  Visual odometry networks commonly use pretrained optical flow networks in
order to derive the ego-motion between consecutive frames. The features
extracted by these networks represent the motion of all the pixels between
frames. However, due to the existence of dynamic objects and texture-less
surfaces in the scene, the motion information for every image region might not
be reliable for inferring odometry due to the ineffectiveness of dynamic
objects in derivation of the incremental changes in position. Recent works in
this area lack attention mechanisms in their structures to facilitate dynamic
reweighing of the feature maps for extracting more refined egomotion
information. In this paper, we explore the effectiveness of self-attention in
visual odometry. We report qualitative and quantitative results against the
SOTA methods. Furthermore, saliency-based studies alongside specially designed
experiments are utilized to investigate the effect of self-attention on VO. Our
experiments show that using self-attention allows for the extraction of better
features while achieving a better odometry performance compared to networks
that lack such structures.
