Network architecture based on improved DenseFusion algorithm research on the recognition and grasping method of robotic arm

Hui Qi; Shanhe Gong

doi:10.1117/12.2681054

23 May 2023 Network architecture based on improved DenseFusion algorithm research on the recognition and grasping method of robotic arm

Hui Qi, Shanhe Gong

Author Affiliations +

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126452V (2023) https://doi.org/10.1117/12.2681054
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Aiming at the problem of partial occlusion and stacking in the field of robotic arm recognition and grasping, especially in non-single scenes where multiple objects are cluttered. This paper proposes a recognition and grasping method based on the improved DenseFusion algorithm network architecture to improve pose estimation’s accuracy rate and combined with the RGB information and depth information of the depth camera to estimate the 6D pose of the target, and the experimental verification was carried out on the robotic arm platform independently developed by the laboratory. The experimental results show that the proposed method can not only identify the target object accurately, but also obtain the 6D pose of the target object accurately, so as to improve the success rate of grasping by the robotic arm.

Citation Download Citation

Hui Qi and Shanhe Gong "Network architecture based on improved DenseFusion algorithm research on the recognition and grasping method of robotic arm", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126452V (23 May 2023); https://doi.org/10.1117/12.2681054

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