Simultaneous Localization And Mapping: A feature-based probabilistic approach

Piotr Skrzypczyński

International Journal of Applied Mathematics and Computer Science, Tome 19 (2009), p. 575-588 / Harvested from The Polish Digital Mathematics Library

Résumé

This article provides an introduction to Simultaneous Localization And Mapping (SLAM), with the focus on probabilistic SLAM utilizing a feature-based description of the environment. A probabilistic formulation of the SLAM problem is introduced, and a solution based on the Extended Kalman Filter (EKF-SLAM) is shown. Important issues of convergence, consistency, observability, data association and scaling in EKF-SLAM are discussed from both theoretical and practical points of view. Major extensions to the basic EKF-SLAM method and some recent advances in SLAM are also presented.

Détail

Publié le : 2009-01-01

Zbl 1300.93157

EUDML-ID : urn:eudml:doc:207956

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