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An improved particle filter for sparse environments

Journal of the Brazilian Computer Society volume 15, pages 55–64 (2009)Cite this article

Abstract

In this paper, we combine a path planner based on Boundary Value Problems (BVP) and Monte Carlo Localization (MCL) to solve the wake-up robot problem in a sparse environment. This problem is difficult since large regions of sparse environments do not provide relevant information for the robot to recover its pose. We propose a novel method that distributes particle poses only in relevant parts of the environment and leads the robot along these regions using the numeric solution of a BVP. Several experiments show that the improved method leads to a better initial particle distribution and a better convergence of the localization process.

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Authors and Affiliations

  1. Instituto de Informática - UFRGS, P.O. Box 15064, 91501-970, Porto Alegre, RS, Brazil

    Edson Prestes, Marcus Ritt & Gustavo Führ

Authors
  1. Edson Prestes
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  2. Marcus Ritt
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  3. Gustavo Führ
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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Prestes, E., Ritt, M. & Führ, G. An improved particle filter for sparse environments. J Braz Comp Soc 15, 55–64 (2009). https://doi.org/10.1007/BF03194506

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  • DOI: https://doi.org/10.1007/BF03194506

Keywords

  • boundary value problems
  • autonomous navigation
  • environment exploration
  • global localization
  • Monte Carlo localization