  This paper describes the synthesis and evaluation of a novel state estimator
for a Quadrotor Micro Aerial Vehicle. Dynamic equations which relate
acceleration, attitude and the aero-dynamic propeller drag are encapsulated in
an extended Kalman filter framework for estimating the velocity and the
attitude of the quadrotor. It is demonstrated that exploiting the relationship
between the body frame accelerations and velocities, due to blade flapping,
enables drift free estimation of lateral and longitudinal components of body
frame translational velocity along with improvements to roll and pitch
components of body attitude estimations. Real world data sets gathered using a
commercial off-the-shelf quadrotor platform, together with ground truth data
from a Vicon system, are used to evaluate the effectiveness of the proposed
algorithm.
