  This paper fosters the idea that deep learning methods can be used to
complement classical visual odometry pipelines to improve their accuracy and to
associate uncertainty models to their estimations. We show that the biases
inherent to the visual odometry process can be faithfully learned and
compensated for, and that a learning architecture associated with a
probabilistic loss function can jointly estimate a full covariance matrix of
the residual errors, defining an error model capturing the heteroscedasticity
of the process. Experiments on autonomous driving image sequences assess the
possibility to concurrently improve visual odometry and estimate an error
associated with its outputs.
