Enriching the robot representation of the operational environment is a
challenging task that aims at bridging the gap between low-level sensor
readings and high-level semantic understanding. Having a rich representation
often requires computationally demanding architectures and pure point cloud
based detection systems that struggle when dealing with everyday objects that
have to be handled by the robot. To overcome these issues, we propose a
graph-based representation that addresses this gap by providing a semantic
representation of robot environments from multiple sources. In fact, to acquire
information from the environment, the framework combines classical computer
vision tools with modern computer vision cloud services, ensuring computational
feasibility on onboard hardware. By incorporating an ontology hierarchy with
over 800 object classes, the framework achieves cross-domain adaptability,
eliminating the need for environment-specific tools. The proposed approach
allows us to handle also small objects and integrate them into the semantic
representation of the environment. The approach is implemented in the Robot
Operating System (ROS) using the RViz visualizer for environment
representation. This work is a first step towards the development of a
general-purpose framework, to facilitate intuitive interaction and navigation
across different domains.Comment: 5 pages, 4 figure