  In the recent years, many methods demonstrated the ability of neural networks
to learn depth and pose changes in a sequence of images, using only
self-supervision as the training signal. Whilst the networks achieve good
performance, the often over-looked detail is that due to the inherent ambiguity
of monocular vision they predict depth up to an unknown scaling factor. The
scaling factor is then typically obtained from the LiDAR ground truth at test
time, which severely limits practical applications of these methods. In this
paper, we show that incorporating prior information about the camera
configuration and the environment, we can remove the scale ambiguity and
predict depth directly, still using the self-supervised formulation and not
relying on any additional sensors.
