01926nam a2200277 a 4500 vtls000020819 KUKTEM 20251117144339.0 070817s2007 gw f 001 0 eng d 9783540463993 3540463992 201203222026 ida 201107131922 VLOAD 200908141329 VLOAD 200908141301 VLOAD 200708170950 kam UMP TJ211.35 .M66 2007 Montemerlo, Michael Fastslam : a scalable method for the the simultaneous localization and mapping problem in robotics / Michael Montemerlo, Sebastian Thrun Berlin : Springer, 2007 119 p. : ill. (some col.), maps ; 24 cm. This 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. Robots Control systems Mobile robots Data transmission systems Cartography Thrun, Sebastian 0 0 lcc 0 0 20000 20000 2019-09-04 281.88 0 TJ211.35 .M66 2007 0000024571 2019-09-04 00:00:00 1 281.88 2019-09-04 OPEN VIRTUA40 34460 34466 VTLSSORT0080*0200*0201*0400*0900*1000*2450*2600*3000*5200*6500*6501*6502*6503*7000*9991