Adaptive Filtering for Mobile Robot Localization with Unknown Odometry Statistics

Author

Caballero, R. ; Rodriguez-Losada, D. ; Matía, F.

Author_Institution

Univ. Tecnol. de Panama, Panama City, Panama

fYear

2009

fDate

26-28 Feb. 2009

Firstpage

231

Lastpage

234

Abstract

One of the most important tasks in mobile robotics is the vehicle self localization from a reference frame system. In this sense, most of the mobile robots fuse odometry sensors with laser range finders or sonar sensors. Nevertheless, the odometry and kinematic model error statistics are usually unknown and time variant. An adaptive extended Kalman filter is proposed for mobile robot localization and the first and second moment of odometry sensors noise estimation.

Keywords

adaptive Kalman filters; electronic noses; laser ranging; mobile robots; sonar signal processing; adaptive extended Kalman filter; adaptive filtering; kinematic model error statistics; laser range finders; mobile robot localization; mobile robotics; noise estimation; odometry sensors; reference frame system; sonar sensors; unknown odometry statistics; vehicle self localization; Adaptive filters; Fuses; Laser modes; Laser noise; Mobile robots; Robot sensing systems; Sensor fusion; Sonar; Statistics; Vehicles; Kalman Filtering; Mobile Robots;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical, Communications, and Computers, 2009. CONIELECOMP 2009. International Conference on

Conference_Location

Cholula, Puebla

Print_ISBN

978-0-7695-3587-6

Electronic_ISBN

978-0-7695-3587-6

Type

conf

DOI

10.1109/CONIELECOMP.2009.53

Filename

5163923