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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.

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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