Parallel range data processing: a real case study

摘要

In this paper, we begin by describing a range image processing method developed to allow an autonomous robot to detect and locate objects. The procedure consists of taking a range image, filtering and preprocessing the image, calculating surface normals, obtaining edge maps from both range and surface normals, combining the edge maps into an initial scene segmentation map, and analyzing each object in the scene in order to detect and locate the desired objects. While this method is reasonably fast and robust, its surface characterization is fairly crude. More elaborate surface characterization on a serial computer would be considerably slower, which is not desirable for autonomous robot operations. We then discuss the development of parallel techniques for surface characterization using range data. By using parallel processing, the complexity and accuracy of the characterization can be increased without an unacceptable cost in processing time. The method explored is a parallel implementation of a least squares QR surface fitting technique using Givens transformations. We conclude by summarizing the work done and briefly listing some future extensions of the current research.