Monte Carlo uncertainty maps-based for mobile robot autonomous SLAM navigation

Fernando A. Auat Cheein, Juan M. Toibero, Fernando Di Sciascio, Ricardo Carelli, F. Lobo Pereira

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)


This paper presents an uncertainty maps construction method of an environment by a mobile robot when executing a SLAM (Simultaneous Localization and Mapping) algorithm. The SLAM algorithm is implemented on a 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. 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 mobile robot has a contour-following controller implemented on it to drive the robot to the uncertainty points. SLAM real time experiments within real environments are also included in this work.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Industrial Technology
Number of pages6
ISBN (Electronic)9781424456970
Publication statusPublished - 27 May 2010
EventIEEE-ICIT 2010 International Conference on Industrial Technology - Vina del Mar, Chile
Duration: 14 Mar 201017 Mar 2010


ConferenceIEEE-ICIT 2010 International Conference on Industrial Technology
Abbreviated titleICIT 2010
CityVina del Mar

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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