This paper presents an innovative method for estimating wind velocity using an optical flow sensor mounted on a miniature air vehicle (MAV). Using the flow of features measured by the optical flow sensor in the longitudinal and lateral directions, the MAV's crab-angle is estimated. By combining the crab-angle with measurements of ground track from GPS and the MAV's airspeed, the wind velocity is computed. Our algorithm allow this computation to be performed in real time on board an MAV. Unlike previous techniques, this approach does not require the use of magnetometers. This algorithm has been implemented and its effectiveness demonstrated through experimental test results.

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Miniature UAVs (MAVs) are quickly gaining acceptance as a platform for performing remote sensing or surveillance of remote areas. However, due to their small size, MAVs are very susceptible to wind gusts and other atmospheric disturbances. These disturbances cause the video from an MAV to have a significant amount of movement, making the video frames difficult to watch and disorienting to the user. In addition, the context of the video (where and at what orientation are the objects being observed) is often as important as the video itself. To overcome the video movement problem and present location context to the user, we present a method for creating a mosaic from the frames of the video while simultaneously georeferencing the mosaic. We demonstrate results using a 5-foot fixed-wing MAV.