Lightweight Pixel-Wise Generative Robot Grasping Detection Based on RGB-D Dense Fusion

Abstract

Grasping detection is one of the essential tasks for robots to achieve automation and intelligence. The existing grasp detection mainly relies on data-driven discriminative and generative strategies. Generative strategies have significant advantages over discriminative strategies in terms of efficiency. RGB and Depth data are widely used in grasping data sources due to the sufficient amount of information and low cost of acquisition. RGB-D fusion has shown advantages over only using RGB or Depth. However, existing research has mainly focused on early fusion and late fusion, which is challenging to utilize information from both modalities fully. Improving the accuracy of grasping while leveraging the knowledge of both modalities, while ensuring lightweight and real-time is crucial. Therefore, this paper proposes a pixel-wise RGB-D dense fusion method based on a generative strategy. The technique is doubly experimentally validated on public datasets and real robot platform. Accuracy rates of 98.9% and 94.0% are achieved on Cornell and Jacquard datasets, and efficiency of only 15ms is achieved for single image processing. The average success rate of the AUBO i5 robotic platform with DHAG-95 parallel gripper reached 94.0% for single-object scenes, 86.7% for three-object scenes, and 84% for five-object scenes. Our approach has outperformed existing state-of-the-art methods.