FastSLAM a scalable method for the simultaneous localization and mapping problem in robotics
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 enor...
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| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Berlin, Heidelberg u.a.
Springer
2007
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| Schriftenreihe: | Springer tracts in advanced robotics
27 |
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| Online Zugang: | Inhaltsverzeichnis Cover Inhaltstext Table of contents only Publisher description |
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| Zusammenfassung: | 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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| Beschreibung: | Literaturverz. S. [111] - 116 |
| Beschreibung: | XIII, 119 S. Ill., graph. Darst. 235 mm x 155 mm |
| ISBN: | 3540463992 3-540-46399-2 9783540463993 978-3-540-46399-3 |