发表机构
Meta（Meta）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对相机与动作捕捉标定部署后漂移且缺乏独立验证的问题，提出Lollypop参考靶标，耦合ArUco与动作捕捉标记，通过投影偏差验证标定，实验显示亚像素精度及对扰动的敏感性。
AI 中文摘要
相机到动作捕捉（mocap）标定对于在机器人、AR/VR以及其他计算机视觉任务中将mocap用作真值至关重要。然而，标定在部署后可能会发生漂移，而标定残差和视觉检查只能提供有限的独立验证。我们提出了Lollypop，一种用于独立标定验证的基准标记-动作捕捉参考靶标。该靶标将ArUco基准标记与动作捕捉标记星座相结合，使得视觉中心和跟踪质心表示同一物理点。给定候选标定，验证过程将动作捕捉点投影到图像中，并测量其与检测到的基准标记中心的偏差。实验表明，标定具有亚像素级标称误差、对受控外参扰动的敏感性，以及在示例性混合操作序列中误差逐渐增大的特性。
英文摘要
Camera-to-motion-capture (mocap) calibration is essential for using mocap as ground truth in robotics, AR/VR, and other computer vision tasks. However, the calibration can drift after deployment, while calibration residuals and visual inspection provide limited independent verification. We present Lollypop, a fiducial-mocap reference target for independent calibration verification. The target couples an ArUco fiducial with a mocap marker constellation so the visual center and tracked centroid represent the same physical point. Given a candidate calibration, verification projects the mocap point into the image and measures its disagreement with the detected fiducial center. Experiments show sub-pixel nominal error, sensitivity to controlled extrinsic perturbations, and increasing error during an illustrative mixed-handling sequence.
CommentsAccepted at IEEE SENSORS 2026. 4 pages, 3 figures
