Sparse pose manifolds

Abstract

The efficient manipulation of randomly placed objects relies on the accurate estimation of their 6 DoF geometrical configuration. In this paper we tackle this issue by following the intuitive idea that different objects, viewed from the same perspective, should share identical poses and, moreover, these should be efficiently projected onto a well-defined and highly distinguishable subspace. This hypothesis is formulated here by the introduction of pose manifolds relying on a bunch-based structure that incorporates unsupervised clustering of the abstracted visual cues and encapsulates appearance and geometrical properties of the objects. The resulting pose manifolds represent the displacements among any of the extracted bunch points and the two foci of an ellipse fitted over the members of the bunch-based structure. We post-process the established pose manifolds via $$l_1$$ l 1 norm minimization so as to build sparse and highly representative input vectors that are characterized by large discrimination capabilities. While other approaches for robot grasping build high dimensional input vectors, thus increasing the complexity of the system, in contrast, our method establishes highly distinguishable manifolds of low dimensionality. This paper represents the first integrated research endeavor in formulating sparse pose manifolds, with experimental results providing evidence of low generalization error, justifying thus our theoretical claims.