Title :
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
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;
Conference_Titel :
Robotics and Automation, 2000. Proceedings. ICRA '00. IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-5886-4
DOI :
10.1109/ROBOT.2000.844766
