Based on the Iterative Closest Point (ICP) framework, we present a generalized solution for the registration
between homologous points and lines. The transformation we seek comprises an anisotropic scaling, followed by
rotation and translation. This algorithm is demonstrated using the Perspective-n-Point (PnP) problem where
lines form a bundle, and the Non-Perspective-n-Point (NPnP) problem where each line potentially has its own
origin. We also prove that one existing NPnP solution is, in fact, equivalent to ICP, and that a second PnP
solution differs from ICP only in the iteratively estimated translation. Applications for these types of registration
include ultrasound calibration, kinematics tracking under fluoroscopic video, and camera pose estimation.
Simulation results suggest this ICP algorithm compares favorably to existing PnP and NPnP algorithms, and
has an extremely compact formulation.