用于高性能灵巧操作捕捉的光纤传感手套

中文摘要 AI 辅助

在灵巧操作过程中捕捉手部姿势仍然存在困难：基于视觉的方法在遮挡和具有挑战性的光照条件下性能下降，而传感器化手套虽不受遮挡影响，但容易出现漂移和磁干扰，且很少能达到动作捕捉的精度。我们推出了一款用于全手部姿势跟踪的光纤传感手套，旨在解决这些故障模式，使用多芯形状传感光纤来捕捉每根光纤的完整三维形状，而非仅曲率。一条新颖的流程将每根重构的光纤形状配准到通用手部参考坐标系，一款新的逆运动学求解器利用曲线约束以60Hz的频率重构全手部姿势。在包含5名受试者完成的2小时灵巧物体操作任务数据集上进行基准测试，该手套与动作捕捉真值相比，指尖位置平均误差为7.2毫米；通过对光纤路由集线器进行一次性工厂校准（该校准可在不同用户和会话间转移），误差降至4.9毫米。这些能力实现了高保真数据捕捉和双手虚拟遥操作，二者对推动机器人领域发展至关重要。

英文摘要

Capturing hand pose during dexterous manipulation remains difficult: vision-based methods degrade under occlusion and challenging lighting, while sensorized gloves, though occlusion-free, are prone to drift and magnetic interference and rarely match motion-capture accuracy. We introduce a fiber optic sensing glove for full hand pose tracking that targets these failure modes, using multi-core shape-sensing fibers that capture each fiber's full 3D shape rather than curvature alone. A novel pipeline registers each reconstructed fiber shape to a common hand reference frame, and a new inverse-kinematics solver reconstructs full hand pose at 60 Hz using curve constraints. Benchmarked on a 2-hour dataset of dexterous object manipulation tasks across 5 subjects, the glove achieves 7.2 mm mean fingertip position error against motion capture ground truth, reduced to 4.9 mm by a one-time factory calibration of the fiber routing hub that transfers across users and sessions. These capabilities enable high-fidelity data capture and bimanual virtual teleoperation - both essential to advancing the robotics field.

发表机构

• Meta
• Northwestern University（西北大学）

机构由 AI 辅助整理，请以论文原文为准。

相关深度报道

• Meta做了一只光纤传感手套，60Hz捕捉手势、指尖误差4.9毫米