Egomotion estimation using planar and non-planar constraints

There are two major approaches for estimating camera motion (egomotion) given an image sequence. Each approach has own strengths and weaknesses. One approach is the feature based methods. In this approach the point feature correspondences are taken as the input. Since initially the depths of point features are unknown, the egomotion is estimated by the depth independent epipolar constraints on the point feature correspondences. This approach is robust in practice, but is relatively limited in accuracy since it exploits no structure assumption, such as planarity. The other approach, termed the direct method, has the advantage in its accuracy. In this method, the egomotion is estimated as the parameters of a homography by directly aligning the planar potion of two images. The direct method may be preferable in the cases with known planes that are persistent in the view. The on-board camera system for ground vehicles is a representative example. Despite the potential accuracy, the direct method fails when the plane lacks proper texture. We propose an egomotion estimation method that is based on both the homographic constraint on a planar region, and on the epipolar constraint on generally non-planar regions, so that the both kinds of visual cues contribute to the estimation. We observe that the method improves the egomotion estimation in robustness while retaining the comparable accuracy to the direct method.