用单目视觉实时感知柔性内镜器械位置，提升手术操控效率
Real-time Monocular 2D and 3D Perception of Endoluminal Scenes for Controlling Flexible Robotic Endoscopic Instruments
- 基于单目图像的2D/3D学习算法，实现器械位姿与组织距离估计
- 仿真平台生成真实内腔场景，支撑算法训练与系统验证
- 实测操控时间减少70%以上，适合内镜机器人研发与临床应用
内腔手术为早期胃肠道和泌尿系统癌症提供微创治疗选择，但受限于手术工具及高学习成本。连续体机器人可提供灵活器械，实现精准组织切除，有望改善疗效。本文提出一种面向连续体机器人系统的视觉感知平台，旨在利用单目内窥镜图像，识别柔性器械的位置与姿态，并测量其与组织的距离。我们开发了2D与3D基于学习的感知算法，并构建一个模拟柔性器械动力学的物理逼真仿真器，可生成真实内腔场景，支持机器人控制与大规模数据采集。基于连续体机器人原型，我们进行了模块与系统级评估。结果表明，该算法显著提升器械操控能力，在轨迹跟踪任务中操控时间减少超70%，增强对术中场景的理解，实现更稳健的内腔手术。
原文摘要 · Abstract (English)
Endoluminal surgery offers a minimally invasive option for early-stage gastrointestinal and urinary tract cancers but is limited by surgical tools and a steep learning curve. Robotic systems, particularly continuum robots, provide flexible instruments that enable precise tissue resection, potentially improving outcomes. This paper presents a visual perception platform for a continuum robotic system in endoluminal surgery. Our goal is to utilize monocular endoscopic image-based perception algorithms to identify position and orientation of flexible instruments and measure their distances from tissues. We introduce 2D and 3D learning-based perception algorithms and develop a physically-realistic simulator that models flexible instruments dynamics. This simulator generates realistic endoluminal scenes, enabling control of flexible robots and substantial data collection. Using a continuum robot prototype, we conducted module and system-level evaluations. Results show that our algorithms improve control of flexible instruments, reducing manipulation time by over 70% for trajectory-following tasks and enhancing understanding of surgical scenarios, leading to robust endoluminal surgeries.
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