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This paper looks into the problem of grasping unknown objects in a clutteredenvironment using 3D point cloud data obtained from a range or an RGBD sensor.The objective is to identify graspable regions and detect suitable grasp posesfrom a single view, possibly, partial 3D point cloud without any aprioriknowledge of the object geometry. The problem is solved in two steps: (1)identifying and segmenting various object surfaces and, (2) searching forsuitable grasping handles on these surfaces by applying geometric constraintsof the physical gripper. The first step is solved by using a modified versionof region growing algorithm that uses a pair of thresholds for smoothnessconstraint on local surface normals to find natural boundaries of objectsurfaces. In this process, a novel concept of edge point is introduced thatallows us to segment between different surfaces of the same object. The secondstep is solved by converting a 6D pose detection problem into a 1D linearsearch problem by projecting 3D cloud points onto the principal axes of theobject surface. The graspable handles are then localized by applying physicalconstraints of the gripper. The resulting method allows us to grasp all kindsof objects including rectangular or box-type objects with flat surfaces whichhave been difficult so far to deal with in the grasping literature. Theproposed method is simple and can be implemented in real-time and does notrequire any off-line training phase for finding these affordances. Theimprovements achieved is demonstrated through comparison with anotherstate-of-the-art grasping algorithm on various publicly-available andself-created datasets.

A Novel Geometry-based Algorithm for Robust Grasping in Extreme Clutter Environment