Human-Robot Collaboration for Semantic Labeling of the Environment

Today’s robots are able to perform more and more complex tasks, which usually require a high degree of interaction with the environment they have to operate in. As a consequence, robotic systems should have a deep and specific knowledge of their workspaces, which goes far beyond a simple metric representation a robotic system can build up through SLAM (Simultaneous Localization and Mapping). In this paper, we present a novel human-robot collaboration approach, designed to extract 3D shapes associated to objects of interest pointed out by a human operator. The information regarding the segmented objects are then integrated into a metric map, built by the robot, providing a high-level representation of the environment that embodies all the knowledge required by a robot to actually execute complex tasks.