  General object grasping is an important yet unsolved problem in the field of
robotics. Most of the current methods either generate grasp poses with few DoF
that fail to cover most of the success grasps, or only take the unstable depth
image or point cloud as input which may lead to poor results in some cases. In
this paper, we propose RGBD-Grasp, a pipeline that solves this problem by
decoupling 7-DoF grasp detection into two sub-tasks where RGB and depth
information are processed separately. In the first stage, an encoder-decoder
like convolutional neural network Angle-View Net(AVN) is proposed to predict
the SO(3) orientation of the gripper at every location of the image.
Consequently, a Fast Analytic Searching(FAS) module calculates the opening
width and the distance of the gripper to the grasp point. By decoupling the
grasp detection problem and introducing the stable RGB modality, our pipeline
alleviates the requirement for the high-quality depth image and is robust to
depth sensor noise. We achieve state-of-the-art results on GraspNet-1Billion
dataset compared with several baselines. Real robot experiments on a UR5 robot
with an Intel Realsense camera and a Robotiq two-finger gripper show high
success rates for both single object scenes and cluttered scenes. Our code and
trained model will be made publicly available.
