ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
Source abstract
Abstract
In this article, to further improve the accuracy and speed of grasp detection for unknown objects, a new omni-dimensional dynamic convolution grasp detection network (ODGNet) is proposed. The ODGNet includes two key designs. Firstly, it integrates omni-dimensional dynamic convolution to enhance the feature extraction of the graspable region. Secondly, it employs a grasping region feature enhancement fusion module to refine the features of the graspable region and promote the separation of the graspable region from the background. The ODGNet attained an accuracy of 98.4% and 97.8% on the image-wise and object-wise subsets of the Cornell dataset, respectively. Moreover, the ODGNet’s detection speed can reach 50 fps. A comparison with previous algorithms shows that the ODGNet not only improves the grasp detection accuracy, but also satisfies the requirement of real-time grasping. The grasping experiments in the simulation environment verify the effectiveness of the proposed algorithm.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robot Manipulation and Learning · Hand Gesture Recognition Systems · Muscle activation and electromyography studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Engineering, Shanghai Ocean University, No. 999 Hucheng Ring Road, Shanghai 201306, China
Funding
National Key Research and Development Program of China: Design and Development of High Performance Marine Electric Field Sensor
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