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Human-robot interaction requires a common understanding of the operationalenvironment, which can be provided by a representation that blends geometricand symbolic knowledge: a semantic map. Through a semantic map the robot caninterpret user commands by grounding them to its sensory observations. Semanticmapping is the process that builds such a representation. Despite beingfundamental to enable cognition and high-level reasoning in robotics, semanticmapping is a challenging task due to generalization to different scenarios andsensory data types. In fact, it is difficult to obtain a rich and accuratesemantic map of the environment and of the objects therein. Moreover, to date,there are no frameworks that allow for a comparison of the performance inbuilding semantic maps for a given environment. To tackle these issues wedesign RoSmEEry, a novel framework based on the Gazebo simulator, where weintroduce an accessible and ready-to-use methodology for a systematicevaluation of semantic mapping algorithms. We release our framework, as anopen-source package, with multiple simulation environments with the aim toprovide a general set-up to quantitatively measure the performances inacquiring semantic knowledge about the environment.

RoSmEEry: Robotic Simulated Environment for Evaluation and Benchmarking of Semantic Mapping Algorithms