Mobile vehicle's egomotion estimation from time varying image sequences
Sparse optical flow estimated by an image sequence, often, is enough to obtain useful information about camera motion. Normally, matching among features on image sequence is estimated by radiometric similarity. When the camera is moving on a flat surface (planer navigation) additional constraints can be introduced in order to obtain a more robust estimation of the optical flow. In this paper, an energy based optimization method to compute a robust sparse optical flow using global constraints induced by camera motion, is proposed. Convergence is reached in few iterations, making the proposed method suitable for real-time applications.