Diagonalization of Covariance Matrix in Simultaneous Localization and Mapping of Mobile Robot

  • Maziatun Mohamad Mazlan
  • Nur Aqilah OthmanEmail author
  • Hamzah Ahmad
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 538)


The purpose of this study is to analyze the effects of covariance state update by means of modified algorithm of diagonal matrix using eigenvalue, and diagonalization function in MATLAB on the computational cost of extended Kalman filter based Simultaneous Localization and Mapping (SLAM). The multiplications of the covariance matrix with other parameters increase its dimension, which is twice the number of landmarks and might result in erroneous estimation. This motivates this study in searching for ways to reduce the computational cost of the covariance matrix without minimizing the accuracy of the state estimation using eigenvalue method. The matrix diagonalization strategy which is applied to the covariance matrix in EKF-based SLAM must be examined to simplify the multiplication procedure. Therefore, improvement is needed to find better diagonalization method. Simulation results demonstrate that MATLAB’s built-in diagonalization function can reduce the computational cost.


Covariance Diagonalization Eigenvalue Extended Kalman filter Localization 



This study was supported by the Universiti Malaysia Pahang (UMP) internal grant RDU170369.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Maziatun Mohamad Mazlan
    • 1
  • Nur Aqilah Othman
    • 2
    Email author
  • Hamzah Ahmad
    • 2
  1. 1.Electrical Engineering DepartmentPoliteknik Sultan Haji Ahmad ShahKuantanMalaysia
  2. 2.Faculty of Electrical and Electronics EngineeringUniversiti Malaysia PahangPekanMalaysia

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