arXiv 2603.089832026-03-09cs.ROcs.CV

SurgCalib: 基于高斯点散的机器人辅助微创手术手眼标定

SurgCalib: Gaussian Splatting-Based Hand-Eye Calibration for Robot-Assisted Minimally Invasive Surgery

Robotics and Control Laboratory, University of British Columbia（机器人与控制实验室，不列颠哥伦比亚大学）；Department of Biomedical Engineering, National University of Singapore（生物医学工程系，新加坡国立大学）；Division of Otolaryngology – Head and Neck Surgery, University of British Columbia（耳鼻喉科–头颈外科部，不列颠哥伦比亚大学）

Zijian Wu, Shuojue Yang, Yu Chung Lee, Eitan Prisman, Yueming Jin, Septimiu E. Salcudean

备注：: 9 pages, 7 figures

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AI总结：

SurgCalib通过基于高斯点散的无标记方法实现手术机器人手眼标定，提升手术精度与无菌环境合规性。

摘要：

我们提出了一种基于高斯点散的框架，用于达芬奇手术机器人的手眼标定。在视觉引导的机器人系统中，准确估计机器人基座与相机框架之间的刚体变换对于可靠的闭环控制至关重要。对于由电缆驱动的手术机器人，这项任务面临独特的挑战。手术仪器的编码器由于电缆拉伸和反向间隙常常产生不准确的本体感觉测量。传统手眼标定方法通常依赖于已知的标记图案并解决AX = XB的公式。虽然有效，但向手术室（OR）环境引入额外标记可能会违反无菌协议并扰乱手术流程。在本研究中，我们提出了SurgCalib，一种自动、无标记的框架，有望在OR中使用。SurgCalib首先使用原始运动学测量初始化手术仪器的位姿，随后通过在基于高斯点散的可微渲染管道中的RCM约束下的两阶段优化过程来细化此位姿。我们评估了所提出的方法在公开的dVRK基准SurgPose上的性能。结果表明，对于左和右仪器，平均的2D工具尖端重投影误差分别为12.24 px（2.06 mm）和11.33 px（1.9 mm），3D工具尖端欧几里得距离误差分别为5.98 mm和4.75 mm。

英文摘要：

We present a Gaussian Splatting-based framework for hand-eye calibration of the da Vinci surgical robot. In a vision-guided robotic system, accurate estimation of the rigid transformation between the robot base and the camera frame is essential for reliable closed-loop control. For cable-driven surgical robots, this task faces unique challenges. The encoders of surgical instruments often produce inaccurate proprioceptive measurements due to cable stretch and backlash. Conventional hand-eye calibration approaches typically rely on known fiducial patterns and solve the AX = XB formulation. While effective, introducing additional markers into the operating room (OR) environment can violate sterility protocols and disrupt surgical workflows. In this study, we propose SurgCalib, an automatic, markerless framework that has the potential to be used in the OR. SurgCalib first initializes the pose of the surgical instrument using raw kinematic measurements and subsequently refines this pose through a two-phase optimization procedure under the RCM constraint within a Gaussian Splatting-based differentiable rendering pipeline. We evaluate the proposed method on the public dVRK benchmark, SurgPose. The results demonstrate average 2D tool-tip reprojection errors of 12.24 px (2.06 mm) and 11.33 px (1.9 mm), and 3D tool-tip Euclidean distance errors of 5.98 mm and 4.75 mm, for the left and right instruments, respectively.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。