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

Published online by Cambridge University Press:  31 March 2010

Fernando A. Auat Cheein ,
Fernando di Sciascio ,
Gustavo Scaglia  and
Ricardo Carelli
Fernando A. Auat Cheein*
Affiliation:
Instituto de Automatica, National University of San Juan, San Juan, Argentina
Fernando di Sciascio
Affiliation:
Instituto de Automatica, National University of San Juan, San Juan, Argentina
Gustavo Scaglia
Affiliation:
Instituto de Automatica, National University of San Juan, San Juan, Argentina
Ricardo Carelli
Affiliation:
Instituto de Automatica, National University of San Juan, San Juan, Argentina
*
*Corresponding author. E-mail: fauat@inaut.unsj.edu.ar
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Summary

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.

Keywords

SLAMFeature selectionMobile robot
Type
Article
Information
Robotica , Volume 29 , Issue 2 , March 2011 , pp. 271 - 282
Copyright
Copyright © Cambridge University Press 2010

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