A Factorized Recursive Estimation of Structure and Motion from Image Velocities

We propose a new approach for the recursive estimation of structure and motion from image velocities. The estimation of structure and motion from image velocities is preferred to the estimation from pixel correspondences when the image displacements are small, since the former approach provides a stronger constraint being based on the instantaneous equation of rigid bodies motion. However the recursive estimation when dealing with image velocities is harder than its counterpart (in the case of pixel correspondences) since the number of points is usually larger and the equations are more involved. For this reason, in contrast to the case of point correspondences, the approaches presented so far are mostly limited to assuming a known 3D motion, or estimating the motion and structure independently. The approach presented in this paper introduces a factorized particle filter for estimating simultaneously the 3D motion and depth. Each particle consists of a 3D motion and a set of probability distributions of the depths of the pixels. The recursive estimation is done in three stages. (1) a resampling and a prediction of new samples; (2) a recursive filtering of the individual depths distributions performed using Extended Kalman Filters; and (3)finally a reweighting of the particles based on the image measurement. Results on simulation data show the efficiency of the approach. Future work will focus on incorporating an estimation of object boundaries to be used in a following regularization step.