In recent years, Autonomous Navigation for flying robots has become a global challenge. Unmanned Aerial Vehicles (UAVs) have a high potential in both military and civil applications such as aerial reconnaissance, surveillance, search and rescue, product deliveries, agriculture, or infrastructure inspections. Autonomous Navigation of UAVs exploits an onboard Inertial Measurement Unit (IMU) which consists of a three-axis accelerometer and a three-axis gyroscope providing the linear acceleration and the angular velocity of the robot. The problem is that IMU measurements suffer from noise and bias resulting in a drift on the pose estimation. Even if the drift is irrelevant, it will accumulate to a significant value over time.