  Traditionally, pose estimation is considered as a two step problem. First,
feature correspondences are determined by direct comparison of image patches,
or by associating feature descriptors. In a second step, the relative pose and
the coordinates of corresponding points are estimated, most often by minimizing
the reprojection error (RPE). RPE optimization is based on a loss function that
is merely aware of the feature pixel positions but not of the underlying image
intensities. In this paper, we propose a sparse direct method which introduces
a loss function that allows to simultaneously optimize the unscaled relative
pose, as well as the set of feature correspondences directly considering the
image intensity values. Furthermore, we show how to integrate statistical prior
information on the motion into the optimization process. This constructive
inclusion of a Bayesian bias term is particularly efficient in application
cases with a strongly predictable (short term) dynamic, e.g. in a driving
scenario. In our experiments, we demonstrate that the JET algorithm we propose
outperforms the classical reprojection error optimization on two synthetic
datasets and on the KITTI dataset. The JET algorithm runs in real-time on a
single CPU thread.
