用显微立体相机与新注册方法，提升机器人微操作精度。
A Cranial-Feature-Based Registration Scheme for Robotic Micromanipulation Using a Microscopic Stereo Camera System
- 基于卷积神经网络的彩色约束注册策略，精准匹配部分暴露颅骨。
- 注册误差仅1.13mm（±0.31mm）和3.38°（±0.89°），实测稳定。
- 适用于小鼠颅窗创建等高精度微创手术自动化任务。
生物样本在尺寸和形状上存在显著差异，给自主机器人微操作带来挑战。本文以小鼠颅骨窗制作为例，提出一种增强型显微立体相机系统（MSCS），结合线性深度感知模型。同时开发了一种针对部分暴露颅骨表面的精确注册方案，采用基于CNN的约束与彩色化注册策略。该系统集成于MSCS，用于机器人微操作任务。实验显示，MSCS在台阶高度测量中精度达0.10 mm ± 0.02 mm，3D重建实时性能为30 FPS。注册方案在105帧连续测试中，平移误差为1.13 mm ± 0.31 mm，旋转误差为3.38° ± 0.89°，平均速度1.60 FPS。本研究展示了MSCS与新型注册方案在提升科研与外科领域机器人微操作精度与准确性的应用潜力，为微观操作自动化提供新方法，推动更精准、高效、微创的微手术与研究进程。
原文摘要 · Abstract (English)
Biological specimens exhibit significant variations in size and shape, challenging autonomous robotic manipulation. We focus on the mouse skull window creation task to illustrate these challenges. The study introduces a microscopic stereo camera system (MSCS) enhanced by the linear model for depth perception. Alongside this, a precise registration scheme is developed for the partially exposed mouse cranial surface, employing a CNN-based constrained and colorized registration strategy. These methods are integrated with the MSCS for robotic micromanipulation tasks. The MSCS demonstrated a high precision of 0.10 mm $\pm$ 0.02 mm measured in a step height experiment and real-time performance of 30 FPS in 3D reconstruction. The registration scheme proved its precision, with a translational error of 1.13 mm $\pm$ 0.31 mm and a rotational error of 3.38$^{\circ}$ $\pm$ 0.89$^{\circ}$ tested on 105 continuous frames with an average speed of 1.60 FPS. This study presents the application of a MSCS and a novel registration scheme in enhancing the precision and accuracy of robotic micromanipulation in scientific and surgical settings. The innovations presented here offer automation methodology in handling the challenges of microscopic manipulation, paving the way for more accurate, efficient, and less invasive procedures in various fields of microsurgery and scientific research.
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