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A multilevel relaxation algorithm for simultaneous localization and mapping

delete2005-04-01
delete221
PRE
AI
U
Udo Frese
P
P. Larsson
T
Tom Duckett
DOI：10.1109/TRO.2004.839220delete
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Abstract

Abstract

En 中文
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.
Keywords:
Galerkin multigrid
Gauss-Seidel relaxation
metric-topological maps
mobile robot navigation
simultaneous localization and mapping (SLAM)
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Journal

IEEE Transactions on Robotics cover
IEEE Transactions on Robotics
IF:
10.5
Papers:
3.3K
Citations:
2.8W

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