  Pose estimation is one of the most important problems in computer vision. It
can be divided in two different categories -- absolute and relative -- and may
involve two different types of camera models: central and non-central.
State-of-the-art methods have been designed to solve separately these problems.
This paper presents a unified framework that is able to solve any pose problem
by alternating optimization techniques between two set of parameters, rotation
and translation. In order to make this possible, it is necessary to define an
objective function that captures the problem at hand. Since the objective
function will depend on the rotation and translation it is not possible to
solve it as a simple minimization problem. Hence the use of Alternating
Minimization methods, in which the function will be alternatively minimized
with respect to the rotation and the translation. We show how to use our
framework in three distinct pose problems. Our methods are then benchmarked
with both synthetic and real data, showing their better balance between
computational time and accuracy.
