MRG: A Multi-Robot Manufacturing Digital Scene Generation Method Using Multi-Instance Point Cloud Registration

TL;DR

This paper introduces MRG, a novel multi-instance point cloud registration method tailored for manufacturing scenes, significantly improving digital environment accuracy for industrial robots and processes.

Contribution

It presents the first application of multi-instance point cloud registration in manufacturing, with a new transformer-based module and optimization algorithm for enhanced scene generation.

Findings

Outperforms existing registration techniques.

Achieves 12.15% and 17.79% improvements on Scan2CAD.

Achieves 16.95% and 24.15% improvements on Welding-Station.

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.