This paper reports on a new real-time robot-centered 3D-2D vascular image alignment algorithm, which is robust to outliers and can align nonrigid shapes. Few works have managed to achieve both real-time and accurate performance for vascular intervention robots. This work bridges high-accuracy 3D-2D registration techniques and computational efficiency requirements in intervention robot applications. We categorize centerline-based vascular 3D-2D image registration problems as an iterative Perspective-n-Point (PnP) problem and propose to use the Levenberg-Marquardt solver on the Lie manifold. Then, the recently developed Reproducing Kernel Hilbert Space (RKHS) algorithm is introduced to overcome the ``big-to-small'' problem in typical robotic scenarios. Finally, an iterative reweighted least squares is applied to solve RKHS-based formulation efficiently. Experiments indicate that the proposed algorithm processes registration over 50 Hz (rigid) and 20 Hz (nonrigid) and obtains competing registration accuracy similar to other works. Results indicate that our Iterative PnP is suitable for future vascular intervention robot applications.

翻译：本文报告了一种新型的以机器人为中心的实时3D-2D血管图像对齐算法，该算法对异常值具有鲁棒性且能够对齐非刚性形状。目前很少有工作能够同时实现血管介入机器人的实时性和高精度性能。本研究弥合了高精度3D-2D配准技术与介入机器人应用中计算效率需求之间的差距。我们将基于中心线的血管3D-2D图像配准问题归类为迭代透视n点（PnP）问题，并提出在流形上使用Levenberg-Marquardt求解器。随后引入近期发展的再生核希尔伯特空间（RKHS）算法，以克服典型机器人场景中的"大-小映射"问题。最后应用迭代重加权最小二乘法高效求解基于RKHS的公式。实验表明，所提算法以超过50Hz（刚性）和20Hz（非刚性）的频率完成配准，并获得了与其他工作相近的竞争性配准精度。结果表明，我们的迭代PnP适用于未来血管介入机器人应用。