In this paper, we propose a novel method to estimate the camera’s ego-motion parameters by directly using the normal flows. Normal flows, the projection of the optical flows along the direction of the gradient of image intensity, could be calculated directly from the image sequence without any artificial assumptions about the captured scene. Different from many traditional approaches which tackle the problem by establishing motion correspondences or by estimating optical flows, our proposed method could obtain the motion parameters directly by using the information of spatio-temporal gradient of the image intensity. Hence, our method requires no specific assumptions about the captured scene, such as the smoothness constraint, continuity constraint, distinct features appearing in the scene and etc.. Our method has been experimentally tested by using both synthetic image data and real image sequences. The experimental results demonstrate that our proposed method is feasible and reliable.
An autonomous system must have the capability of estimating or controlling its own motion parameters. There already exit tens of research work to fulfill the task. However, most of them are based on the motion correspondences establishment or full optical flows estimation. The above solutions put restrictions on the scene: either there must be presence of enough distinct features, or there must be dense texture. Different from the traditional works, utilizing no motion correspondences or epipolar geometry, we start from the normal flow data, ensure good use of every piece of them because they could only be sparsely available. We apply the spherical image model to avoid the ambiguity in describing the camera motion. Since each normal flow gives a locus for the location of the camera motion, the intersection of such loci offered by different data points will narrow the possibilities of the camera motion and even pinpoint it. A voting scheme in φ-θ domain is applied to simplify the 3D voting space to a 2D voting space. We tested the algorithms introduced above by using both synthetic image data and real image sequences. Experimental results are shown to illustrate the potential of the methods.
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