Fastslam : a scalable method for the the simultaneous localization and mapping problem in robotics / Michael Montemerlo, Sebastian Thrun

By:

Montemerlo, Michael

Contributor(s):

Thrun, Sebastian

Material type: Text

TextPublication details: Berlin : Springer, 2007Description: 119 p. : ill. (some col.), maps ; 24 cmISBN:

9783540463993
3540463992

Subject(s):

Summary: 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.

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Title notes ( 1 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Open Shelf	UMPLIB PEKAN	TJ211.35 .M66 2007 (Browse shelf(Opens below))	1	Available		0000024571

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.