Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Fastslam: A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics

Fastslam: A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics

Montemerlo, Michael
Thrun, Sebastian

Hardcover

Series: Springer Tracts in Advanced Robotics, Book 27

Technology & EngineeringGeneral ComputersGeneral Mathematics

ISBN10: 3540463992
ISBN13: 9783540463993
Publisher: Springer Nature
Published: Jan 18 2007
Pages: 120
Weight: 0.85
Height: 0.40 Width: 6.20 Depth: 9.40
Language: English

This monograph, from the winners of the DARPA Grand Challenge, describes a new family of algorithms for the simultaneous localization and mapping problem in robotics (SLAM). It is the first book on the market about FastSLAM which is the most influential of recent contributions to the SLAM problem for mobile robots. 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 in the past few years, 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.

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