Surgical robots are controlled using a priori models based on robots' geometric parameters, which are calibrated before the surgical procedure. One of the challenges in using robots in real surgical settings is that parameters change over time, consequently deteriorating control accuracy. In this context, our group has been investigating online calibration strategies without added sensors. In one step toward that goal, we have developed an algorithm to estimate the pose of the instruments' shafts in endoscopic images. In this study, we build upon that earlier work and propose a new framework to more precisely estimate the pose of a rigid surgical instrument. Our strategy is based on a novel pose estimation model called MBAPose and the use of synthetic training data. Our experiments demonstrated an improvement of 21 % for translation error and 26 % for orientation error on synthetic test data with respect to our previous work. Results with real test data provide a baseline for further research.

翻译：外科机器人使用基于机器人几何参数的先验模型来控制外科机器人,这些先验模型在外科手术之前经过校准。在实际外科手术环境中使用机器人的挑战之一是参数随时间变化而变化,从而导致控制准确性下降。在这方面,我们的小组一直在研究在线校准战略,但没有增加传感器。在实现这一目标的一步中,我们开发了一个算法来估计仪器轴在内骨图中的位置。在这个研究中,我们借鉴了先前的工作,并提出了一个新框架来更准确地估计僵硬外科器的构成。我们的战略基于一个叫作MBAPose的新型外科估计模型和使用合成培训数据。我们的实验显示,翻译错误改进了21%,合成测试数据方向错误改进了26%,与我们以前的工作有关。有实际测试数据的结果为进一步研究提供了基准。