  Accurate camera motion estimation is a fundamental building block for many
Computer Vision algorithms. For improved robustness, temporal consistency of
translational and rotational camera velocity is often assumed by propagating
motion information forward using stochastic filters. Classical stochastic
filters, however, use linear approximations for the non-linear observer model
and for the non-linear structure of the underlying Lie Group
$\operatorname{SE}_3$ and have to approximate the unknown posteriori
distribution. In this paper we employ a non-linear measurement model for the
camera motion estimation problem that incorporates multiple observation
equations. We solve the underlying filtering problem using a novel Minimum
Energy Filter on $\operatorname{SE}_3$ and give explicit expressions for the
optimal state variables. Experiments on the challenging KITTI benchmark show
that, although a simple motion model is only employed, our approach improves
rotational velocity estimation and otherwise is on par with the
state-of-the-art.
