Graph-based Pose Estimation of Texture-less Surgical Tools for Autonomous Robot Control
Graph-based Pose Estimation of Texture-less Surgical Tools for Autonomous Robot Control
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DOI：
10.1109/icra48891.2023.10160287
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Haozheng Xu;M. Runciman;João Cartucho;Chi Xu;S. Giannarou
中科院分区：
文献类型：
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作者：
Haozheng Xu;M. Runciman;João Cartucho;Chi Xu;S. Giannarou
在机器人辅助微创手术中，手术工具的姿态估计对于手术导航、视觉伺服、自主机器人任务执行和增强现实等应用至关重要。文献中已经提出了过多的基于硬件和基于视觉的方法。然而，由于手术场景中的部分工具可见性、闭塞和变化，这些方法在RMI中的直接应用有很大的局限性。在这项工作中，提出了一种新的基于关键点图的网络来估计小直径无纹理圆柱形手术工具的位姿。为了应对远程管理信息系统中的挑战，采用了关键点对象表示，并首次在关键点图形表示中将时间信息和空间信息相结合，进行关键点求精。最后，使用PNP求解器计算出稳定和准确的刀具位姿。我们的性能评估研究表明，该方法能够准确地预测无纹理机器人轴的位姿，Add-S得分超过98%。在目标遮挡、场景光照变化等困难条件下，该方法的性能优于最新的姿态估计模型。
In Robot-assisted Minimally Invasive Surgery (RMIS), the estimation of the pose of surgical tools is crucial for applications such as surgical navigation, visual servoing, autonomous robotic task execution and augmented reality. A plethora of hardware-based and vision-based methods have been proposed in the literature. However, direct application of these methods to RMIS has significant limitations due to partial tool visibility, occlusions and changes in the surgical scene. In this work, a novel keypoint-graph-based network is proposed to estimate the pose of texture-less cylindrical surgical tools of small diameter. To deal with the challenges in RMIS, keypoint object representation is used and for the first time, temporal information is combined with spatial information in keypoint graph representation, for keypoint refinement. Finally, stable and accurate tool pose is computed using a PnP solver. Our performance evaluation study has shown that the proposed method is able to accurately predict the pose of a textureless robotic shaft with an ADD-S score of over 98%. The method outperforms state-of-the-art pose estimation models under challenging conditions such as object occlusion and changes in the lighting of the scene.