  Grasping objects is a fundamental yet important capability of robots, and
many tasks such as sorting and picking rely on this skill. The prerequisite for
stable grasping is the ability to correctly identify suitable grasping
positions. However, finding appropriate grasping points is challenging due to
the diverse shapes, varying density distributions, and significant differences
between the barycenter of various objects. In the past few years, researchers
have proposed many methods to address the above-mentioned issues and achieved
very good results on publicly available datasets such as the Cornell dataset
and the Jacquard dataset. The problem is that the backgrounds of Cornell and
Jacquard datasets are relatively simple - typically just a whiteboard, while in
real-world operational environments, the background could be complex and noisy.
Moreover, in real-world scenarios, robots usually only need to grasp fixed
types of objects. To address the aforementioned issues, we proposed a
large-scale grasp detection dataset called NBMOD: Noisy Background Multi-Object
Dataset for grasp detection, which consists of 31,500 RGB-D images of 20
different types of fruits. Accurate prediction of angles has always been a
challenging problem in the detection task of oriented bounding boxes. This
paper presents a Rotation Anchor Mechanism (RAM) to address this issue.
Considering the high real-time requirement of robotic systems, we propose a
series of lightweight architectures called RA-GraspNet (GraspNet with Rotation
Anchor): RARA (network with Rotation Anchor and Region Attention), RAST
(network with Rotation Anchor and Semi Transformer), and RAGT (network with
Rotation Anchor and Global Transformer) to tackle this problem. Among them, the
RAGT-3/3 model achieves an accuracy of 99% on the NBMOD dataset. The NBMOD and
our code are available at https://github.com/kmittle/Grasp-Detection-NBMOD.
