  Most geometric approaches to monocular Visual Odometry (VO) provide robust
pose estimates, but sparse or semi-dense depth estimates. Off late, deep
methods have shown good performance in generating dense depths and VO from
monocular images by optimizing the photometric consistency between images.
Despite being intuitive, a naive photometric loss does not ensure proper pixel
correspondences between two views, which is the key factor for accurate depth
and relative pose estimations. It is a well known fact that simply minimizing
such an error is prone to failures.
  We propose a method using Epipolar constraints to make the learning more
geometrically sound. We use the Essential matrix, obtained using Nister's Five
Point Algorithm, for enforcing meaningful geometric constraints on the loss,
rather than using it as labels for training. Our method, although simplistic
but more geometrically meaningful, using lesser number of parameters, gives a
comparable performance to state-of-the-art methods which use complex losses and
large networks showing the effectiveness of using epipolar constraints. Such a
geometrically constrained learning method performs successfully even in cases
where simply minimizing the photometric error would fail.
