DocumentCode
2362828
Title
Visual based Localization for mobile robots with Support Vector Machines
Author
Shen, Jiali ; Hu, Huosheng
Author_Institution
Dept. of Comput. Sci., Essex Univ., Colchester
fYear
2006
fDate
6-10 Nov. 2006
Firstpage
4176
Lastpage
4181
Abstract
Support vector machine (SVM) is a relative new classification algorithm with some advantages over other machine learning methods. This paper presents a SVM based localisation algorithm for indoor robot navigation by recognizing the environmental features that are known as priori. A topological map is adopted by the mobile robot for the coarse global localisation. Then at each topological node located, geometrical grids are adopted for the proposed algorithm to provide fine position information of the mobile robot. Experimental results are presented to show the feasibility and good performance of the proposed method
Keywords
feature extraction; mobile robots; navigation; support vector machines; coarse global localisation; environmental features recognition; fine position information; indoor robot navigation; machine learning methods; mobile robots; support vector machines; visual based localization; Computational efficiency; Histograms; Image edge detection; Machine learning algorithms; Mobile robots; Navigation; Robot kinematics; Simultaneous localization and mapping; Support vector machine classification; Support vector machines; Landmarks; Mobile robot; SLAM; Support Vector Machine; Visual based location;
fLanguage
English
Publisher
ieee
Conference_Titel
IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on
Conference_Location
Paris
ISSN
1553-572X
Print_ISBN
1-4244-0390-1
Type
conf
DOI
10.1109/IECON.2006.347454
Filename
4152950
Link To Document