  This work proposes a novel deep network architecture to solve the camera
Ego-Motion estimation problem. A motion estimation network generally learns
features similar to Optical Flow (OF) fields starting from sequences of images.
This OF can be described by a lower dimensional latent space. Previous research
has shown how to find linear approximations of this space. We propose to use an
Auto-Encoder network to find a non-linear representation of the OF manifold. In
addition, we propose to learn the latent space jointly with the estimation
task, so that the learned OF features become a more robust description of the
OF input. We call this novel architecture LS-VO.
  The experiments show that LS-VO achieves a considerable increase in
performances in respect to baselines, while the number of parameters of the
estimation network only slightly increases.
