无需额外传感器，实时精准追踪眼球方向以控制眼科手术机器人
Stable Tracking of Eye Gaze Direction During Ophthalmic Surgery
- 融合机器学习与传统算法，不依赖面部特征点实现眼球定位
- 眼球朝向估计平均误差仅0.58度，机械臂控制误差2.08度
- 适用于光照变化大、遮挡多的复杂手术场景，适合眼科手术机器人应用
眼科手术机器人通过消除医生自然手抖，提升稳定性和精度，可在狭小空间内完成精细操作。尽管视觉和力反馈控制技术已取得进展，术前导航仍高度依赖手动操作，影响一致性并增加不确定性。现有眼动估计算法（无论是传统还是基于深度学习的方法）普遍存在对额外传感器依赖、手术环境中遮挡问题以及面部检测需求等挑战。为此，本研究提出一种创新的眼球定位与追踪方法，结合机器学习与传统算法，无需特征点即可在不同光照和阴影条件下稳定检测虹膜并估计视线方向。大量真实场景实验表明，该方法在眼球朝向估计上的平均误差为0.58度，基于该估计的机械臂运动控制平均误差为2.08度。
原文摘要 · Abstract (English)
Ophthalmic surgical robots offer superior stability and precision by reducing the natural hand tremors of human surgeons, enabling delicate operations in confined surgical spaces. Despite the advancements in developing vision- and force-based control methods for surgical robots, preoperative navigation remains heavily reliant on manual operation, limiting the consistency and increasing the uncertainty. Existing eye gaze estimation techniques in the surgery, whether traditional or deep learning-based, face challenges including dependence on additional sensors, occlusion issues in surgical environments, and the requirement for facial detection. To address these limitations, this study proposes an innovative eye localization and tracking method that combines machine learning with traditional algorithms, eliminating the requirements of landmarks and maintaining stable iris detection and gaze estimation under varying lighting and shadow conditions. Extensive real-world experiment results show that our proposed method has an average estimation error of 0.58 degrees for eye orientation estimation and 2.08-degree average control error for the robotic arm's movement based on the calculated orientation.
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