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  • Enhanced Monte Carlo localization incorporating a mechanism for preventing premature convergence

    Author(s)
    Chien, Chiang-Heng
    Wang, Wei-Yen
    Jo, Jun
    Hsu, Chen-Chien
    Griffith University Author(s)
    Jo, Jun
    Year published
    2017
    Metadata
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    Abstract
    In this paper, we propose an enhanced Monte Carlo localization (EMCL) algorithm for mobile robots, which deals with the premature convergence problem in global localization as well as the estimation error existing in pose tracking. By incorporating a mechanism for preventing premature convergence (MPPC), which uses a “reference relative vector” to modify the weight of each sample, exploration of a highly symmetrical environment can be improved. As a consequence, the proposed method has the ability to converge particles toward the global optimum, resulting in successful global localization. Furthermore, by applying the unscented ...
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    In this paper, we propose an enhanced Monte Carlo localization (EMCL) algorithm for mobile robots, which deals with the premature convergence problem in global localization as well as the estimation error existing in pose tracking. By incorporating a mechanism for preventing premature convergence (MPPC), which uses a “reference relative vector” to modify the weight of each sample, exploration of a highly symmetrical environment can be improved. As a consequence, the proposed method has the ability to converge particles toward the global optimum, resulting in successful global localization. Furthermore, by applying the unscented Kalman Filter (UKF) to the prediction state and the previous state of particles in Monte Carlo Localization (MCL), an EMCL can be established for pose tracking, where the prediction state is modified by the Kalman gain derived from the modified prior error covariance. Hence, a better approximation that reduces the discrepancy between the state of the robot and the estimation can be obtained. Simulations and practical experiments confirmed that the proposed approach can improve the localization performance in both global localization and pose tracking.
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    Journal Title
    Robotica
    DOI
    https://doi.org/10.1017/S026357471600028X
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Artificial intelligence
    Mechanical engineering
    Publication URI
    http://hdl.handle.net/10072/99713
    Collection
    • Journal articles

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