Covariance matrix analysis in simultaneous localization and mapping / (Record no. 6708)

MARC details
000 -LEADER
fixed length control field 04386ntm a2200289 a 4500
001 - CONTROL NUMBER
control field vtls000098111
003 - CONTROL NUMBER IDENTIFIER
control field KUKTEM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251117113324.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 161117s2016 my a f abm 000 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number THE0005163(Local)
039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE]
Level of rules in bibliographic description 201905131819
Level of effort used to assign nonsubject heading access points hanafiah
Level of effort used to assign subject headings 201710111456
Level of effort used to assign classification aishah
Level of effort used to assign subject headings 201611221219
Level of effort used to assign classification saini
-- 201611171604
-- saini
040 ## - CATALOGING SOURCE
Original cataloging agency UMP
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) FKEE .A65 2016 r Thesis
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Nur Aqilah Othman
245 10 - TITLE STATEMENT
Title Covariance matrix analysis in simultaneous localization and mapping /
Statement of responsibility, etc. Nur Aqilah Othman
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Kuantan, Pahang :
Name of publisher, distributor, etc. UMP,
Date of publication, distribution, etc. 2016
300 ## - PHYSICAL DESCRIPTION
Extent xx, 144 p. :
Other physical details ill. (some col.) ;
Dimensions 30 cm.
500 ## - GENERAL NOTE
General note Faculty of Electrical and Electronics Engineering
502 ## - DISSERTATION NOTE
Dissertation note Thesis (Doctor of Philosophy (Instrumentation Engineering)) -- Universiti Malaysia Pahang – 2016
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Bibliography : p. 133-142
520 3# - SUMMARY, ETC.
Summary, etc. Estimation at a specific time or also known as the filtering technique in estimation and control theory is a method to estimate the desired parameters from indirect and uncertain observations, taking into account the system and measurement errors. One of the applications that implements estimation technique is the simultaneous localization and mapping (SLAM) of a mobile robot. SLAM is one of the navigation techniques which enables the mobile robot to move autonomously and observes its surrounding in an unknown environment. SLAM does not require a priori map, but with the aid of sensors on board, the mobile robot incrementally builds a map of the environment and use this map to localize its position. Therefore, an estimation technique is used to provide the approximate location of mobile robot and landmarks at any time based on the measurement data that are recursively recorded by the sensors. In mobile robot SLAM, extended Kalman filter (EKF) has been one of the most preferable estimators due to its relatively simple algorithm and efficiency of the estimation through the representation of the belief by a multivariate Gaussian distribution; unimodal distribution, with a single mean annotated with a corresponding covariance uncertainty. Nonetheless, EKF-based SLAM suffers from high computational cost due to the update process of covariance matrix. One of the objectives of this thesis is to propose an alternative method to simplify the structure of covariance matrix by means of matrix diagonalization method using eigenvalues. The non-diagonal parts of the covariance matrix are cross-correlation elements, which represent the correlation between the position of the robot and the landmarks. In diagonalizing the covariance matrix, these terms would be eliminated. However, this thesis has proven that the cross-correlation elements are important to ensure the accuracy of the estimation; hence it should be integrated in the diagonalization process. In EKF-based SLAM, measurement data are required at each time step to complete the estimation process. Sudden absence of these data might affect the state estimation and the covariance value. Hence, this thesis also investigates the impact of this phenomenon, which is addressed as intermittent measurement condition. The requirement of Gaussian distribution behavior of measurement and system noises has limited the use of extended Kalman filter in all conditions. In mobile robot SLAM, the noise characteristics might be unknown. Therefore, H∞ filter is used instead of EKF under this condition. One of the biggest challenges in implementing H∞ filter-based SLAM is the manual parameter tuning. Thus, this thesis also proposes a sufficient condition for the estimation of H∞ filter-based SLAM by providing a lower boundary of the selection for the parameter gamma under specific assumptions and environmental conditions. All of these issues are examined and investigated from an estimation-theoretic perspective through mathematical analysis. Theorems, lemmas and propositions are proposed to represent the findings of the analysis. The results obtained were validated through simulation analysis.
610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element Faculty of Electrical and Electronics Engineering
General subdivision Dissertations
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Universities and Colleges
General subdivision Dissertations
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Theses
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://ecollib.ump.edu.my/3510/">http://ecollib.ump.edu.my/3510/</a>
Public note Library access only
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type Public note
  Not lost Library of Congress Classification   Not for loan UMPLIB PEKAN UMPLIB PEKAN 04/09/2019   FKEE .A65 2016 r Thesis 0000113718 04/09/2019 2 04/09/2019 Thesis c.2
  Not lost Library of Congress Classification   Not for loan UMPLIB PEKAN UMPLIB PEKAN 04/09/2019   FKEE .A65 2016 r Thesis 0000111065 04/09/2019 1 04/09/2019 Thesis  
  Not lost Library of Congress Classification   Not for loan UMPLIB PEKAN UMPLIB PEKAN 04/09/2019   CD 9891 | FKEE .A65 2016 r Thesis 0000111066 04/09/2019 1 04/09/2019 Thesis  

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