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.

翻译：在机器人辅助微创手术中，精确的手术工具定位对于确保患者安全和手术任务成功执行至关重要。然而，对于达芬奇机器人这类线缆驱动式手术系统，由于编码器读数误差会导致位姿估计偏差，实现精准定位仍面临挑战。本研究提出一种动态计算手眼变换矩阵的标定框架，以实现高精度工具定位。该框架包含两个相互关联的算法模块：特征关联模块与手眼标定模块。前者无需预训练即可为单目图像检测到的关键点提供鲁棒对应关系，后者通过采用多种滤波方法适配不同手术场景。为验证框架有效性，我们在公开视频数据集上进行了全面测试，这些数据集涵盖体外与离体场景下多种手术器械执行任务的情况，包含不同光照条件及关键点测量精度等级。实验结果表明，该标定框架能显著降低工具定位误差，在保持与现有先进方法相当精度的同时，具有更优的时间效率。