TY  - JOUR
ID  - david2021kbs
T1  - ViMantic, a distributed robotic architecture for semantic mapping in indoor environments
A1  - Fernandez-Chaves, David
A1  - Ruiz-Sarmiento, J. R.
A1  - Petkov, Nicolai
A1  - Gonzalez-Jimenez, Javier
JA  - International Journal of Knowledge-Based Systems
Y1  - 2021
SP  - 107440
SN  - 0950-7051
UR  - https://www.sciencedirect.com/science/article/pii/S0950705121007024
M2  - doi: https://doi.org/10.1016/j.knosys.2021.107440
KW  - Detectron2
KW  - mobile robots
KW  - Object detection
KW  - Robot@Home
KW  - Robotic architecture
KW  - ROS
KW  - Semantic maps
KW  - Unity 3D
N2  - 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.
M1  - img_url=https%3A%2F%2Fars.els-cdn.com%2Fcontent%2Fimage%2F1-s2.0-S0950705121007024-gr1.jpg
M1  - rank_indexname=JCR
M1  - rank_pos_in_category=16
M1  - rank_num_in_category=139
M1  - rank_cat_name=COMPUTER%20SCIENCE%2C%20ARTIFICIAL%20INTELLIGENCE
M1  - impact_factor=8.038
ER  -