Simultaneous Localization And Mapping: A feature-based probabilistic approach
International Journal of Applied Mathematics and Computer Science (2009)
- Volume: 19, Issue: 4, page 575-588
- ISSN: 1641-876X
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topPiotr Skrzypczyński. "Simultaneous Localization And Mapping: A feature-based probabilistic approach." International Journal of Applied Mathematics and Computer Science 19.4 (2009): 575-588. <http://eudml.org/doc/207956>.
@article{PiotrSkrzypczyński2009,
abstract = {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.},
author = {Piotr Skrzypczyński},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {mobile robot; navigation; simultaneous localization and mapping; feature matching},
language = {eng},
number = {4},
pages = {575-588},
title = {Simultaneous Localization And Mapping: A feature-based probabilistic approach},
url = {http://eudml.org/doc/207956},
volume = {19},
year = {2009},
}
TY - JOUR
AU - Piotr Skrzypczyński
TI - Simultaneous Localization And Mapping: A feature-based probabilistic approach
JO - International Journal of Applied Mathematics and Computer Science
PY - 2009
VL - 19
IS - 4
SP - 575
EP - 588
AB - 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.
LA - eng
KW - mobile robot; navigation; simultaneous localization and mapping; feature matching
UR - http://eudml.org/doc/207956
ER -
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Citations in EuDML Documents
top- André M. Santana, Adelardo A.D. Medeiros, Straight-lines modelling using planar information for monocular SLAM
- Artur Wilkowski, Tomasz Kornuta, Maciej Stefańczyk, Włodzimierz Kasprzak, Efficient generation of 3D surfel maps using RGB-D sensors
- Marek Kraft, Michał Nowicki, Rudi Penne, Adam Schmidt, Piotr Skrzypczyński, Efficient RGB-D data processing for feature-based self-localization of mobile robots
- Dominik Belter, Przemysław Łabecki, Péter Fankhauser, Roland Siegwart, RGB-D terrain perception and dense mapping for legged robots
- Mateusz Kowalski, Piotr Kaczmarek, Rafał Kabaciński, Mieszko Matuszczak, Kamil Tranbowicz, Robert Sobkowiak, A simultaneous localization and tracking method for a worm tracking system
- Pablo A. Martínez, Mario Castelán, Gustavo Arechavaleta, Vision based persistent localization of a humanoid robot for locomotion tasks
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