实时校准达芬奇机器人工具定位，提升手术安全性和精度。
On-the-fly hand-eye calibration for the da Vinci surgical robot
- 通过在线计算手眼变换矩阵，实现无需预训练的精准工具定位。
- 在多种光照和测量误差条件下，工具定位误差显著降低。
- 适用于不同手术场景，比现有方法更快且兼容性强。
在机器人辅助微创手术（RMIS）中，精确的工具定位对患者安全和任务成功至关重要。然而，对于缆绳驱动的机器人（如达芬奇机器人），编码器读数错误会导致位姿估计偏差，带来挑战。本文提出一种在线校准框架，通过实时计算手眼变换矩阵实现高精度工具定位。该框架包含两个相互关联的模块：特征关联模块可在无预训练的情况下从单目图像中鲁棒地匹配关键点；手眼校准模块则采用多种滤波方法，适应不同手术场景。我们在公开视频数据集上进行了广泛验证，涵盖体外与离体环境下的多器械操作，在不同光照条件及关键点测量精度下测试。结果表明，所提框架显著降低了工具定位误差，性能达到当前顶尖水平，且计算效率更高。
原文摘要 · Abstract (English)
In Robot-Assisted Minimally Invasive Surgery (RMIS), accurate tool localization is crucial to ensure patient safety and successful task execution. However, this remains challenging for cable-driven robots, such as the da Vinci robot, because erroneous encoder readings lead to pose estimation errors. In this study, we propose a calibration framework to produce accurate tool localization results through computing the hand-eye transformation matrix on-the-fly. The framework consists of two interrelated algorithms: the feature association block and the hand-eye calibration block, which provide robust correspondences for key points detected on monocular images without pre-training, and offer the versatility to accommodate various surgical scenarios by adopting an array of filter approaches, respectively. To validate its efficacy, we test the framework extensively on publicly available video datasets that feature multiple surgical instruments conducting tasks in both in vitro and ex vivo scenarios, under varying illumination conditions and with different levels of key point measurement accuracy. The results show a significant reduction in tool localization errors under the proposed calibration framework, with accuracies comparable to other state-of-the-art methods while being more time-efficient.
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