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Differential Evolution Markov Chain Filter for Global Localization

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Abstract

A key challenge for an autonomous mobile robot is to estimate its location according to the available information. A particular aspect of this task is the global localization problem. In our previous work, we developed an algorithm based on the Differential Evolution method that solves this problem in 2D and 3D environments. The robot’s pose is represented by a set of possible location estimates weighted by a fitness function. The Markov Chain Monte Carlo algorithms have been successfully applied to multiple fields such as econometrics or computing science. It has been demonstrated that they can be combined with the Differential Evolution method to solve efficiently many optimization problems. In this work, we have combined both approaches to develop a global localization filter. The algorithm performance has been tested in simulated and real maps. The population requirements have been reduced when compared to the previous version.

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Authors and Affiliations

  1. Carlos III University, Madrid, Spain

    Luis Moreno, Fernando Martín & Santiago Garrido

  2. Universidad Politécnica, Madrid, Spain

    María Luisa Muñoz

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  1. Luis Moreno
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  2. Fernando Martín
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  3. María Luisa Muñoz
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  4. Santiago Garrido
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Correspondence to Fernando Martín.

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Moreno, L., Martín, F., Muñoz, M.L. et al. Differential Evolution Markov Chain Filter for Global Localization. J Intell Robot Syst 82, 513–536 (2016). https://doi.org/10.1007/s10846-015-0245-8

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  • DOI: https://doi.org/10.1007/s10846-015-0245-8

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