Google Map Aided Visual Navigation for UAVs in GPS-denied Environment
This provides a practical supplement to GPS for UAVs in challenging environments, though it appears incremental as it builds on existing visual navigation methods.
The paper tackles UAV navigation in GPS-denied environments by proposing a Google Map-aided framework that uses optical flow and particle filters for localization, achieving drift-free results with small localization errors in offline tests.
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.