提出新算法，让机器人在真实工厂中更准识别3D环境。
Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments
- 用双流网络融合图卷积与卷积神经网，实现仿真到真实的平滑迁移。
- 在真实工业场景中达到97.76%分割准确率，优于现有方法。
- 适合需要高精度环境理解的工业人机协作项目使用。
精准解析三维环境对人机协作（HRC）应用至关重要，安全与效率是核心需求。语义分割在此背景下发挥关键作用，可实现对环境的精确与细致理解。由于真实工业场景中高质量标注数据极度稀缺，本研究提出一种面向工业环境点云语义分割的开创性Sim2Real领域自适应方法。重点在于构建一个能从仿真环境稳定迁移到真实应用的网络模型，提升其在安全人机协作中的实用价值。本文提出一种双流网络架构（FUSION），结合动态图卷积神经网络（DGCNN）与带残差层的卷积神经网络（CNN），作为该领域的自适应算法。所提模型在真实工业协作场景与仿真点云数据上进行评估，展现出当前最优性能，达到97.76%的分割准确率，并显著优于已有方法的鲁棒性。
原文摘要 · Abstract (English)
The robust interpretation of 3D environments is crucial for human-robot collaboration (HRC) applications, where safety and operational efficiency are paramount. Semantic segmentation plays a key role in this context by enabling a precise and detailed understanding of the environment. Considering the intense data hunger for real-world industrial annotated data essential for effective semantic segmentation, this paper introduces a pioneering approach in the Sim2Real domain adaptation for semantic segmentation of 3D point cloud data, specifically tailored for HRC. Our focus is on developing a network that robustly transitions from simulated environments to real-world applications, thereby enhancing its practical utility and impact on a safe HRC. In this work, we propose a dual-stream network architecture (FUSION) combining Dynamic Graph Convolutional Neural Networks (DGCNN) and Convolutional Neural Networks (CNN) augmented with residual layers as a Sim2Real domain adaptation algorithm for an industrial environment. The proposed model was evaluated on real-world HRC setups and simulation industrial point clouds, it showed increased state-of-the-art performance, achieving a segmentation accuracy of 97.76%, and superior robustness compared to existing methods.
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