  This paper looks into the problem of grasping unknown objects in a cluttered
environment using 3D point cloud data obtained from a range or an RGBD sensor.
The objective is to identify graspable regions and detect suitable grasp poses
from a single view, possibly, partial 3D point cloud without any apriori
knowledge of the object geometry. The problem is solved in two steps: (1)
identifying and segmenting various object surfaces and, (2) searching for
suitable grasping handles on these surfaces by applying geometric constraints
of the physical gripper. The first step is solved by using a modified version
of region growing algorithm that uses a pair of thresholds for smoothness
constraint on local surface normals to find natural boundaries of object
surfaces. In this process, a novel concept of edge point is introduced that
allows us to segment between different surfaces of the same object. The second
step is solved by converting a 6D pose detection problem into a 1D linear
search problem by projecting 3D cloud points onto the principal axes of the
object surface. The graspable handles are then localized by applying physical
constraints of the gripper. The resulting method allows us to grasp all kinds
of objects including rectangular or box-type objects with flat surfaces which
have been difficult so far to deal with in the grasping literature. The
proposed method is simple and can be implemented in real-time and does not
require any off-line training phase for finding these affordances. The
improvements achieved is demonstrated through comparison with another
state-of-the-art grasping algorithm on various publicly-available and
self-created datasets.
