Monte Carlo localization for teach-and-repeat feature-based navigation

Nitsche, Matías, Pire, Taihú, Krajnik, Tomas , Kulich, Miroslav and Mejail, Marta (2014) Monte Carlo localization for teach-and-repeat feature-based navigation. In: Advances in Autonomous Robotics Systems. Lecture Notes in Computer Science, 8717 . Springer-Verlag, pp. 13-24. ISBN 9783319104003

Full content URL: http://dx.doi.org/10.1007/978-3-319-10401-0_2

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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 robotics, localization and mapping
Subjects:H Engineering > H670 Robotics and Cybernetics
Divisions:College of Science > School of Computer Science
ID Code:14894
Deposited On:10 Sep 2014 18:01

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