TY  - BOOK
AU  - Montemerlo,Michael
AU  - Thrun,Sebastian
AU  - Siciliano,Bruno
TI  - FastSLAM: A Scalable Method For The Simultaneous Localization And Mapping Problem In Robotics 
SN  - 3540463992
AV  - TJ211.35 .M667 2007
PY  - 2007///
CY  - Berlin
PB  - Springer
KW  - Robots
KW  - Control Systems
KW  - Mobile Robots
KW  - Data Transmission Systems
KW  - Cartography
N1  - Includes bibliographical references (p. [111]-116) and index
N2  - 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
UR  - http://www.loc.gov/catdir/enhancements/fy0818/2006936725-t.html
UR  - http://www.loc.gov/catdir/enhancements/fy0818/2006936725-d.html
ER  -