论文标题
Open-VICO:一种用于人类机器人协作中基于视觉骨架跟踪的开源凉亭工具包
Open-VICO: An Open-Source Gazebo Toolkit for Vision-based Skeleton Tracking in Human-Robot Collaboration
论文作者
论文摘要
模拟工具对于机器人研究至关重要,尤其是对于那些至关重要的那些领域,例如人类机器人协作(HRC)。但是,模拟人类行为是一项挑战,现有的机器人模拟器不会整合功能性的人类模型。这项工作介绍了开放式VICO,这是一种开源工具包,旨在将虚拟人类模型集成到凉亭中,重点关注基于视觉的人类跟踪。尤其是,开放式维科允许在相同的仿真环境中结合现实的人体运动模型,多相机视觉设置以及人类跟踪技术,以及众多的机器人和传感器模型,得益于凉亭。将预录的人类骨骼运动与运动捕获系统结合在一起的可能性扩大了人类机器人相互作用(HRI)设置中人类绩效行为分析的景观。 To describe the functionalities and stress the potential of the toolkit four specific examples, chosen among relevant literature challenges in the field, are developed using our simulation utils: i) 3D multi-RGB-D camera calibration in simulation, ii) creation of a synthetic human skeleton tracking dataset based on OpenPose, iii) multi-camera scenario for human skeleton tracking in simulation, and iv) a human-robot interaction 例子。这项工作的关键是创建一条直接的管道,我们希望该管道能够激发对基于新的视觉的算法和方法的研究,以实现轻巧的人体跟踪和灵活的人类机器人应用。
Simulation tools are essential for robotics research, especially for those domains in which safety is crucial, such as Human-Robot Collaboration (HRC). However, it is challenging to simulate human behaviors, and existing robotics simulators do not integrate functional human models. This work presents Open-VICO, an open-source toolkit to integrate virtual human models in Gazebo focusing on vision-based human tracking. In particular, Open-VICO allows to combine in the same simulation environment realistic human kinematic models, multi-camera vision setups, and human-tracking techniques along with numerous robot and sensor models thanks to Gazebo. The possibility to incorporate pre-recorded human skeleton motion with Motion Capture systems broadens the landscape of human performance behavioral analysis within Human-Robot Interaction (HRI) settings. To describe the functionalities and stress the potential of the toolkit four specific examples, chosen among relevant literature challenges in the field, are developed using our simulation utils: i) 3D multi-RGB-D camera calibration in simulation, ii) creation of a synthetic human skeleton tracking dataset based on OpenPose, iii) multi-camera scenario for human skeleton tracking in simulation, and iv) a human-robot interaction example. The key of this work is to create a straightforward pipeline which we hope will motivate research on new vision-based algorithms and methodologies for lightweight human-tracking and flexible human-robot applications.