Title :
Probabilistic localization methods of a mobile robot using ultrasonic perception system
Author :
Zhang, Lei ; Zapata, René
Author_Institution :
Lab. d´´Inf. de Robot. et de Microelectron., Univ. Montpellier II, Montpellier, France
Abstract :
Effective localization is a fundamental prerequisite for achieving autonomous mobile robot. In this paper, we propose three probabilistic approaches to solve the global localization problem and the kidnapped robot problem. The first approach named the hybrid Grid-MCL algorithm merges Monte Carlo Localization (MCL) and grid localization. It can solve the global localization problem with very low on-line computational costs. The second approach, sampling in Similar Energy Regions (SER), is used to conquer the kidnapped robot problem. The third approach is a combination of previous two approaches with adaptive samples, which solves the global localization problem and the kidnapped robot problem together. The validity of our approaches is verified through extensive simulations employing ultrasonic perception system.
Keywords :
Monte Carlo methods; mobile robots; Monte Carlo localization; autonomous mobile robot; global localization problem; grid localization; kidnapped robot problem; online computational costs; probabilistic localization methods; similar energy regions; ultrasonic perception system; Computational efficiency; Mobile robots; Monte Carlo methods; Particle filters; Robot sensing systems; Robotics and automation; Sampling methods; Testing; Tracking; Working environment noise;
Conference_Titel :
Information and Automation, 2009. ICIA '09. International Conference on
Conference_Location :
Zhuhai, Macau
Print_ISBN :
978-1-4244-3607-1
Electronic_ISBN :
978-1-4244-3608-8
DOI :
10.1109/ICINFA.2009.5205075