Abstract
A high-fidelity digital simulation environment is crucial for accurately replicating physical operational processes. However, inconsistencies between simulation and physical environments result in low confidence in simulation outcomes, limiting their effectiveness in guiding real-world production. Unlike the traditional step-by-step point cloud "segmentation-registration" generation method, this paper introduces, for the first time, a novel Multi-Robot Manufacturing Digital Scene Generation (MRG) method that leverages multi-instance point cloud registration, specifically within manufacturing scenes. Tailored to the characteristics of industrial robots and manufacturing settings, an instance-focused transformer module is developed to delineate instance boundaries and capture correlations between local regions. Additionally, a hypothesis generation module is proposed to extract target instances while preserving key features. Finally, an efficient screening and optimization algorithm is designed to refine the final registration results. Experimental evaluations on the Scan2CAD and Welding-Station datasets demonstrate that: (1) the proposed method outperforms existing multi-instance point cloud registration techniques; (2) compared to state-of-the-art methods, the Scan2CAD dataset achieves improvements in MR and MP by 12.15% and 17.79%, respectively; and (3) on the Welding-Station dataset, MR and MP are enhanced by 16.95% and 24.15%, respectively. This work marks the first application of multi-instance point cloud registration in manufacturing scenes, significantly advancing the precision and reliability of digital simulation environments for industrial applications.
Abstract (translated)
高保真数字仿真环境对于准确再现物理操作过程至关重要。然而，仿真与物理环境之间的不一致性导致了对仿真结果的信心不足，从而限制了其在指导现实世界生产中的有效性。不同于传统的逐点云“分割-注册”生成方法，本文首次提出了一种新颖的多机器人制造数字场景生成（MRG）方法，该方法利用了制造场景中多实例点云注册技术。针对工业机器人和制造环境的特点，开发了一个以实例为中心的变压器模块来界定实例边界并捕捉局部区域间的相关性。此外，还提出了一个假设生成模块来提取目标实例的同时保留关键特征。最后，设计了一种高效的筛选和优化算法来精炼最终的注册结果。在Scan2CAD和Welding-Station数据集上的实验评估表明：(1) 提出的方法优于现有的多实例点云注册技术；(2) 与最先进的方法相比，在Scan2CAD数据集中，MR（匹配率）和MP（模型精确度）分别提高了12.15% 和17.79%；(3) 在Welding-Station数据集中，MR和MP分别提升了16.95% 和24.15%。这项工作标志着多实例点云注册技术在制造场景中的首次应用，显著推进了工业应用中数字仿真环境的精度和可靠性。
URL
https://arxiv.org/abs/2501.02041
