  In this work, we propose a new learning approach for autonomous navigation
and landing of an Unmanned-Aerial-Vehicle (UAV). We develop a multimodal fusion
of deep neural architectures for visual-inertial odometry. We train the model
in an end-to-end fashion to estimate the current vehicle pose from streams of
visual and inertial measurements. We first evaluate the accuracy of our
estimation by comparing the prediction of the model to traditional algorithms
on the publicly available EuRoC MAV dataset. The results illustrate a $25 \%$
improvement in estimation accuracy over the baseline. Finally, we integrate the
architecture in the closed-loop flight control system of Airsim - a plugin
simulator for Unreal Engine - and we provide simulation results for autonomous
navigation and landing.
