  The main contribution of this paper is a high frequency, low-complexity,
on-board visual-inertial odometry system for quadrotor micro air vehicles. The
system consists of an extended Kalman filter (EKF) based state estimation
algorithm that fuses information from a low cost MEMS inertial measurement unit
acquired at 200Hz and VGA resolution images from a monocular camera at 50Hz.
The dynamic model describing the quadrotor motion is employed in the estimation
algorithm as a third source of information. Visual information is incorporated
into the EKF by enforcing the epipolar constraint on features tracked between
image pairs, avoiding the need to explicitly estimate the location of the
tracked environmental features. Combined use of the dynamic model and epipolar
constraints makes it possible to obtain drift free velocity and attitude
estimates in the presence of both accelerometer and gyroscope biases. A
strategy to deal with the unobservability that arises when the quadrotor is in
hover is also provided. Experimental data from a real-time implementation of
the system on a 50 gram embedded computer are presented in addition to the
simulations to demonstrate the efficacy of the proposed system.
