Open-VICO: An Open-Source Gazebo Toolkit for Vision-based Skeleton Tracking in Human-Robot Collaboration

TL;DR

Open-VICO is an open-source Gazebo toolkit that enables realistic simulation of human models, multi-camera vision setups, and human tracking for advancing research in safe human-robot collaboration.

Contribution

It introduces a comprehensive, flexible simulation toolkit integrating virtual human models, multi-camera vision, and motion capture for human-robot interaction research.

Findings

Simulated multi-RGB-D camera calibration in Gazebo

Generated synthetic human skeleton tracking datasets

Demonstrated multi-camera human skeleton tracking scenarios

Abstract

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.