01632nam a2200229 a 4500001001400000003000700014005001700021008004100038020001800079020001500097040000800112100002400120245014300144260003000287300004700317520092500364650002801289650001801317650003001335650001601365700002101381vtls000020819KUKTEM20251117144339.0070817s2007    gw     f      001 0 eng d  a9783540463993  a3540463992  aUMP1 aMontemerlo, Michael10aFastslam :ba scalable method for the the simultaneous localization and mapping problem in robotics /cMichael Montemerlo, Sebastian Thrun  aBerlin :bSpringer,c2007  a119 p. :bill. (some col.), maps ;c24 cm.  aThis monograph describes a new family of algorithms for the simultaneous localization and mapping problem in robotics (SLAM). SLAM addresses the problem of acquiring an environment map with a roving robot, while simultaneously localizing the robot relative to this map. This problem has received enormous attention in the robotics community, reaching a peak of popularity on the occasion of the DARPA Grand Challenge in October 2005, which was won by the team headed by the authors. The FastSLAM family of algorithms applies particle filters to the SLAM Problem, which provides new insights into the data association problem that is paramount in SLAM. The FastSLAM-type algorithms have enabled robots to acquire maps of unprecedented size and accuracy in a number of robot application domains and have been successfully applied in different dynamic environments, including the solution to the problem of people tracking. 0aRobotsxControl systems 0aMobile robots 0aData transmission systems 0aCartography1 aThrun, Sebastian