  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.
