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Enhanced resampling scheme for Monte Carlo localization

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Abstract

The indoor positioning problem is a critical research domain essential for real-time control of mobile robots. Within this field, Monte Carlo-based solutions have been devised, leveraging the processing of diverse sensor data to address numerous challenges in local and global positioning. This study focuses on resampling strategies within the conventional Monte Carlo framework, which directly impact positioning performance. From this perspective, in contrast to the conventional method of employing weight thresholding and full particle scattering when the robot becomes disoriented, this study proposes an alternative approach. It advocates for a localized space resampling strategy, adaptive noise injection guided by likelihood, and the incorporation of beam rejection modifications to address dynamic (unmapped) obstacles effectively. The real-time experimental results, conducted with varying particle counts, demonstrate that the proposed scheme effectively manages the presence of unmapped obstacles while employing fewer particles than the standard Monte Carlo implementation.

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Acknowledgements

This study was conducted in the Machine Vision Laboratory of Kocaeli University Mechatronics Engineering department.

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  1. Kocaeli University, 41001, Izmit, Kocaeli, Turkey

    Suat Karakaya

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  1. Suat Karakaya
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S. Karakaya wrote the manuscript, prepared figures, and organize the experimental work.

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Correspondence to Suat Karakaya.

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The author has no relevant financial or non-financial interests to disclose. The author has no conflicts of interest to declare that are relevant to the content of this article. The author certifies that he has no affiliation with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The author has no financial or proprietary interests in any material discussed in this article.

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Karakaya, S. Enhanced resampling scheme for Monte Carlo localization. Intel Serv Robotics (2024). https://doi.org/10.1007/s11370-024-00530-9

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