Improving MAV pose estimation using visual information

We present a system to improve the estimation of MAV location and attitude by combining GPS, IMU and visual information in an unscented Kalman filter framework. Feature points are tracked and combined to create a homography matrix which is used as the measurement input to the filter. We present a novel method to transform uncertainty in feature tracking to uncertainty in the homography. Using a system developed with this framework, we present results which show that this method can substantially increase the accuracy of pose estimation, compared to GPS/IMU alone.