Work

Research on a Fusion Technique of YOLOv8-URE-Based 2D Vision and Point Cloud for Robotic Grasping in Stacked Scenarios

Year: 2025

Type: article

Abstract: In industrial robotic grasping tasks, traditional 3D point cloud registration and pose estimation methods often struggle with low efficiency and limited accuracy in stacked and cluttered environments. To address these challenges, this paper proposes a grasp pose estimation algorithm that integrates 2D object detection based on YOLOv8-URE with 3D point cloud registration. In the detection stage, the method enhances object feature perception and localization by optimizing the receptive field structure and introducing attention mechanisms. It also employs an efficient multi-scale feature fusion strategy to improve bounding box regression accuracy. During point cloud processing, target centers predicted by the detector guide rapid segmentation, followed by robust registration techniques to estimate precise object poses. Experimental results demonstrate that YOLOv8-URE improves detection accuracy by 9.21% compared to YOLOv8n, reduces registration time by 60.5%, and significantly increases grasp success rates, proving its reliability and effectiveness in industrial scenarios.

Source: Applied Sciences

DOI: 10.3390/app15126583

Authors: Xuhui Ye, Xiaoyang Qin, Leming Zhan, Jun Wang, Yan Chen

Institutions: Hubei University of Technology, Wuhan Donghu University

Language: English

Topic: Robot Manipulation and Learning

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: Hubei Provincial Department of Education, Natural Science Foundation of Hubei Province