SemRob: Towards Semantic Stream Reasoning for Robotic Operating Systems
This work addresses the challenge of combining real-time sensor data with knowledge-based reasoning for robotic operating systems, presenting a system-level approach that is incremental in nature.
The paper tackles the integration of high-dimensional data streams like video and LiDAR with symbolic reasoning in robotics, proposing the SemRob platform to unify these representations for semantic stream reasoning.
Stream processing and reasoning is getting considerable attention in various application domains such as IoT, Industry IoT and Smart Cities. In parallel, reasoning and knowledge-based features have attracted research into many areas of robotics, such as robotic mapping, perception and interaction. To this end, the Semantic Stream Reasoning (SSR) framework can unify the representations of symbolic/semantic streams with deep neural networks, to integrate high-dimensional data streams, such as video streams and LiDAR point clouds, with traditional graph or relational stream data. As such, this positioning and system paper will outline our approach to build a platform to facilitate semantic stream reasoning capabilities on a robotic operating system called SemRob.