Scan Matching and KNN Classification for Mobile Robot Localisation Algorithm

Mobile robots have made tremendous impact in our modern lives today, and its development is set to continue further. One of the most important domains to allow the interaction of mobile robots with human is its ability to know where it is in its environment, and how to navigate through it. This abil...

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Main Authors:	Addie, Irawan, Marni Azira, Markom, Abdul Hamid, Adom, Mohd Muslim Tan, E. S.
Format:	Conference or Workshop Item
Language:	English
Published:	IEEE 2017
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/18650/ http://umpir.ump.edu.my/id/eprint/18650/1/fkee-2017-addie-scan%20matching%20and%20knn1.pdf

id	ump-18650
recordtype	eprints
spelling	ump-186502017-10-31T04:00:59Z http://umpir.ump.edu.my/id/eprint/18650/ Scan Matching and KNN Classification for Mobile Robot Localisation Algorithm Addie, Irawan Marni Azira, Markom Abdul Hamid, Adom Mohd Muslim Tan, E. S. TK Electrical engineering. Electronics Nuclear engineering Mobile robots have made tremendous impact in our modern lives today, and its development is set to continue further. One of the most important domains to allow the interaction of mobile robots with human is its ability to know where it is in its environment, and how to navigate through it. This ability, however, needs algorithm has become more complex and requires high computational ability due to the demand for high accuracy, real time implementations and multi-tasking requirements. These are partly due to the need of multi-sensory system. This paper presents the use of single laser range finder for the mobile robot mapping and localisation system. The localisation algorithm is developed using scan matching method which is incorporated with K-nearest neighbours (KNN) classification. The mobile robot and the developed algorithm are tested in static environment. The results of the location estimation are able to achieve 80% of accuracy for each scan location with the distance range of ±2cm compared to the real location. As conclusion, the simple flow of the algorithm is suitable to replace the complex and high computational algorithm and system. IEEE 2017 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/18650/1/fkee-2017-addie-scan%20matching%20and%20knn1.pdf Addie, Irawan and Marni Azira, Markom and Abdul Hamid, Adom and Mohd Muslim Tan, E. S. (2017) Scan Matching and KNN Classification for Mobile Robot Localisation Algorithm. In: 3rd IEEE International Symposium on Robotic & Manufacturing Automation , 19-21 September 2017 , Universiti Putra Malaysia, Malaysia. . (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Addie, Irawan Marni Azira, Markom Abdul Hamid, Adom Mohd Muslim Tan, E. S. Scan Matching and KNN Classification for Mobile Robot Localisation Algorithm
description	Mobile robots have made tremendous impact in our modern lives today, and its development is set to continue further. One of the most important domains to allow the interaction of mobile robots with human is its ability to know where it is in its environment, and how to navigate through it. This ability, however, needs algorithm has become more complex and requires high computational ability due to the demand for high accuracy, real time implementations and multi-tasking requirements. These are partly due to the need of multi-sensory system. This paper presents the use of single laser range finder for the mobile robot mapping and localisation system. The localisation algorithm is developed using scan matching method which is incorporated with K-nearest neighbours (KNN) classification. The mobile robot and the developed algorithm are tested in static environment. The results of the location estimation are able to achieve 80% of accuracy for each scan location with the distance range of ±2cm compared to the real location. As conclusion, the simple flow of the algorithm is suitable to replace the complex and high computational algorithm and system.
format	Conference or Workshop Item
author	Addie, Irawan Marni Azira, Markom Abdul Hamid, Adom Mohd Muslim Tan, E. S.
author_facet	Addie, Irawan Marni Azira, Markom Abdul Hamid, Adom Mohd Muslim Tan, E. S.
author_sort	Addie, Irawan
title	Scan Matching and KNN Classification for Mobile Robot Localisation Algorithm
title_short	Scan Matching and KNN Classification for Mobile Robot Localisation Algorithm
title_full	Scan Matching and KNN Classification for Mobile Robot Localisation Algorithm
title_fullStr	Scan Matching and KNN Classification for Mobile Robot Localisation Algorithm
title_full_unstemmed	Scan Matching and KNN Classification for Mobile Robot Localisation Algorithm
title_sort	scan matching and knn classification for mobile robot localisation algorithm
publisher	IEEE
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/18650/ http://umpir.ump.edu.my/id/eprint/18650/1/fkee-2017-addie-scan%20matching%20and%20knn1.pdf
first_indexed	2023-09-18T22:26:32Z
last_indexed	2023-09-18T22:26:32Z
_version_	1777416003775365120

Scan Matching and KNN Classification for Mobile Robot Localisation Algorithm

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