Object Location by Parallel Pose Clustering

This paper describes the location of 3D objects in either depth or intensity data using parallel pose clustering. A leader-based partitional algorithm is used that allows the number of clusters to be selected on the basis of the input data, which is important because the number of pose clusters cannot usually be determined in advance. In comparison with previous work, no assumptions are made about the number or distribution of data patterns, or that the processor topology should be matched to this distribution. After overcoming a parallel bottleneck, we show that our approach exhibits superlinear speedup, since the overall computation is reduced in the parallel system. Isolated pose estimates may be eliminated from the cluster space after an initial stage, which may be done with low probability of missing a true cluster. The algorithm has been tested using real and synthetic data on a transputer-based MIMD architecture. © 1998 Academic Press.