Semantic-Aware Environment Perception for Mobile Human-Robot Interaction
This work addresses the challenge of reliable and fast semantic perception for mobile assistive robots, which is incremental as it builds on existing research but focuses on practical deployment.
The paper tackles the problem of enabling mobile robots to understand their environment semantically for better human-robot interaction, presenting a vision-based system that achieves this without prior knowledge and is tested on a humanoid robot in real-world scenarios.
Current technological advances open up new opportunities for bringing human-machine interaction to a new level of human-centered cooperation. In this context, a key issue is the semantic understanding of the environment in order to enable mobile robots more complex interactions and a facilitated communication with humans. Prerequisites are the vision-based registration of semantic objects and humans, where the latter are further analyzed for potential interaction partners. Despite significant research achievements, the reliable and fast registration of semantic information still remains a challenging task for mobile robots in real-world scenarios. In this paper, we present a vision-based system for mobile assistive robots to enable a semantic-aware environment perception without additional a-priori knowledge. We deploy our system on a mobile humanoid robot that enables us to test our methods in real-world applications.