无需额外传感器，通过视觉实现介入机器人姿态精准感知。
Pose State Perception of Interventional Robot for Cardio-cerebrovascular Procedures
- 基于双头多任务U-Net同时检测血管与机器人
- 结合骨架提取优化算法提升姿态感知精度
- 适用于心血管介入手术机器人控制场景
针对日益增长的心脑血管介入手术需求，精确控制介入机器人至关重要。在复杂的血管环境中，对介入机器人姿态状态的准确可靠感知尤为关键。本文提出一种无需额外传感器或标记的新型视觉方法。核心为三部分框架：首先，采用双头多任务U-Net模型实现血管段与介入机器人同步检测；其次，设计先进的骨架提取与优化算法；最后，基于几何特征构建完整的姿态状态感知系统，可精准识别机器人姿态并为后续控制提供策略。实验结果表明，该方法在轨迹跟踪与姿态感知方面具有高可靠性与准确性。
原文摘要 · Abstract (English)
In response to the increasing demand for cardiocerebrovascular interventional surgeries, precise control of interventional robots has become increasingly important. Within these complex vascular scenarios, the accurate and reliable perception of the pose state for interventional robots is particularly crucial. This paper presents a novel vision-based approach without the need of additional sensors or markers. The core of this paper's method consists of a three-part framework: firstly, a dual-head multitask U-Net model for simultaneous vessel segment and interventional robot detection; secondly, an advanced algorithm for skeleton extraction and optimization; and finally, a comprehensive pose state perception system based on geometric features is implemented to accurately identify the robot's pose state and provide strategies for subsequent control. The experimental results demonstrate the proposed method's high reliability and accuracy in trajectory tracking and pose state perception.
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