In this research, a new cooperative matching algorithm is developed based on the integration of stereo and motion cues. In this algorithm, depth and image flow values are recovered from two successive pairs of stereo images by solving the stereo and motion correspondence problems. Feature points are extracted from the images as matching objects. The entire matching process composes of a network of four subprocesses (two for stereo and two for motion). Each of the subprocesses can access information from connected nodes to perform the disambiguation. The "best" matches are obtained in a relaxation manner using the 3-D continuity constraint. Using the matching results, methods for passive navigation are also discussed. The rotational and translational components of a rigid body motion are...[ Read more ]

In this research, a new cooperative matching algorithm is developed based on the integration of stereo and motion cues. In this algorithm, depth and image flow values are recovered from two successive pairs of stereo images by solving the stereo and motion correspondence problems. Feature points are extracted from the images as matching objects. The entire matching process composes of a network of four subprocesses (two for stereo and two for motion). Each of the subprocesses can access information from connected nodes to perform the disambiguation. The "best" matches are obtained in a relaxation manner using the 3-D continuity constraint. Using the matching results, methods for passive navigation are also discussed. The rotational and translational components of a rigid body motion are recovered from a set of 3-D point correspondences using three least-square approximation approaches. Experimental results are presented to illustrate the performances of these methods.