Three-dimensional descriptors are a common tool nowadays, used in a wide range
of tasks. Most of the descriptors that have been proposed in the literature
focus on tasks such as object recognition and identification. This paper
proposes a novel three-dimensional local descriptor, structured as a set of
histograms of the curvature observed on the surface of the object in different
directions. This descriptor is designed with a focus on the resolution of the
robotic grasping problem, especially on the determination of the orientation
required to grasp an object. We validate our proposal following a data-driven
approach using grasping information and examples generated using the Gazebo
simulator and a simulated PR2 robot. Experimental results show that the
proposed descriptor is well suited for the grasping problem, exceeding the
performance observed with recent descriptors.