BlobBoards: Robust Markers for Accurate Pose
Computer Vision and Pattern Recognition
Summary
The authors introduce BlobBoards, a new type of marker system made of many small blob patterns that help cameras find and identify objects and estimate their position and angle. BlobBoards work well even when the camera is far away or looking at the object from a steep angle, and they handle partially covered markers better than older systems. Compared to popular markers like AprilTag and ArUco, BlobBoards are more accurate, especially in measuring distance and angle, and detect markers more often, even when parts are hidden. Their approach uses learned features to match blobs and confirm identity and position reliably.
Authors
James Pritts, Till Sittart, Hendrik Sauer, Silja Janßen, Felix Seegräber, David Nakath, Kevin Köser
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.