  This paper addresses the problem of scale estimation in monocular SLAM by
estimating absolute distances between camera centers of consecutive image
frames. These estimates would improve the overall performance of classical (not
deep) SLAM systems and allow metric feature locations to be recovered from a
single monocular camera. We propose several network architectures that lead to
an improvement of scale estimation accuracy over the state of the art. In
addition, we exploit a possibility to train the neural network only with
synthetic data derived from a computer graphics simulator. Our key insight is
that, using only synthetic training inputs, we can achieve similar scale
estimation accuracy as that obtained from real data. This fact indicates that
fully annotated simulated data is a viable alternative to existing
deep-learning-based SLAM systems trained on real (unlabeled) data. Our
experiments with unsupervised domain adaptation also show that the difference
in visual appearance between simulated and real data does not affect scale
estimation results. Our method operates with low-resolution images (0.03MP),
which makes it practical for real-time SLAM applications with a monocular
camera.
