  This paper presents a spectral correlation-based method (SpectGRASP) for
robotic grasping of arbitrarily shaped, unknown objects. Given a point cloud of
an object, SpectGRASP extracts contact points on the object's surface matching
the hand configuration. It neither requires offline training nor a-priori
object models. We propose a novel Binary Extended Gaussian Image (BEGI), which
represents the point cloud surface normals of both object and robot fingers as
signals on a 2-sphere. Spherical harmonics are then used to estimate the
correlation between fingers and object BEGIs. The resulting spectral
correlation density function provides a similarity measure of gripper and
object surface normals. This is highly efficient in that it is simultaneously
evaluated at all possible finger rotations in SO(3). A set of contact points
are then extracted for each finger using rotations with high correlation
values. We then use our previous work, Local Contact Moment (LoCoMo) similarity
metric, to sequentially rank the generated grasps such that the one with
maximum likelihood is executed. We evaluate the performance of SpectGRASP by
conducting experiments with a 7-axis robot fitted with a parallel-jaw gripper,
in a physics simulation environment. Obtained results indicate that the method
not only can grasp individual objects, but also can successfully clear randomly
organized groups of objects. The SpectGRASP method also outperforms the closest
state-of-the-art method in terms of grasp generation time and grasp-efficiency.
