TY - BOOK ID - 8657031 TI - Random finite sets for robot mapping and SLAM : new concepts in autonomous robotic map representations PY - 2011 SN - 3642213898 3642213901 PB - New York : Springer, DB - UniCat KW - Robotics KW - Mobile robots KW - Mappings (Mathematics) KW - Mechanical Engineering KW - Engineering & Applied Sciences KW - Mechanical Engineering - General KW - Finite groups. KW - Random sets. KW - SLAM (Computer program language) KW - Robots KW - Mathematics. KW - Control systems. KW - Simulation Language for Alternative Modeling (Computer program language) KW - Groups, Finite KW - Robot control KW - Engineering. KW - Artificial intelligence. KW - Robotics. KW - Automation. KW - Robotics and Automation. KW - Artificial Intelligence (incl. Robotics). KW - Automatic factories KW - Automatic production KW - Computer control KW - Engineering cybernetics KW - Factories KW - Industrial engineering KW - Mechanization KW - Assembly-line methods KW - Automatic control KW - Automatic machinery KW - CAD/CAM systems KW - Automation KW - Machine theory KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Construction KW - Industrial arts KW - Technology KW - FORTRAN (Computer program language) KW - Modeling languages (Computer science) KW - Geometric probabilities KW - Set theory KW - Group theory KW - Modules (Algebra) KW - Artificial Intelligence. UR - https://www.unicat.be/uniCat?func=search&query=sysid:8657031 AB - 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. ER -