Analysis of robot localisation performance based on extended kalman filter

This thesis presents the Simultaneous Localization and Mapping (SLAM) problem for a mobile robot in an unknown indoor environment. A most current localisation algorithm has less flexibility and autonomy because it depends on human to determine what aspects of the sensor data to use in localisation....

Full description

Saved in:

Bibliographic Details
Main Author:	Che Hassan, Farah Hazwani
Format:	Thesis
Language:	English English English
Published:	2015
Subjects:	TJ210.2-211.47 Mechanical devices and figures Automata Ingenious mechanisms Robots (General)
Online Access:	http://eprints.uthm.edu.my/1313/2/FARAH%20HAZWANI%20CHE%20HASSAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1313/1/24p%20FARAH%20HAZWANI%20CHE%20HASSAN.pdf http://eprints.uthm.edu.my/1313/3/FARAH%20HAZWANI%20CHE%20HASSAN%20WATERMARK.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uthm-ep.1313
record_format	uketd_dc
spelling	my-uthm-ep.13132021-10-03T06:21:42Z Analysis of robot localisation performance based on extended kalman filter 2015-01 Che Hassan, Farah Hazwani TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) This thesis presents the Simultaneous Localization and Mapping (SLAM) problem for a mobile robot in an unknown indoor environment. A most current localisation algorithm has less flexibility and autonomy because it depends on human to determine what aspects of the sensor data to use in localisation. To improve the localisation accuracy for a mobile robot, the Extended Kalman Filter (EKF) algorithm is used to achieve the required robustness and accuracy. EKF is a technique from estimation theory that combines the information of different uncertain sources to obtain the value of variables. However, there are a number of variations of EKF with different values of variables, which lead to contradicting results in terms of standard deviations of path (distance) and angle. This project is implemented based on the existing localisation algorithm [30]. There are two types of results that have been analysed in this paper. First is the performance of the algorithm using different parameters in which different velocities and number of landmarks have been used to determine the accuracy of the localisation method. Second is comparing the performance of update approaches of filters namely Kalman Filter Joseph, Kalman Filter Cholesky and Kalman Filter Update in different scenarios. MATLAB coding [30] is used to run the simulation of update approaches of filters. Finding the best variation and a good choice of variables are important factors to have acceptable results consistently. 2015-01 Thesis http://eprints.uthm.edu.my/1313/ http://eprints.uthm.edu.my/1313/2/FARAH%20HAZWANI%20CHE%20HASSAN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1313/1/24p%20FARAH%20HAZWANI%20CHE%20HASSAN.pdf text en public http://eprints.uthm.edu.my/1313/3/FARAH%20HAZWANI%20CHE%20HASSAN%20WATERMARK.pdf text en validuser phd masters Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TJ210.2-211.47 Mechanical devices and figures Automata Ingenious mechanisms Robots (General)
spellingShingle	TJ210.2-211.47 Mechanical devices and figures Automata Ingenious mechanisms Robots (General) Che Hassan, Farah Hazwani Analysis of robot localisation performance based on extended kalman filter
description	This thesis presents the Simultaneous Localization and Mapping (SLAM) problem for a mobile robot in an unknown indoor environment. A most current localisation algorithm has less flexibility and autonomy because it depends on human to determine what aspects of the sensor data to use in localisation. To improve the localisation accuracy for a mobile robot, the Extended Kalman Filter (EKF) algorithm is used to achieve the required robustness and accuracy. EKF is a technique from estimation theory that combines the information of different uncertain sources to obtain the value of variables. However, there are a number of variations of EKF with different values of variables, which lead to contradicting results in terms of standard deviations of path (distance) and angle. This project is implemented based on the existing localisation algorithm [30]. There are two types of results that have been analysed in this paper. First is the performance of the algorithm using different parameters in which different velocities and number of landmarks have been used to determine the accuracy of the localisation method. Second is comparing the performance of update approaches of filters namely Kalman Filter Joseph, Kalman Filter Cholesky and Kalman Filter Update in different scenarios. MATLAB coding [30] is used to run the simulation of update approaches of filters. Finding the best variation and a good choice of variables are important factors to have acceptable results consistently.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Master's degree
author	Che Hassan, Farah Hazwani
author_facet	Che Hassan, Farah Hazwani
author_sort	Che Hassan, Farah Hazwani
title	Analysis of robot localisation performance based on extended kalman filter
title_short	Analysis of robot localisation performance based on extended kalman filter
title_full	Analysis of robot localisation performance based on extended kalman filter
title_fullStr	Analysis of robot localisation performance based on extended kalman filter
title_full_unstemmed	Analysis of robot localisation performance based on extended kalman filter
title_sort	analysis of robot localisation performance based on extended kalman filter
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Electrical and Electronic Engineering
publishDate	2015
url	http://eprints.uthm.edu.my/1313/2/FARAH%20HAZWANI%20CHE%20HASSAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1313/1/24p%20FARAH%20HAZWANI%20CHE%20HASSAN.pdf http://eprints.uthm.edu.my/1313/3/FARAH%20HAZWANI%20CHE%20HASSAN%20WATERMARK.pdf
_version_	1747830767776432128

Analysis of robot localisation performance based on extended kalman filter

Similar Items