Hybrid localization approach of a bi-steerable mobile robot based on grids matching and extended Kalman filter

Author

Bouraine, S. ; Djekoune, A.O. ; Azouaoui, O.

Author_Institution

Centre de Dev. des Technol. Av., Algers

fYear

2008

fDate

12-15 Oct. 2008

Firstpage

1136

Lastpage

1141

Abstract

This paper presents a mobile robot self localization method used to determine the position of the mobile robot Robucar. The localization approach is based on using both grids matching method and extended Kalman filter (EKF) method. The grids matching method provides accurate results but requires a large computational time that is why the EKF is introduced. EKF fuses odometric data and laser data to estimate the robot position. The developed algorithms are implemented and tested on the mobile robot Robucar.

Keywords

Kalman filters; mobile robots; nonlinear filters; position control; Robucar; bisteerable mobile robot; extended Kalman filter; grids matching; hybrid localization; laser data; odometric data; robot position estimation; self localization; Fuses; Global Positioning System; Grid computing; Intelligent robots; Intelligent transportation systems; Mobile robots; Position measurement; Robot sensing systems; Sensor fusion; Ultrasonic variables measurement; Extended Kalman Filter and Certainty grid; Localization; Mobiles Robots; grids matching;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Transportation Systems, 2008. ITSC 2008. 11th International IEEE Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-2111-4

Electronic_ISBN

978-1-4244-2112-1

Type

conf

DOI

10.1109/ITSC.2008.4732674

Filename

4732674