Parallel dense depth-from-motion on the image understanding architecture

Abstract

The design and implementation of a single instruction multiple data depth-from-motion algorithm on the image understanding architecture simulator are described. Correspondences are established in parallel for two temporarily separated images through correlation. The correspondences are used to determine the translational and rotational motion parameters of the camera through a parallel motion algorithm. This is done by first determining the appropriate translational parameters and then constraining the search for the exact translational and rotational parameters. The dense depth map is computed from the image correspondences and the computed motion parameters. Results are analyzed for three image sequences acquired from mobile vehicles. Depths are obtained at an average accuracy of about 8% in outdoor image sequences. The depth maps are processed to locate relatively small obstacles, like cans and cones, to a distance of about 60 ft. Large obstacles, like hills, are located even when they are much further away. >