This paper describes a technique for the estimation of the translational and rotational velocities of a miniature helicopter using the video signals from a single onboard camera. For every two consecutive frames from the camera, point correspondences are identified and Epipolar Geometry based algorithms are used to find the likely estimates of the absolute rotations and relative displacements. Images from onboard camera are often corrupted with various types of noises; SIFT descriptors were found to be the best feature descriptors to be used for point correspondences. To speed up the processing, we introduce a new representation of these descriptors based on compressive sensing formalisms. To estimate the absolute displacement of the helicopter between frames, we use the measurements from a simulated IR sensor to find the true change in altitude of the body frame, scaling other translational dimensions accordingly, and later estimating the velocities. Experiments conducted using data from a real helicopter in an indoor environment demonstrate promising results.