  Visual odometry is an essential key for a localization module in SLAM
systems. However, previous methods require tuning the system to adapt
environment changes. In this paper, we propose a learning-based approach for
frame-to-frame monocular visual odometry estimation. The proposed network is
only learned by disparity maps for not only covering the environment changes
but also solving the scale problem. Furthermore, attention block and
skip-ordering scheme are introduced to achieve robust performance in various
driving environment. Our network is compared with the conventional methods
which use common domain such as color or optical flow. Experimental results
confirm that the proposed network shows better performance than other
approaches with higher and more stable results.
