Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation

Kaixin Bai1,2; Lei Zhang1,2; Zhaopeng Chen2; Fang Wan3; Jianwei Zhang1

10.1109/ICRA57147.2024.10611401

2024-05-17

979-8-3503-8458-1

2024 IEEE International Conference on Robotics and Automation (ICRA)

13-17 May 2024

Yokohama, Japan

Despite the substantial progress in deep learning, its adoption in industrial robotics projects remains limited, primarily due to challenges in data acquisition and labeling. Previous sim2real approaches using domain randomization require extensive scene and model optimization. To address these issues, we introduce an innovative physically-based structured light simulation system, generating both RGB and physically realistic depth images, surpassing previous dataset generation tools. We create an RGBD dataset tailored for robotic industrial grasping scenarios and evaluate it across various tasks, including object detection, instance segmentation, and embedding sim2real visual perception in industrial robotic grasping. By reducing the sim2real gap and enhancing deep learning training, we facilitate the application of deep learning models in industrial settings. Project details are available at https://baikaixin-public.github.io/structured_light_3D_synthesizer/

其他

EI

1.Department of Informatics, TAMS (Technical Aspects of Multimodal Systems), Universität Hamburg, Germany
2.Agile Robots AG, Munich, Germany
3.School of Design, Southern University of Science and Technology, Shenzhen, China

Kaixin Bai,Lei Zhang,Zhaopeng Chen,et al. Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation[C],2024.