Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments

TL;DR

This paper presents a novel Sim2Real domain adaptation algorithm using a dual-stream network for 3D point cloud segmentation, significantly improving accuracy and robustness in industrial human-robot collaboration environments.

Contribution

It introduces a dual-stream network architecture combining DGCNN and CNN with residual layers for effective Sim2Real domain adaptation in industrial point cloud segmentation.

Findings

Achieved 97.76% segmentation accuracy on real-world HRC data.

Demonstrated superior robustness over existing methods.

Validated effectiveness in real industrial environments.

Abstract

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.