  Grasping of novel objects in pick and place applications is a fundamental and
challenging problem in robotics, specifically for complex-shaped objects. It is
observed that the well-known strategies like \textit{i}) grasping from the
centroid of object and \textit{ii}) grasping along the major axis of the object
often fails for complex-shaped objects. In this paper, a real-time grasp pose
estimation strategy for novel objects in robotic pick and place applications is
proposed. The proposed technique estimates the object contour in the point
cloud and predicts the grasp pose along with the object skeleton in the image
plane. The technique is tested for the objects like ball container, hand
weight, tennis ball and even for complex shape objects like blower (non-convex
shape). It is observed that the proposed strategy performs very well for
complex shaped objects and predicts the valid grasp configurations in
comparison with the above strategies. The experimental validation of the
proposed grasping technique is tested in two scenarios, when the objects are
placed distinctly and when the objects are placed in dense clutter. A grasp
accuracy of 88.16\% and 77.03\% respectively are reported. All the experiments
are performed with a real UR10 robot manipulator along with WSG-50 two-finger
gripper for grasping of objects.
