Date of Award
2010
Publication Type
Master Thesis
Degree Name
M.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
The robot localization problem is a key problem in making truly autonomous robots. If a robot does not know where it is, it can be difficult to determine what to do next. Monte Carlo Localization as a well known localization algorithm represents a robot's belief by a set of weighted samples. This set of samples approximates the posterior probability of where the robot is located. Our method presents an extension to the MCL algorithm when localizing in highly symmetrical environments; a situation where MCL is often unable to correctly track equally probable poses for the robot. The sample sets in MCL often become impoverished when samples are generated in several locations. Our approach incorporates the idea of clustering the samples and organizes them considering to their orientation. Experimental results show our method is able to successfully determine the position of the robot in symmetric environment, while ordinary MCL often fails.
Recommended Citation
Akhavan Malayeri, Ali, "Robot localization in symmetric environment" (2010). Electronic Theses and Dissertations. 8221.
https://scholar.uwindsor.ca/etd/8221