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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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References

  1. Dulimart HS, Jain AK (1997) Mobile robot localization in indoor environment. Pattern Recogn 30(1):99–111

    Article  Google Scholar 

  2. Gao X, Li J, Fan L, Zhou Q, Yin K, Jianxu W, Song C, Huang L, Wang Z (2018) Review of wheeled mobile robots’ navigation problems and application prospects in agriculture. IEEE Access. 49248–49268.

  3. Baltzakis H, Trahanias PA (2003) Hybrid framework for mobile robot localization: Formulation using switching state-space models. Auton Robot 15:169–191

    Article  Google Scholar 

  4. Huang GP, Trawny N, Mourikis AI, Roumeliotis SI (2011) Observability-based consistent EKF estimators for multi-robot cooperative localization. Auton Robot 30:99–122

    Article  Google Scholar 

  5. Boccadoro M, Martinelli F, Pagnottelli S (2010) Constrained and quantized Kalman filtering for an RFID robot localization problem. Auton Robot 29:235–251

    Article  Google Scholar 

  6. Munaro M, Menegatti E (2014) Fast RGB-D people tracking for service robots. Auton Robot 37:227–242

    Article  Google Scholar 

  7. Akai N (2023) Reliable Monte Carlo localization for mobile robots. Journal of Field Robotics 40:595–613

    Article  Google Scholar 

  8. Buchanan R, Bednarek J, Camurri M (2021) Navigating by touch: haptic Monte Carlo localization via geometric sensing and terrain classification. Auton Robot 45:843–857

    Article  Google Scholar 

  9. He S, Song T, Wang P et al (2023) An enhanced adaptive Monte Carlo Localization for service robots in dynamic and featureless environments. J Intell Robot Syst 108:6

    Article  Google Scholar 

  10. Peavy M, Kim P, Oyediran H, Kim K (2023) Integration of real-time semantic building map updating with Adaptive Monte Carlo Localization (AMCL) for robust indoor Mobile Robot Localization. Appl Sci 13(2):909

    Article  Google Scholar 

  11. Jaenal A, Moreno F-A, Gonzalez-Jimenez J (2023) Int J Robot Res. Int J Robot Res 42(13):1117–1132

    Article  Google Scholar 

  12. Chen C, Ma Y, Lv J, Zhao X, Li L, Liu Y, Gao W (2023) OL-SLAM: a robust and versatile system of object localization and SLAM. Sensors 23(2):801

    Article  Google Scholar 

  13. Facerias M, Puig V, Alcala E (2022) Zonotopic linear parameter varying SLAM applied to autonomous vehicles. Sensors 22(10):3672

    Article  Google Scholar 

  14. Perea D, Hernández-Aceituno J, Morell A, Toledo J, Hamilton A, Acosta L (2013) MCL with sensor fusion based on a weighting mechanism versus a particle generation approach. In: 16th international IEEE conference on intelligent transportation systems, Netherlands.

  15. Patten T, Martens W, Fitch R (2018) Monte Carlo planning for active object classification. Auton Robot 42:391–421

    Article  Google Scholar 

  16. Ge G, Zhang Y, Wang W, Jiang Q, Hu L, Wang Y (2011) Text-MCL: autonomous mobile robot localization in similar environment using text-level semantic information. Machines 10(3):1–21

    Google Scholar 

  17. Zhang L, Zapata R, Lépinay P (2009) Self-adaptive Monte Carlo localization for cooperative multi-robot systems. In: 10th Towards autonomous robotic systems (TAROS 2009), pp 259–266.

  18. Nagi I, Adiprawita W, Mutijarsa K (2014) Vision-based Monte Carlo localization for RoboCup Humanoid Kid-Size League. In: 13th International conference on control automation robotics & vision (ICARCV), Singapore.

  19. Karakaya S, Ocak H (2020) Low cost easy-to-install indoor positioning system. J Intell Robot Syst 100:131–144

    Article  Google Scholar 

Download references

Acknowledgements

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

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Authors and Affiliations

  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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Cite this article

Karakaya, S. Enhanced resampling scheme for Monte Carlo localization. Intel Serv Robotics 17, 703–714 (2024). https://doi.org/10.1007/s11370-024-00530-9

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  • DOI: https://doi.org/10.1007/s11370-024-00530-9

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