光纤传感手套实现高精度手部动作捕捉，克服遮挡与漂移问题。
Fiber Optic Sensing Glove for High Performance Dexterous Manipulation Capture
- 采用多芯形状传感光纤，精确捕获三维形变而非仅曲率。
- 在5名受试者上实现7.2毫米指尖定位误差，校准后降至4.9毫米。
- 适用于机器人高保真数据采集与双手远程操控，适合人机交互研究。
手部精细操作的姿态捕捉仍具挑战：基于视觉的方法在遮挡和复杂光照下表现下降，而传感器手套虽无遮挡问题，却易受漂移与电磁干扰影响，且极少达到动作捕捉的精度。本文提出一种光纤传感手套，用于全手姿态追踪，针对上述缺陷，采用多芯形状传感光纤，可捕捉每根光纤的完整3D形状，而非仅曲率。设计新型处理流程，将各重建的光纤形状注册到统一的手部参考坐标系，并通过新提出的逆运动学求解器，在60 Hz采样率下重建完整手部姿态。在包含5名受试者、历时2小时的精细物体操作任务数据集上进行基准测试，手套对动作捕捉真实值的平均指尖位置误差为7.2毫米，经一次工厂级光纤路由枢纽校准后，误差降低至4.9毫米。该能力支持高保真数据采集与双臂虚拟远程操作，对推动机器人技术发展至关重要。
原文摘要 · Abstract (English)
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.
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