  In this paper we present an on-manifold sequence-to-sequence learning
approach to motion estimation using visual and inertial sensors. It is to the
best of our knowledge the first end-to-end trainable method for visual-inertial
odometry which performs fusion of the data at an intermediate
feature-representation level. Our method has numerous advantages over
traditional approaches. Specifically, it eliminates the need for tedious manual
synchronization of the camera and IMU as well as eliminating the need for
manual calibration between the IMU and camera. A further advantage is that our
model naturally and elegantly incorporates domain specific information which
significantly mitigates drift. We show that our approach is competitive with
state-of-the-art traditional methods when accurate calibration data is
available and can be trained to outperform them in the presence of calibration
and synchronization errors.
