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| 4.49 MB | Adobe PDF |
Advisor(s)
Abstract(s)
Object grasping is a task that humans do without
major concerns. This results from self learning and by observing
of other skilled humans doing such task with previous information.
However, grasping novel objects in unknown positions for a
robot is a complex task which encounters many problems, such as
sub-optimal performance rates and the time consumption. In this
paper we present a method that complements the state-of-the-art
grasping algorithms with two segmentation steps, the first one
which removes the largest planar surface in the point cloud of the
world before the grasp detector receives them and the second one
that complements this segmentation with another segmentation
that calculates where the object is located and segments the
point cloud by executing a crop around the object. The proposed
method significantly improves the grasping success rate (100%
improvement over the baseline approach) and simultaneously is
able to reduce the time consumption by 23%.