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Random Finite Sets for Robot Mapping and SLAM : New Concepts in Autonomous Robotic Map Representations / by John Mullane, Ba-Ngu Vo, Martin Adams, Ba-Tuong Vo.



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Creatore: Mullane, John Visualizza persona
Titolo: Random Finite Sets for Robot Mapping and SLAM : New Concepts in Autonomous Robotic Map Representations / by John Mullane, Ba-Ngu Vo, Martin Adams, Ba-Tuong Vo.
Link to work: Random Finite Sets for Robot Mapping and SLAM Visualizza cluster
Pubblicazione: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011
Estensione: 1 online resource.
Tipo formato: computer
Tipo contenuto: text
Tipo supporto: online resource
Disciplina: 629.892
Titolo uniforme di collana: Springer tracts in advanced robotics ; 72. 1610-7438
Index term-Uncontrolled: Robotics and Automation
Artificial Intelligence (incl. Robotics)
Engineering
Soggetto non controllato: Robotics and Automation
Artificial Intelligence (incl. Robotics)
Engineering
Termine d'indicizzazione-Occupazione: Robotics and Automation
Artificial Intelligence (incl. Robotics)
Engineering
Classificazione LOC: TJ211.415 .M85 2011eb
Creatori/Collaboratori: Vo, Ba-Ngu
Adams, Martin
Vo, Ba-Tuong
Nota di contenuto: Introduction -- Random Finite Sets -- Random Finite Set Based Robotic Mapping -- Random Finite Set Based Simultaneous Localisation and Map Building.
Restrizioni accesso: Access is restricted to users affiliated with licensed institutions.
Sommario/riassunto: Simultaneous Localisation and Map (SLAM) building algorithms, which rely on random vectors to represent sensor measurements and feature maps are known to be extremely fragile in the presence of feature detection and data association uncertainty. Therefore new concepts for autonomous map representations are given in this book, based on random finite sets (RFSs). It will be shown that the RFS representation eliminates the necessity of fragile data association and map management routines. It fundamentally differs from vector based approaches since it estimates not only the spatial states of features but also the number of map features which have passed through the field(s) of view of a robot's sensor(s), an attribute which is necessary for SLAM. The book also demonstrates that in SLAM, a valid measure of map estimation error is critical. It will be shown that under an RFS-SLAM representation, a consistent metric, which gauges both feature number as well as spatial errors, can be defined. The concepts of RFS map representations are accompanied with autonomous SLAM experiments in urban and marine environments. Comparisons of RFS-SLAM with state of the art vector based methods are given, along with pseudo-code implementations of all the RFS techniques presented. John Mullane received the B.E.E. degree from University College Cork, Ireland, and Ph. D degree from Nanyang Technological University (NTU), Singapore. Ba-Ngu Vo is Winthrop Professor and Chair of Signal Processing, University of Western Australia (UWA). He received joint Bachelor degrees (Science and Elec. Eng.), UWA, and Ph. D., Curtin University. Martin Adams is Professor in autonomous robotics research, University of Chile. He holds bachelors, masters and doctoral degrees from Oxford University. Ba-Tuong Vo is Assistant Professor, UWA. He received his B. Sc, B.E and Ph. D. degrees from UWA.
Collana: Springer Tracts in Advanced Robotics, 1610-7438 ; 72
ISBN: 9783642213908
3642213901
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 006178952
Localizzazioni e accesso elettronico https://ebookcentral.proquest.com/lib/nyulibrary-ebooks/detail.action?docID=3067057
Collocazione: Electronic access
Lo trovi qui: New York University