  Historically, feature-based approaches have been used extensively for
camera-based robot perception tasks such as localization, mapping, tracking,
and others. Several of these approaches also combine other sensors (inertial
sensing, for example) to perform combined state estimation. Our work rethinks
this approach; we present a representation learning mechanism that identifies
visual features that best correspond to robot motion as estimated by an
external signal. Specifically, we utilize the robot's transformations through
an external signal (inertial sensing, for example) and give attention to image
space that is most consistent with the external signal. We use a pairwise
consistency metric as a representation to keep the visual features consistent
through a sequence with the robot's relative pose transformations. This
approach enables us to incorporate information from the robot's perspective
instead of solely relying on the image attributes. We evaluate our approach on
real-world datasets such as KITTI & EuRoC and compare the refined features with
existing feature descriptors. We also evaluate our method using our real robot
experiment. We notice an average of 49% reduction in the image search space
without compromising the trajectory estimation accuracy. Our method reduces the
execution time of visual odometry by 4.3% and also reduces reprojection errors.
We demonstrate the need to select only the most important features and show the
competitiveness using various feature detection baselines.
