This paper develops a simple and low-cost method for 3D, high-rate vehicle state estimation, specially designed for free-flying Micro Aerial Vehicles (MAVs). We fuse observations from inertial measurement units and the recently appeared low-cost optical flow smart cameras. These smart cameras integrate a sonar altimeter, a triaxial gyrometer and an optical flow sensor, and directly provide metric ego-motion information in the form of body velocities and altitude. Compared to state-of-the-art visual-inertial odometry methods, we are able to drastically reduce the computational load in the main processor unit, and obtain an accurate estimation of the vehicle state at a high update rate of 100Hz. We thus extend the current use of these smart cameras from hovering purposes to odometry estimation. In order to propose a simple algorithmic solution, we investigate the performances of two Kalman filters, in the extended and error-state flavors, alongside a large number of algorithm variations, using simulations and real experiments with precise ground-truth. We observe that the marginal performance gain attained with these algorithm improvements does not pay for the effort of implementing them. We conclude that a classical EKF in its simplest form is sufficient for providing motion estimates that coherently exploit the available measurements.