Fast, unconstrained camera motion estimation from stereo without tracking and robust statistics

Camera motion estimation is useful for a range of applications. Usually, feature tracking is performed through the sequence of images to determine correspondences. Furthermore, robust statistical techniques are normally used to handle large number of outliers in correspondences. This paper proposes a new method that avoids both. Motion is calculated between two consecutive stereo images without any pre-knowledge or prediction about feature location or the possibly large camera movement. This permits a lower frame rate and almost arbitrary movements. Euclidean constraints are used to incrementally select inliers from a set of initial correspondences, instead of using robust statistics that has to handle all inliers and outliers together. These constraints are so strong that the set of initial correspondences can contain several times more outliers than inliers. Experiments on a worst-case stereo sequence show that the method is robust, accurate and can be used in real-time.