  Odometry on aerial robots has to be of low latency and high robustness whilst
also respecting the Size, Weight, Area and Power (SWAP) constraints as demanded
by the size of the robot. A combination of visual sensors coupled with Inertial
Measurement Units (IMUs) has proven to be the best combination to obtain robust
and low latency odometry on resource-constrained aerial robots. Recently, deep
learning approaches for Visual Inertial fusion have gained momentum due to
their high accuracy and robustness. However, the remarkable advantages of these
techniques are their inherent scalability (adaptation to different sized aerial
robots) and unification (same method works on different sized aerial robots) by
utilizing compression methods and hardware acceleration, which have been
lacking from previous approaches.
  To this end, we present a deep learning approach for visual translation
estimation and loosely fuse it with an Inertial sensor for full 6DoF odometry
estimation. We also present a detailed benchmark comparing different
architectures, loss functions and compression methods to enable scalability. We
evaluate our network on the MSCOCO dataset and evaluate the VI fusion on
multiple real-flight trajectories.
