  We present a self-supervised deep pose correction (DPC) network that applies
pose corrections to a visual odometry estimator to improve its accuracy.
Instead of regressing inter-frame pose changes directly, we build on prior work
that uses data-driven learning to regress pose corrections that account for
systematic errors due to violations of modelling assumptions. Our
self-supervised formulation removes any requirement for six-degrees-of-freedom
ground truth and, in contrast to expectations, often improves overall
navigation accuracy compared to a supervised approach. Through extensive
experiments, we show that our self-supervised DPC network can significantly
enhance the performance of classical monocular and stereo odometry estimators
and substantially out-performs state-of-the-art learning-only approaches.
