 基尔大学; 基尔大学计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出BlobBoards基准标记系统，通过多尺度高斯斑点与特征流水线实现联合检测、识别与位姿估计，在检测率、位姿精度和遮挡鲁棒性上优于AprilTag、ArUco等现有系统。
AI 中文摘要
我们提出了BlobBoards，这是一种基准标记系统，包含密集的多尺度高斯斑点场以及用于联合检测、识别和位姿估计的基于特征的流水线。每个标记板由数百个斑点特征注册而成，这些特征的密集空间覆盖范围约束了位姿，而多尺度特性则在焦距、距离和倾斜度发生大幅变化时保持可检测性。学习到的局部描述符与参考模式匹配并经过空间验证，因此对应关系可确定位姿并确认身份。在与动作捕捉真值的对比中，BlobBoards的中位平移误差为3.6-5.0毫米，在小型标记板上将AprilTag的中位平移误差降低了89%，在大型标记板上降低了70%。与最先进的标记系统相比，它们还产生的大旋转失败案例少得多。BlobBoards实现了最高的检测率，为80%，而AprilTag为74%，ArUco为58%，在最小的标记上优势最大。在遮挡率低于50%的情况下，它们仍能检测到69%的标记板，且中位平移误差基本不变，而AprilTag和ArUco则无法检测到任何标记板。实验表明，BlobBoards在检测率、位姿精度和遮挡鲁棒性方面达到了最先进水平。
英文摘要
We propose BlobBoards, a fiducial marker system comprising a dense, multi-scale field of Gaussian blobs and a feature-based pipeline for joint detection, identification, and pose estimation. Each board is registered from hundreds of blob features whose dense spatial coverage constrains pose, while multiple scales preserve detectability across large changes in focal length, distance, and obliquity. Learned local descriptors are matched to the reference pattern and spatially verified, so the correspondences determine pose and certify identity. Against motion-capture ground truth, BlobBoards achieve median translation errors of 3.6-5.0 mm, reducing AprilTag's median translation error by 89% on small boards and 70% on large ones. They also produce far fewer large-rotation failures than state-of-the-art tag systems. BlobBoards achieve the highest detection rate, 80% versus 74% for AprilTag and 58% for ArUco, with the largest margin on the smallest markers. Under 50% occlusion, they still detect 69% of boards with essentially unchanged median translation error, while AprilTag and ArUco detect none. In experiments BlobBoards give state-of-the-art detection rate, pose accuracy and occlusion robustness.
