Simultaneous localization and mapping using Extended Kalman Filter

S. Yavuz; Z. Kurt; M.S. Bicer

doi:10.1109/SIU.2009.5136492

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2009 IEEE 17th Signal Processing and Communications Applications Conference > 700 - 703

Abstract

In this study, an offline statistical estimation algorithm based on extended Kalman filter method is developed to solve the SLAM (simultaneous localization and map building) problem. For the application, a robot equipped with only simple and cheap sensors is used. Two of the most frequent problems in SLAM algorithms which are known as loop closing and data association are effectively solved by extended Kalman filter method.

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book ISBN :	978-1-4244-4435-9
book e-ISBN :	978-1-4244-4436-6
DOI	10.1109/SIU.2009.5136492

Keywords

statistical analysis estimation theory Kalman filters mobile robots nonlinear filters sensor fusion mobile robot extended Kalman filter method offline statistical estimation algorithm simultaneous localization and map building problem sensors data association Robots Data mining Simultaneous localization and mapping Probability density function Jacobian matrices Sonar

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IEEE

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Simultaneous localization and mapping using Extended Kalman Filter

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Yavuz, S.

Kurt, Z.

Bicer, M.S.

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Yavuz, S.

Kurt, Z.

Bicer, M.S.

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Simultaneous localization and mapping using Extended Kalman Filter