Paper Info
Title
RoboEarth Semantic Mapping: A Cloud Enabled Knowledge-Based Approach
Abstract
The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work, we describe the RoboEarth semantic mapping system. The semantic map is composed of: 1) an ontology to code the concepts and relations in maps and objects and 2) a SLAM map providing the scene geometry and the object locations with respect to the robot. We propose to ground the terminological knowledge in the robot perceptions by means of the SLAM map of objects. RoboEarth boosts mapping by providing: 1) a subdatabase of object models relevant for the task at hand, obtained by semantic reasoning, which improves recognition by reducing computation and the false positive rate; 2) the sharing of semantic maps between robots; and 3) software as a service to externalize in the cloud the more intensive mapping computations, while meeting the mandatory hard real time constraints of the robot. To demonstrate the RoboEarth cloud mapping system, we investigate two action recipes that embody semantic map building in a simple mobile robot. The first recipe enables semantic map building for a novel environment while exploiting available prior information about the environment. The second recipe searches for a novel object, with the efficiency boosted thanks to the reasoning on a semantically annotated map. Our experimental results demonstrate that, by using RoboEarth cloud services, a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. In addition, we show the synergetic relation of the SLAM map of objects that grounds the terminological knowledge coded in the ontology.
Year
DOI
Venue
2015
10.1109/TASE.2014.2377791
IEEE T. Automation Science and Engineering
Keywords
Field
DocType
Semantics,Knowledge based systems,Search problems,Simultaneous localization and mapping,Navigation,Visualization
Semantic mapping,Computer science,Control engineering,Artificial intelligence,Simultaneous localization and mapping,Information retrieval,Visualization,Knowledge-based systems,Robot,Machine learning,Semantics,Mobile robot,Cloud computing
Journal
Volume
Issue
ISSN
12
2
1545-5955
Citations 
PageRank 
References 
5
0.46
32
Authors
11
Name
Order
Citations
PageRank
L. Riazuelo11359.22
Moritz Tenorth261532.70
Daniel Di Marco390.98
Marta Salas4190.98
Dorian Gálvez-López52899.78
Lorenz Mösenlechner627414.54
Lars Kunze715711.60
Michael Beetz83784284.03
Juan Domingo93319258.54
Luis Montano10545.24
J. M. M. Montiel11152586.77