Iterative PnP and its application in 3D-2D vascular image registration for robot navigation

TL;DR

This paper introduces a real-time, robust 3D-2D vascular image registration algorithm for robot navigation that effectively handles outliers and nonrigid shapes, achieving high accuracy and computational efficiency.

Contribution

It proposes a novel iterative PnP approach using Lie manifold optimization and RKHS to improve real-time vascular image registration for robotic interventions.

Findings

Processes registration over 50 Hz for rigid shapes

Achieves accuracy comparable to existing methods

Handles outliers and nonrigid deformations effectively

Abstract

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.