In this paper, a quick and efficient method is presented for grasping unknown objects in clutter. The grasping method relies on real-time superquadric (SQ) representation of partial view objects and incomplete object modelling, well suited for unknown symmetric objects in cluttered scenarios which is followed by optimized antipodal grasping. The incomplete object models are processed through a mirroring algorithm that assumes symmetry to first create an approximate complete model and then fit for SQ representation. The grasping algorithm is designed for maximum force balance and stability, taking advantage of the quick retrieval of dimension and surface curvature information from the SQ parameters. The pose of the SQs with respect to the direction of gravity is calculated and used together with the parameters of the SQs and specification of the gripper, to select the best direction of approach and contact points. The SQ fitting method has been tested on custom datasets containing objects in isolation as well as in clutter. The grasping algorithm is evaluated on a PR2 and real time results are presented. Initial results indicate that though the method is based on simplistic shape information, it outperforms other learning based grasping algorithms that also work in clutter in terms of time-efficiency and accuracy.
A considerable amount of work exists in the area of finding feasible grasping points on novel objects using vision based systems. Initial efforts using 2D images to find grasping points in presented in @cite_9 . The arrival of economic RGB-D cameras allowed interpreting objects in 3D, which led to object identification as point clouds and derived parameters. Different models have been utilized for this task, such as representing objects by implicit polynomial and algebric invariants @cite_14 , spherical harmonics @cite_3 @cite_8 , geons @cite_20 , generalized cylinders @cite_32 , symmetry-seeking models @cite_15 mgp_approx_symm_sig_06 , blob models @cite_28 , union of balls @cite_11 and hyperquadrics @cite_21 , to name a few.
@cite_14 @cite_8 @cite_28 @cite_9 @cite_21 @cite_32 @cite_3 @cite_15 @cite_20 @cite_11
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We treat the use of more complex higher degree polynomial curves and surfaces of degree higher than 2, which have many desirable properties for object recognition and position estimation, and attack the instability problem arising in their use with partial and noisy data. The scenario discussed in this paper is one where we have a set of objects that are modeled as implicit polynomial functions, or a set of representations of classes of objects with each object in a class modeled as an implicit polynomial function, stored in the database. Then, given partial data from one of the objects, we want to recognize the object (or the object class) or collect more data in order to get better parameter estimates for more reliable recognition. Two problems arising in this scenario are discussed: 1) the problem of recognizing these polynomials by comparing them in terms of their coefficients; and 2) the problem of where to collect data so as to improve the parameter estimates as quickly as possible. We use an asymptotic Bayesian approximation for solving the two problems. The intrinsic dimensionality of polynomials and the use of the Mahalanobis distance are discussed. We consider the problem of grasping novel objects, specifically objects that are being seen for the first time through vision. Grasping a previously unknown object, one for which a 3-d model is not available, is a challenging problem. Furthermore, even if given a model, one still has to decide where to grasp the object. We present a learning algorithm that neither requires nor tries to build a 3-d model of the object. Given two (or more) images of an object, our algorithm attempts to identify a few points in each image corresponding to good locations at which to grasp the object. This sparse set of points is then triangulated to obtain a 3-d location at which to attempt a grasp. This is in contrast to standard dense stereo, which tries to triangulate every single point in an image (and often fails to return a good 3-d model). Our algorithm for identifying grasp locations from an image is trained by means of supervised learning, using synthetic images for the training set. We demonstrate this approach on two robotic manipulation platforms. Our algorithm successfully grasps a wide variety of objects, such as plates, tape rolls, jugs, cellphones, keys, screwdrivers, staplers, a thick coil of wire, a strangely shaped power horn and others, none of which were seen in the training set. We also apply our method to the task of unloading items from dishwashers. The shape representation and modeling based on implicit functions have received considerable attention in computer vision literature. In this paper, we propose extended hyperquadrics, as a generalization of hyperquadrics developed by Hanson, for modeling global geometric shapes. The extended hyperquadrics can strengthen the representation power of hyperquadrics, especially for the object with concavities. We discuss the distance measures between extended hyperquadric surfaces and given data set and their minimization to obtain the optimum model parameters. We present several experimental results for fitting extended hyperquadrics to 3D real and synthetic data. We demonstrate that extended hyperquadrics can model more complex shapes than hyperquadrics, maintaining many desirable properties of hyperquadrics. Generalized cylinder (GC) is a class of parametric shapes that is very flexible and capable of modeling many different types of real-world objects, and have subsequently been the focus of considerable research in the vision community. Most of the related works proposed previously have dealt with the recovery of 3D shape description of objects based on the GC representation from one or more 2D image data. Different from the objective of the previous works, in this paper, we will propose a new approach to obtain a GC-based shape description of 3D objects. The proposed approach of deriving the GC axis is a further extension of the potential-based skeletonization approach presented in (IEEE Trans. Pattern Anal. Mach. Intell. 22(11) (2000) 1241). Simulation results demonstrate that the derived GC representation will yield better approximation of object shape than that based on simpler subclasses of GC since there is, in principle, no restriction on the topology of the GC axis and the shape of the cross-sections. In this paper, we present a new and efficient spherical harmonics decomposition for spherical functions defining 3D triangulated objects. Such spherical functions are intrinsically associated to star-shaped objects. However, our results can be extended to any triangular object after segmentation into star-shaped surface patches and recomposition of the results in the implicit framework. There is thus no restriction about the genus number of the object. We demonstrate that the evaluation of the spherical harmonics coefficients can be performed by a Monte Carlo integration over the edges, which makes the computation more accurate and faster than previous techniques, and provides a better control over the precision error in contrast to the voxel-based methods. We present several applications of our research, including fast spectral surface reconstruction from point clouds, local surface smoothing and interactive geometric texture transfer. We propose models of 3D shape which may be viewed as deformable bodies composed of simulated elastic material. In contrast to traditional, purely geometric models of shape, deformable models are active—their shapes change in response to externally applied forces. We develop a deformable model for 3D shape which has a preference for axial symmetry. Symmetry is represented even though the model does not belong to a parametric shape family such as (generalized) cylinders. Rather, a symmetry-seeking property is designed into internal forces that constrain the deformations of the model. We develop a framework for 3D object reconstruction based on symmetry-seeking models. Instances of these models are formed from monocular image data through the action of external forces derived from the data. The forces proposed in this paper deform the model in space so that the shape of its projection into the image plane is consistent with the 2D silhouette of an object of interest. The effectiveness of our approach is demonstrated using natural images. Typical tasks of future service robots involve grasping and manipulating a large variety of objects differing in size and shape. Generating stable grasps on 3D objects is considered to be a hard problem, since many parameters such as hand kinematics, object geometry, material properties and forces have to be taken into account. This results in a high-dimensional space of possible grasps that cannot be searched exhaustively. We believe that the key to find stable grasps in an efficient manner is to use a special representation of the object geometry that can be easily analyzed. In this paper, we present a novel grasp planning method that evaluates local symmetry properties of objects to generate only candidate grasps that are likely to be of good quality. We achieve this by computing the medial axis which represents a 3D object as a union of balls. We analyze the symmetry information contained in the medial axis and use a set of heuristics to generate geometrically and kinematically reasonable candidate grasps. These candidate grasps are tested for force-closure. We present the algorithm and show experimental results on various object models using an anthropomorphic hand of a humanoid robot in simulation.