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

NBMOD: Find It and Grasp It in Noisy Background