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.

RoSmEEry: Robotic Simulated Environment for Evaluation and Benchmarking of Semantic Mapping Algorithms / Kaszuba, Sara; Sabbella, Sandeep Reddy; Suriani, Vincenzo; Riccio, Francesco; Nardi, Daniele. - (2021). ((Intervento presentato al convegno Autonomous Robots and Multirobot Systems (ARMS) tenutosi a London, UK.

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

Sara Kaszuba
;
Sandeep Reddy Sabbella
;
Vincenzo Suriani
;
Francesco Riccio
;
Daniele Nardi
2021

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1578596
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