Differential evolution Markov chain filter for global localization Articles uri icon

publication date

  • June 2016

start page

  • 513

end page

  • 536

issue

  • 03/04/16

volume

  • 82

International Standard Serial Number (ISSN)

  • 0921-0296

Electronic International Standard Serial Number (EISSN)

  • 1573-0409

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.

keywords

  • differential evolution; markov chain monte carlo; optimization method; global localization; mobile robots; mobile robot localization; monte-carlo localization; optimization; inference; tracking; models; mcmc