Object recognition on the hypercube

The authors present a parallel interpretation tree search algorithm for object recognition using sparse range or tactile data on the Intel iPSC/2 hypercube multicomputer. The objects are typically those which can be approximated as a piecewise combination of polyhedra and which a robot would encounter in an industrial scene during the process of automated inspection or automated assembly. Three strategies for mapping the interpretation tree search process on the hypercube are considered. These are breadth-first mapping, depth-first mapping and depth-first mapping with load sharing. The algorithm has been experimentally verified on synthetic tactile data from two-dimensional scenes. It has shown that the requirement for uniform load sharing leads to increased inter-processor communication