00000cmm a22000004a 4500 UP-99796217611212060 Buklod 20140424080607.0 m go j cr cn |||aaaaa 110725s2011 xx u | eng d 9783642213908 (eBook) 3642213901 (eBook) (iLib)UPD-00214608216 DLC DML eng DMLUC TJ 211 R38 2011eb Mullane, John. Random finite sets for robot mapping and SLAM [electronic resource] new concepts in autonomous robotic map representations John Mullane ... [et. al.]. Berlin Springer-Verlag c2011. 1 online resource (xxiv, 146 p.) ill. (chiefly col.) Springer Tracts in Advanced Robotics 72 Part I Random Finite Sets -- Why Random Finite Sets? -- Estimation with Random Finite Sets -- Part II Random Finite Set Based Robotic Mapping -- An RFS Theoretic for Bayesian Feature-Based Robotic Mapping -- An RFS Brute Force? Formulation for Bayesian SLAM -- Rao-Blackwellised RFS Bayesian SLAM -- Extensions with RFSs in SLAM. IP-based subscription, access limited to within on-campus computer network. Access via Electronic Resources of the UPD University Library Website. 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. Electronic reproduction. New York SpringerLink 2011. Available via World Wide Web through SpringerLink. Robotics. Mobile robots. Mappings (Mathematics) Electronic books. Mullane, John (John Stephen). Vo, Ba-Ngu. Adams, Martin David. Vo, Ba-Tuong. SpringerLink (Online service). Available for University of the Philippines Diliman via SpringerLink. Click here to access http://link.springer.com/book/10.1007/978-3-642-21390-8 FO Monograph UPD DMLR Electronic Resource