Topological mapping and qualitative localization based on K-adjacent union clustering algorithm

In robotic applications, localization and mapping as parts of the navigation system are fundamental competence for mobile autonomous systems. The position of the mobile robot is known as qualitative localization inside a topological map, where the place recognition is an essential problem to overcom...

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Main Author:	Karasfi, Babak
Format:	Thesis
Language:	English
Published:	2012
Subjects:	Topographic maps Cluster analysis Algorithms
Online Access:	http://psasir.upm.edu.my/id/eprint/31442/1/ITMA%202012%2012R.pdf
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id	my-upm-ir.31442
record_format	uketd_dc
spelling	my-upm-ir.314422015-02-24T05:54:02Z Topological mapping and qualitative localization based on K-adjacent union clustering algorithm 2012-10 Karasfi, Babak In robotic applications, localization and mapping as parts of the navigation system are fundamental competence for mobile autonomous systems. The position of the mobile robot is known as qualitative localization inside a topological map, where the place recognition is an essential problem to overcome. Previously, supervised place recognition approaches have been used to solve global localization in offline mode. The aim of this thesis is to develop a mobile robot topological mapping and qualitative localization method based on unsupervised and fully appearance-based place recognition approach. In this research, two different methods are designed and implemented to answer the aim of this thesis. These methods focus on perspective or omnidirectional image similarity based on local features or the combination of global and local features which are identified as speed-up robust feature (SURF) and hue saturation intensity (HIS) color histogram. Moreover, proposed methods are spatial and sequential based place clustering methods (unsupervised learning) which are try to find the representative image that is more similar to the current adjacent robots query image. Therefore, the topological map graph of the place clusters can be created and qualitative localization can be performed over the topological map graph. According to the experimental results, the average of recognition precision for the first offline proposed method is 95% and in a different illumination condition is 86%. Moreover this performance in the kidnapped robot experiment is more than 90%. The average of online place recognition percentage for the second online, incremental and expandable proposed method is 93.56% and in different illumination conditions is 86.06%. In addition, the average performance of the topological mapping and qualitative localization results, obtained from expanded-environment experiments is 91.71%. Considering all results, the proposed topological mapping and qualitative localization methods are robust, accurate, cost effective, portable, low power consumption, low weight, easy to install without any camera calibration and can be applied on various mobile robot platforms. Topographic maps Cluster analysis Algorithms 2012-10 Thesis http://psasir.upm.edu.my/id/eprint/31442/ http://psasir.upm.edu.my/id/eprint/31442/1/ITMA%202012%2012R.pdf application/pdf en public phd doctoral Universiti Putra Malaysia Topographic maps Cluster analysis Algorithms Institute of Advanced Technology
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Topographic maps Cluster analysis Algorithms
spellingShingle	Topographic maps Cluster analysis Algorithms Karasfi, Babak Topological mapping and qualitative localization based on K-adjacent union clustering algorithm
description	In robotic applications, localization and mapping as parts of the navigation system are fundamental competence for mobile autonomous systems. The position of the mobile robot is known as qualitative localization inside a topological map, where the place recognition is an essential problem to overcome. Previously, supervised place recognition approaches have been used to solve global localization in offline mode. The aim of this thesis is to develop a mobile robot topological mapping and qualitative localization method based on unsupervised and fully appearance-based place recognition approach. In this research, two different methods are designed and implemented to answer the aim of this thesis. These methods focus on perspective or omnidirectional image similarity based on local features or the combination of global and local features which are identified as speed-up robust feature (SURF) and hue saturation intensity (HIS) color histogram. Moreover, proposed methods are spatial and sequential based place clustering methods (unsupervised learning) which are try to find the representative image that is more similar to the current adjacent robots query image. Therefore, the topological map graph of the place clusters can be created and qualitative localization can be performed over the topological map graph. According to the experimental results, the average of recognition precision for the first offline proposed method is 95% and in a different illumination condition is 86%. Moreover this performance in the kidnapped robot experiment is more than 90%. The average of online place recognition percentage for the second online, incremental and expandable proposed method is 93.56% and in different illumination conditions is 86.06%. In addition, the average performance of the topological mapping and qualitative localization results, obtained from expanded-environment experiments is 91.71%. Considering all results, the proposed topological mapping and qualitative localization methods are robust, accurate, cost effective, portable, low power consumption, low weight, easy to install without any camera calibration and can be applied on various mobile robot platforms.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Karasfi, Babak
author_facet	Karasfi, Babak
author_sort	Karasfi, Babak
title	Topological mapping and qualitative localization based on K-adjacent union clustering algorithm
title_short	Topological mapping and qualitative localization based on K-adjacent union clustering algorithm
title_full	Topological mapping and qualitative localization based on K-adjacent union clustering algorithm
title_fullStr	Topological mapping and qualitative localization based on K-adjacent union clustering algorithm
title_full_unstemmed	Topological mapping and qualitative localization based on K-adjacent union clustering algorithm
title_sort	topological mapping and qualitative localization based on k-adjacent union clustering algorithm
granting_institution	Universiti Putra Malaysia
granting_department	Institute of Advanced Technology
publishDate	2012
url	http://psasir.upm.edu.my/id/eprint/31442/1/ITMA%202012%2012R.pdf
_version_	1747811615215976448

Topological mapping and qualitative localization based on K-adjacent union clustering algorithm

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