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SLAM Method Based on Independent Particle Filters for Landmark Mapping and Localization for Mobile Robot Based on HF-band RFID System

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Abstract

A novel simultaneous localization and mapping (SLAM) technique based on independent particle filters for landmark mapping and localization for a mobile robot based on a high-frequency (HF)-band radio-frequency identification (RFID) system is proposed in this paper. SLAM is a technique for performing self-localization and map building simultaneously. FastSLAM is a standard landmark-based SLAM method. RFID is a robust identification system with ID tags and readers over wireless communication; further, it is rarely affected by obstacles in the robot area or by lighting conditions. Therefore, RFID is useful for self-localization and mapping for a mobile robot with a reasonable accuracy and sufficient robustness. In this study, multiple HF-band RFID readers are embedded in the bottom of an omnidirectional vehicle, and a large number of tags are installed on the floor. The HF-band RFID tags are used as the landmarks of the environment. We found that FastSLAM is not appropriate for this condition for two reasons. First, the tag detection of the HF-band RFID system does not follow the standard Gaussian distribution, which FastSLAM is supposed to have. Second, FastSLAM does not have a sufficient scalability, which causes its failure to handle a large number of landmarks. Therefore, we propose a novel SLAM method with two independent particle filters to solve these problems. The first particle filter is for self-localization based on Monte Carlo localization. The second particle filter is for landmark mapping. The particle filters are nonparametric so that it can handle the non-Gaussian distribution of the landmark detection. The separation of localization and landmark mapping reduces the computational cost significantly. The proposed method is evaluated in simulated and real environments. The experimental results show that the proposed method has more precise localization and mapping and a lower computational cost than FastSLAM.

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Acknowledgements

The authors gratefully acknowledge Art Finex Co., Ltd. for development of the RFID reader and for provision of other experimental equipment.

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Authors and Affiliations

  1. Department of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui, 3-9-1, Bunkyo, Fukui, Japan

    Jun Wang & Yasutake Takahashi

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  1. Jun Wang
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  2. Yasutake Takahashi
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Correspondence to Jun Wang.

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Wang, J., Takahashi, Y. SLAM Method Based on Independent Particle Filters for Landmark Mapping and Localization for Mobile Robot Based on HF-band RFID System. J Intell Robot Syst 92, 413–433 (2018). https://doi.org/10.1007/s10846-017-0701-8

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  • DOI: https://doi.org/10.1007/s10846-017-0701-8

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