RT Journal Article
T1 ViMantic, a distributed robotic architecture for semantic mapping in indoor environments
A1 Fernandez-Chaves, D.
A1 Ruiz-Sarmiento, J. R.
A1 Petkov, N.
A1 Gonzalez-Jimenez, J.
K1 Semantic maps
K1 Robotic architecture
K1 Mobile robots
K1 Unity 3D
K1 ROS
K1 Object detection
K1 Detectron2
K1 Robot@Home
AB Semantic maps augment traditional representations of robot workspaces, typically based on their geometry and/or topology, with meta-information about the properties, relations and functionalities of their composing elements. A piece of such information could be: fridges are appliances typically found in kitchens and employed to keep food in good condition. Thereby, semantic maps allow for the execution of high-level robotic tasks in an efficient way, e.g. "Hey robot, Store the leftover salad". This paper presents ViMantic, a novel semantic mapping architecture for the building and maintenance of such maps, which brings together a number of features as demanded by modern mobile robotic systems, including: (i) a formal model, based on ontologies, which defines the semantics of the problem at hand and establishes mechanisms for its manipulation; (ii) techniques for processing sensory information and automatically populating maps with, for example, objects detected by cutting-edge CNNs; (iii) distributed execution capabilities through a client-server design, making the knowledge in the maps accessible and extendable to other robots/agents; (iv) a user interface that allows for the visualization and interaction with relevant parts of the maps through a virtual environment; (v) public availability, hence being ready to use in robotic platforms. The suitability of ViMantic has been assessed using Robot@Home, a vast repository of data collected by a robot in different houses. The experiments carried out consider different scenarios with one or multiple robots, from where we have extracted satisfactory results regarding automatic population, execution times, and required size in memory of the resultant semantic maps. (C) 2021 The Author(s). Published by Elsevier B.V.
PB Elsevier
SN 0950-7051
YR 2021
FD 2021-09-09
LK https://hdl.handle.net/10668/27947
UL https://hdl.handle.net/10668/27947
LA en
DS RISalud
RD Apr 7, 2025