Date of Award
2010
Publication Type
Master Thesis
Degree Name
M.A.Sc.
Department
Computer Science
Keywords
Applied sciences
Supervisor
Dan Wu
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
Mobile robot localization is one of the most important problems in robotics. Localization is the process of a robot finding out its location given a map of its environment. A number of successful localization solutions have been proposed, among them the well-known and popular Monte Carlo localization method, which is based on particle filters. This thesis proposes a localization approach based on particle filters, using a different way of initializing and resampling of the particles, that reduces the cost of localization. Ultrasonic and light sensors are used in order to perform the experiments. Monte Carlo Localization may fail to localize the robot properly because of the premature convergence of the particles. Using more number of particles increases the computational cost of localization process. Experimental results show that, applying the proposed method robot can successfully localize itself using less number of particles; therefore the cost of localization is decreased.
Recommended Citation
Seifzadeh, Sepideh, "Mobile robot localization failure recovery" (2010). Electronic Theses and Dissertations. 7867.
https://scholar.uwindsor.ca/etd/7867