A multilevel relaxation algorithm for simultaneous localization and mapping

This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation meth...

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Veröffentlicht in:IEEE transactions on robotics Jg. 21; H. 2; S. 196 - 207
Hauptverfasser: Frese, U., Larsson, P., Duckett, T.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.04.2005
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Applied sciences
Comparative analysis
Computer Science
Computer science; control theory; systems
Control theory. Systems
Datavetenskap
Exact sciences and technology
Galerkin multigrid
Gauss-Seidel relaxation
Maximum likelihood estimation
metric-topological maps
metrictopological maps
Miscellaneous
mobile robot navigation
Mobile robots
Multigrid methods
Navigation
Noise measurement
Optimization methods
Partial differential equations
Relaxation methods
Robotics
Robots
Simultaneous localization and mapping
simultaneous localization and mapping (SLAM)
SLAM
Termsómobile robot navigation
Uncertainty
Relaxation
Maps
Navigation
Multigrid
Mapping
Metric
Topology
Localization
Mobile
Robot
ISSN:1552-3098, 1941-0468, 1941-0468
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Beschreibung
Zusammenfassung:This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping, because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared with other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented.
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ISSN:1552-3098
1941-0468
1941-0468
DOI:10.1109/TRO.2004.839220