  Deep learning techniques have significantly advanced in providing accurate
visual odometry solutions by leveraging large datasets. However, generating
uncertainty estimates for these methods remains a challenge. Traditional sensor
fusion approaches in a Bayesian framework are well-established, but deep
learning techniques with millions of parameters lack efficient methods for
uncertainty estimation.
  This paper addresses the issue of uncertainty estimation for pre-trained
deep-learning models in monocular visual odometry. We propose formulating a
factor graph on an implicit layer of the deep learning network to recover
relative covariance estimates, which allows us to determine the covariance of
the Visual Odometry (VO) solution. We showcase the consistency of the deep
learning engine's covariance approximation with an empirical analysis of the
covariance model on the EUROC datasets to demonstrate the correctness of our
formulation.
