Egomotion estimation using log-polar images

We address the problem of egomotion estimation of a monocular observer moving with arbitrary translation and rotation in an unknown environment, using log-polar images. The method we propose is uniquely based on the spatio-temporal image derivatives, or the normal flow. Thus, we avoid computing the complete optical flow field, which is an ill-posed problem due to the aperture problem. We use a search paradigm based on geometric properties of the normal flow field, and consider a family of search subspaces to estimate the egomotion parameters. These algorithms are particularly well-suited for the log-polar image geometry, as we use a selection of special normal flow, vectors with simple representation in log-polar coordinates. This approach highlights the close coupling between algorithmic aspects and the sensor geometry (retina physiology), often, found in nature. Finally, we present and discuss a set of experiments, for various kinds of camera motions, which show encouraging results