Sensor resetting localization for poorly modelled mobile robots

Author

Lenser, Scott ; Veloso, Manuela

Author_Institution

Sch. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA

Volume

2

fYear

2000

fDate

2000

Firstpage

1225

Abstract

We present a new localization algorithm, called sensor resetting localization, which is an extension of Monte Carlo localization. The algorithm adds sensor based re-sampling to Monte Carlo localization when the robot is lost. Sensor resetting localization (SRL) is robust to modelling errors including unmodelled movements and systematic errors. It can be used in real time on systems with limited computational power. The algorithm has been successfully used on autonomous legged robots in the Sony legged league of the robotic soccer competition RoboCup´99. We present results from the real robots demonstrating the success of the algorithm and results from simulation comparing SRL to Monte Carlo localization

Keywords

Monte Carlo methods; legged locomotion; position control; real-time systems; robot vision; Monte Carlo method; legged locomotion; mobile robots; modelling errors; real time systems; robot vision; robotic soccer; sensor resetting localization; Cameras; Hardware; Legged locomotion; Machine vision; Mobile robots; Monte Carlo methods; Neck; Robot sensing systems; Robot vision systems; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 2000. Proceedings. ICRA '00. IEEE International Conference on

Conference_Location

San Francisco, CA

ISSN

1050-4729

Print_ISBN

0-7803-5886-4

Type

conf

DOI

10.1109/ROBOT.2000.844766

Filename

844766