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Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation

Published online by Cambridge University Press:  02 November 2012

Fernando A. Auat Cheein ,
Fernando M. Lobo Pereira ,
Fernando di Sciascio  and
Ricardo Carelli
Fernando A. Auat Cheein
Affiliation:
Department of Electronics Engineering, Universidad Tecnica Federico Santa Maria, Av. España 1680, Valparaiso, Chile; e-mail: fernando.auat@usm.cl
Fernando M. Lobo Pereira
Affiliation:
Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, s/n 4200-465, Porto, Portugal; e-mail: flp@fe.up.pt
Fernando di Sciascio
Affiliation:
Instituto de Automatica, Universidad Nacional de San Juan, Av. San Martin 1109 Oeste, San Juan, Argentina; e-mail: fernando@unsj.edu.ar, rcarelli@inaut.unsj.edu.ar
Ricardo Carelli
Affiliation:
Instituto de Automatica, Universidad Nacional de San Juan, Av. San Martin 1109 Oeste, San Juan, Argentina; e-mail: fernando@unsj.edu.ar, rcarelli@inaut.unsj.edu.ar
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Abstract

This paper addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.

Type
Articles
Information
The Knowledge Engineering Review , Volume 28 , Issue 1 , March 2013 , pp. 35 - 57
Copyright
Copyright © Cambridge University Press 2012

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