  Robotics and computer vision problems commonly require handling rigid-body
motions comprising translation and rotation - together referred to as pose. In
some situations, a vectorial parameterization of pose can be useful, where
elements of a vector space are surjectively mapped to a matrix Lie group. For
example, these vectorial representations can be employed for optimization as
well as uncertainty representation on groups. The most common mapping is the
matrix exponential, which maps elements of a Lie algebra onto the associated
Lie group. However, this choice is not unique. It has been previously shown how
to characterize all such vectorial parameterizations for SO(3), the group of
rotations. We extend this result to SE(3), the group of poses (translation and
rotation), showing how to build a family of mappings that includes the matrix
exponential as well as the Cayley transformation. While our main contribution
is the theory, we also demonstrate three different applications of the proposed
pose mappings: (i) pose interpolation, (ii) pose servoing control, and (iii)
pose estimation in a pointcloud alignment problem. In the pointcloud alignment
problem our results lead to a new algorithm based on the Cayley transformation,
which we call CayPer.
