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Monte Carlo Localization for Teach-and-Repeat Feature-Based Navigation

  • Matías Nitsche
  • Taihú Pire
  • Tomáš Krajník
  • Miroslav Kulich
  • Marta Mejail
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8717)

Abstract

This work presents a combination of a teach-and-replay visual navigation and Monte Carlo localization methods. It improves a reliable teach-and-replay navigation method by replacing its dependency on precise dead-reckoning by introducing Monte Carlo localization to determine robot position along the learned path. In consequence, the navigation method becomes robust to dead-reckoning errors, can be started from at any point in the map and can deal with the ‘kidnapped robot’ problem. Furthermore, the robot is localized with MCL only along the taught path, i.e. in one dimension, which does not require a high number of particles and significantly reduces the computational cost. Thus, the combination of MCL and teach-and-replay navigation mitigates the disadvantages of both methods. The method was tested using a P3-AT ground robot and a Parrot AR.Drone aerial robot over a long indoor corridor. Experiments show the validity of the approach and establish a solid base for continuing this work.

Keywords

Mobile Robot Position Estimate Iterative Learn Control Dead Reckoning Autonomous Navigation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Matías Nitsche
    • 1
  • Taihú Pire
    • 1
  • Tomáš Krajník
    • 2
  • Miroslav Kulich
    • 3
  • Marta Mejail
    • 1
  1. 1.Laboratory of Robotics and Embedded Systems, Computer Science Department, Faculty of Exact and Natural SciencesUniversity of Buenos AiresArgentina
  2. 2.Lincoln Centre for Autonomous Systems, School of Computer ScienceUniversity of LincolnUK
  3. 3.Intelligent and Mobile Robotics Group, Department of Cybernetics, Faculty of Electrical EngineeringCzech Technical University in PragueCzech Republic

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