This paper describes the synthesis and evaluation of a "virtual odometer" for a Quadrotor Micro Aerial Vehicle. Availability of a velocity estimate has the potential to enhance the accuracy of mapping, estimation and control algorithms used with quadrotors, increasing the effectiveness of their applications. As a result of the unique dynamic characteristics of the quadrotor, a dual axis accelerometer mounted parallel to the propeller plane provides measurements that are directly proportional to vehicle velocities in that plane. Exploiting this insight, we encapsulate quadrotor dynamic equations which relate acceleration, attitude and the aerodynamic propeller drag in an extended Kalman filter framework for the purpose of state estimation. The result is a drift free estimation of lateral and longitudinal components of translational velocity and roll and pitch components of attitude of the quadrotor. Real world data sets gathered from two different quadrotor platforms, together with ground truth data from a Vicon system, are used to evaluate the effectiveness of the proposed algorithm and demonstrate that drift free estimates for the velocity and attitude can be obtained
