  In this paper, we present Segmentation-Based Grasp Detection Network (SGDN)
to predict a feasible robotic grasping for a unsymmetrical three-finger robotic
gripper using RGB images. The feasible grasping of a target should be a
collection of grasp regions with the same grasp angle and width. In other
words, a simplified planar grasp representation should be pixel-level rather
than region-level such as five-dimensional grasp representation.Therefore, we
propose a pixel-level grasp representation, oriented base-fixed triangle. It is
also more suitable for unsymmetrical three-finger gripper which cannot grasp
symmetrically when grasping some objects, the grasp angle is at [0, 2{\pi})
instead of [0, {\pi}) of parallel plate gripper.In order to predict the
appropriate grasp region and its corresponding grasp angle and width in the RGB
image, SGDN uses DeepLabv3+ as a feature extractor, and uses a three-channel
grasp predictor to predict feasible oriented base-fixed triangle grasp
representation of each pixel.On the re-annotated Cornell Grasp Dataset, our
model achieves an accuracy of 96.8% and 92.27% on image-wise split and
object-wise split respectively, and obtains accurate predictions consistent
with the state-of-the-art methods.
