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2013 Volume 28
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RESEARCH ARTICLE   Open Access    

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

  • 1.

    Department of Electronics Engineering

  • 2.

    Rua Dr. Roberto Frias, s/n 4200-465

  • 3.

    Instituto de Automatica

More Information
  • 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.
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  • Cite this article

    Fernando A. Auat Cheein, Fernando M. Lobo Pereira, Fernando di Sciascio, Ricardo Carelli. 2013. Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation. The Knowledge Engineering Review 28(1)35−57, doi: 10.1017/S0269888912000276
    Fernando A. Auat Cheein, Fernando M. Lobo Pereira, Fernando di Sciascio, Ricardo Carelli. 2013. Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation. The Knowledge Engineering Review 28(1)35−57, doi: 10.1017/S0269888912000276
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RESEARCH ARTICLE   Open Access    

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

  • 1.

    Department of Electronics Engineering

  • 2.

    Rua Dr. Roberto Frias, s/n 4200-465

  • 3.

    Instituto de Automatica

  • Published online: 02 November 2012
The Knowledge Engineering Review  28,  2013, 28(1): 35−57  |  Cite this article

Abstract: 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.

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    Acknowledgments

    • The authors would like to thank CONICET-Argentina and FCT-Portugal for partially founding this research.

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    Cite this article
    Fernando A. Auat Cheein, Fernando M. Lobo Pereira, Fernando di Sciascio, Ricardo Carelli. 2013. Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation. The Knowledge Engineering Review 28(1)35−57, doi: 10.1017/S0269888912000276
    Fernando A. Auat Cheein, Fernando M. Lobo Pereira, Fernando di Sciascio, Ricardo Carelli. 2013. Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation. The Knowledge Engineering Review 28(1)35−57, doi: 10.1017/S0269888912000276
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