Towards features updating selection based on the covariance matrix of the SLAM system state

Fernando A. Auat Cheein, Fernando Di Sciascio, Gustavo Scaglia, Ricardo Carelli

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


This paper addresses the problem of a features selection criterion for a simultaneous localization and mapping (SLAM) algorithm implemented on a mobile robot. This SLAM algorithm is a sequential extended Kalman filter (EKF) implementation that extracts corners and lines from the environment. The selection procedure is made according to the convergence theorem of the EKF-based SLAM. Thus, only those features that contribute the most to the decreasing of the uncertainty ellipsoid volume of the SLAM system state will be chosen for the correction stage of the algorithm. The proposed features selection procedure restricts the number of features to be updated during the SLAM process, thus allowing real time implementations with non-reactive mobile robot navigation controllers. In addition, a Monte Carlo experiment is carried out in order to show the map reconstruction precision according to the Kullback-Leibler divergence curves. Consistency analysis of the proposed SLAM algorithm and experimental results in real environments are also shown in this work.

Original languageEnglish
Pages (from-to)271-282
Number of pages12
Issue number2
Publication statusPublished - Mar 2011


  • Feature selection
  • Mobile robot
  • SLAM

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • General Mathematics
  • Computer Science Applications


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