  Human-robot interaction requires a common understanding of the operational
environment, which can be provided by a representation that blends geometric
and symbolic knowledge: a semantic map. Through a semantic map the robot can
interpret user commands by grounding them to its sensory observations. Semantic
mapping is the process that builds such a representation. Despite being
fundamental to enable cognition and high-level reasoning in robotics, semantic
mapping is a challenging task due to generalization to different scenarios and
sensory data types. In fact, it is difficult to obtain a rich and accurate
semantic map of the environment and of the objects therein. Moreover, to date,
there are no frameworks that allow for a comparison of the performance in
building semantic maps for a given environment. To tackle these issues we
design RoSmEEry, a novel framework based on the Gazebo simulator, where we
introduce an accessible and ready-to-use methodology for a systematic
evaluation of semantic mapping algorithms. We release our framework, as an
open-source package, with multiple simulation environments with the aim to
provide a general set-up to quantitatively measure the performances in
acquiring semantic knowledge about the environment.
