TY - JOUR
T1 - A hypothesis of state covariance decorrelation effects to partial observability SLAM
AU - Ahmad, Hamzah
AU - Othman, Nur Aqilah
AU - Saari, Mohd Mawardi
AU - Ramli, Mohd Syakirin
AU - Mazlan, Maziatun Binti Mohamad
AU - Namerikawa, T.
N1 - Funding Information:
The authors would like to thank Ministry of Higher Education and Universiti Malaysia Pahang for supporting this research under RDU160145 and RDU160379.
Funding Information:
Mohd Mawardi Saari received his B.Eng, M.Eng and PhD in Engineering at the Okayama University, Japan in 2011,2013 and 2015 respectively. He received a scholarship from Monbukagakusho (MEXT), Japan during his bachelor’s and master’s studied and then was offered a scholarship by Universiti Malaysia Pahang under fellowship programme to pursue his study in PhD. He currently serves as a senior lecturer at the Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang located in Pahang, Malaysia. His research interests include instrumentations of magnetometer, characterization of magnetic nanoparticles and NonDestructive Test (NDT) using magnetic method.
Publisher Copyright:
© 2019 Institute of Advanced Engineering and Science. All rights reserved.
PY - 2019/5
Y1 - 2019/5
N2 - This paper analyze the performance of partial observability in simultaneous localization and mapping(SLAM) problem. The study focuses mainly on the effect of having a decorrelation technique known as Covariance Inflation to the estimation. The matrix inversion will be the main element to be investigated through two conditions with respect to some defined environment namely as unstable partially observable SLAM and partially observable SLAM via matrix norm analysis. For assessment purposes, the Extended Kalman Filter estimation is referred as the estimator to understand how the conditions can influence the results. The simulation results depicted that, the matrix norm is able to determine the efficiency of estimation and is proportional to the uncertainties of the system.
AB - This paper analyze the performance of partial observability in simultaneous localization and mapping(SLAM) problem. The study focuses mainly on the effect of having a decorrelation technique known as Covariance Inflation to the estimation. The matrix inversion will be the main element to be investigated through two conditions with respect to some defined environment namely as unstable partially observable SLAM and partially observable SLAM via matrix norm analysis. For assessment purposes, the Extended Kalman Filter estimation is referred as the estimator to understand how the conditions can influence the results. The simulation results depicted that, the matrix norm is able to determine the efficiency of estimation and is proportional to the uncertainties of the system.
KW - Extended kalman filter
KW - Matrix norm
KW - Partial observability
KW - SLAM
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U2 - 10.11591/ijeecs.v14.i2.pp588-596
DO - 10.11591/ijeecs.v14.i2.pp588-596
M3 - Article
AN - SCOPUS:85062548806
SN - 2502-4752
VL - 14
SP - 588
EP - 596
JO - Indonesian Journal of Electrical Engineering and Computer Science
JF - Indonesian Journal of Electrical Engineering and Computer Science
IS - 2
ER -