Recognition and localization of objects with curved surfaces

Abstract

This paper concerns the problem of recognition and localization of three-dimensional objects from range data. Most of the previous approaches suffered from one or both of the following shortcomings: (1) They dealt with single object scenes and/or (2) they dealt with polyhedral objects or objects that were approximated as polyhedra. The work in this paper addresses both of these shortcomings. The input scenes are allowed to contain multiple objects with partial occlusion. The objects are not restricted to polyhedra but are allowed to have a piecewise combination of curved surfaces, namely, spherical, cylindrical, and conical surfaces. This restriction on the types of curved surfaces is not unreasonable since most objects encountered in an industrial environment can be thus modeled. This paper shows how the qualitative classification of the surfaces based on the signs of the mean and Gaussian curvature can be used to come up withdihedral feature junctions as features to be used for recognition and localization. Dihedral feature junctions are robust to occlusion, offer a viewpoint independent modeling technique for the object models, do not require elaborate segmentation, and the feature extraction process is amenable to parallelism. Hough clustering on account of its ease of parallelization is chosen as the constraint propagation/ satisfaction mechanisms. Experimental results are presented using the Connection Machine. The fine-grained architecture of the Connection Machine is shown to be well suited for the recognition/localization technique presented in this paper.