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Enriching the robot representation of the operational environment is achallenging task that aims at bridging the gap between low-level sensorreadings and high-level semantic understanding. Having a rich representationoften requires computationally demanding architectures and pure point cloudbased detection systems that struggle when dealing with everyday objects thathave to be handled by the robot. To overcome these issues, we propose agraph-based representation that addresses this gap by providing a semanticrepresentation of robot environments from multiple sources. In fact, to acquireinformation from the environment, the framework combines classical computervision tools with modern computer vision cloud services, ensuring computationalfeasibility on onboard hardware. By incorporating an ontology hierarchy withover 800 object classes, the framework achieves cross-domain adaptability,eliminating the need for environment-specific tools. The proposed approachallows us to handle also small objects and integrate them into the semanticrepresentation of the environment. The approach is implemented in the RobotOperating System (ROS) using the RViz visualizer for environmentrepresentation. This work is a first step towards the development of ageneral-purpose framework, to facilitate intuitive interaction and navigationacross different domains.

Enhancing Graph Representation of the Environment through Local and Cloud Computation