  We propose a framework for Google Map aided UAV navigation in GPS-denied
environment. Geo-referenced navigation provides drift-free localization and
does not require loop closures. The UAV position is initialized via
correlation, which is simple and efficient. We then use optical flow to predict
its position in subsequent frames. During pose tracking, we obtain inter-frame
translation either by motion field or homography decomposition, and we use HOG
features for registration on Google Map. We employ particle filter to conduct a
coarse to fine search to localize the UAV. Offline test using aerial images
collected by our quadrotor platform shows promising results as our approach
eliminates the drift in dead-reckoning, and the small localization error
indicates the superiority of our approach as a supplement to GPS.
