Structure from Motion: Combining features correspondences and optical flow

This paper suggests using discrete feature displacements and optical flow simultaneously to determine the camera motion and its velocity. This is advantageous when the number of feature correspondences is low or when the feature correspondences are noisy. The reason is that usually the available optical flow data largely outnumbers the available feature correspondences data. It is also advantageous from the perspective of the instantaneous motion estimation because it gives better estimates for the camera velocity than those obtained from optical flow by itself. We propose a probabilistic framework capitalizing on the this idea. Monte-Carlo filtering is employed due to the non-linearities involved in the problem and to the non-Gaussianity of the measurements¿ probability distributions.