BlobBoards: Robust Markers for Accurate Pose

2026-08-28

AI Summary

提出BlobBoards，一种基于高斯斑点和特征匹配的标记系统，用于提高姿态估计精度和鲁棒性，相较于现有方法显著降低了位移误差并提高了检测率。

Abstract

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.

Problem

• pose estimation
• detection rate
• occlusion robustness

Innovation

• Gaussian blobs
• pose estimation
• occlusion robustness
• multi-scale