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http://hdl.handle.net/20.500.12390/2564


Título: A categorization of simultaneous localization and mapping knowledge for mobile robots
Autor(es): Cornejo Lupa M.A. 
Ticona-Herrera R.P. 
Cardinale Y. 
Barrios-Aranibar D. 
Resumen: Autonomous robots are playing important roles in academic, technological, and scientific activities. Thus, their behavior is getting more complex. The main tasks of autonomous robots include mapping an environment and localize themselves. These tasks comprise the Simultaneous Localization and Mapping (SLAM) problem. Representation of the SLAM knowledge (e.g., robot characteristics, environment information, mapping and location information), with a standard and well-defined model, provides the base to develop efficient and interoperable solutions. However, as far as we know, there is not a common classification of such knowledge. Many existing works based on Semantic Web, have formulated ontologies to model information related to only some SLAM aspects, without a standard arrangement. In this paper, we propose a categorization of the knowledge managed in SLAM, based on existing ontologies and SLAM principles. We also classify recent and popular ontologies according to our proposed categories and highlight the lessons to learn from existing solutions. © 2020 ACM.
Tema: Mobile robots;Ontologies;Semantic robots;Semantic web;SLAM
Editorial: Association for Computing Machinery
Fecha de publicación: 2020
Publicado en: Proceedings of the ACM Symposium on Applied Computing 
Financiamiento: 01-2019--FONDECYT-BM 
Tipo de publicación: info:eu-repo/semantics/article
Identificador Handle: http://hdl.handle.net/20.500.12390/2564
DOI: 10.1145/3341105.3373974
Nivel de acceso: info:eu-repo/semantics/closedAccess
Colección:6.1 Proyectos de investigación científica

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