Estimating motors from a variety of geometric data in 3D conformal geometric algebra

The motion rotors, or motors, are used to model Euclidean motion in 3D conformal
geometric algebra. In this chapter we present a technique for estimating
the motor which best transforms one set of noisy geometric objects onto another.
The technique reduces to an eigenrotator problem and has some advantages over
matrix formulations. It allows motors to be estimated from a variety of geometric
data such as points, spheres, circles, lines, planes, directions, and tangents; and
the different types of geometric data are combined naturally in a single framework.
Also, it excludes the possibility of a reflection unlike some matrix formulations.
It returns the motor with the smallest translation and rotation angle when
the optimal motor is not unique.