  This letter proposes a reactive navigation strategy for recovering the
altitude, translational velocity and orientation of Micro Aerial Vehicles. The
main contribution lies in the direct and tight fusion of Inertial Measurement
Unit (IMU) measurements with monocular feedback under an assumption of a single
planar scene. An Iterated Extended Kalman Filter (IEKF) scheme is employed. The
state prediction makes use of IMU readings while the state update relies
directly on photometric feedback as measurements. Unlike feature-based methods,
the photometric difference for the innovation term renders an inherent and
robust data association process in a single step. The proposed approach is
validated using real-world datasets. The results show that the proposed method
offers better robustness, accuracy, and efficiency than a feature-based
approach. Further investigation suggests that the accuracy of the flight
velocity estimates from the proposed approach is comparable to those of two
state-of-the-art Visual Inertial Systems (VINS) while the proposed framework is
$\approx15-30$ times faster thanks to the omission of reconstruction and
mapping.
