用高密度多尺度高斯斑点实现精准定位，抗遮挡性能远超现有标记系统。
BlobBoards: Robust Markers for Accurate Pose
- 采用密集多尺度高斯斑点与特征匹配联合估计位置和身份
- 小板误差降低89%，大板误差降低70%，遮挡50%仍保持69%检测率
- 适合对精度和鲁棒性要求高的机器人视觉、AR应用
我们提出BlobBoards，一种由密集多尺度高斯斑点构成的标识标记系统，结合基于特征的联合检测、识别与位姿估计算法。每个板面通过数百个斑点特征注册，其密集空间分布有效约束位姿；多尺度设计确保在焦距、距离和倾斜角大幅变化时仍可检测。通过学习的局部描述符匹配参考图案并进行空间验证，对应关系确定位姿并确认身份。与动作捕捉真值对比，小板中位平移误差为3.6-5.0毫米，较AprilTag降低89%，大板降低70%；相比最先进标签系统，大幅减少大角度旋转失败。检测率达80%，高于AprilTag的74%和ArUco的58%，尤其在最小标记上优势显著。在50%遮挡下仍能检测69%的板面，中位平移误差基本不变，而AprilTag和ArUco完全失效。实验表明，BlobBoards在检测率、位姿精度和遮挡鲁棒性上均达到当前最优水平。
原文摘要 · Abstract (English)
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.
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