Open Access

An improved particle filter for sparse environments

  • Edson Prestes1,
  • Marcus Ritt1 and
  • Gustavo Führ1
Journal of the Brazilian Computer Society15:BF03194506

Received: 7 July 2009

Accepted: 27 August 2009


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.


boundary value problemsautonomous navigationenvironment explorationglobal localizationMonte Carlo localization