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EMFIR: Efficient Dense Magnetic Field Mapping Based on Implicit Representation

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Intelligent Robotics and Applications (ICIRA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15202))

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Abstract

Utilizing unique spatial magnetic field information can provide an effective solution for robot localization, especially in repetitive environment. However, it is not trivial to collect magnetic field data at every location due to the single-point sensing limitation of magnetometers and the large scale of environments. Therefore, we introduce the EMFIR method, which efficiently generates dense magnetic field maps from sparsely collected data. This method efficiently processes sparse magnetic field inputs into low-resolution images. Using the continuous image implicit representation, these magnetic field images are then transformed into high-resolution versions, ultimately culminating in the generation of dense magnetic field maps. We evaluated our technique on an indoor self-recorded dataset. Experimental data from real-world environments show that the method proposed in this paper achieves magnetic field mapping with an error of less than 0.05 G, and reduces the RMSE by 76% compared to the traditional Gaussian Process Regression (GPR) method.

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Disclosure of Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Authors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, 430074, China

    Dezhi Qin, Yicheng Lin, Shuhan Wang & Xin Luo

Authors
  1. Dezhi Qin
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  2. Yicheng Lin
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  3. Shuhan Wang
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  4. Xin Luo
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Corresponding author

Correspondence to Xin Luo .

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Editors and Affiliations

  1. Xi’an Jiaotong University, Xi’an, China

    Xuguang Lan

  2. Xi’an Jiaotong University, Xi’an, China

    Xuesong Mei

  3. Xi’an Jiaotong University, Xi’an, China

    Caigui Jiang

  4. Xi’an Jiaotong University, Xi’an, China

    Fei Zhao

  5. Xi'an Jiaotong University, Xi'an, China

    Zhiqiang Tian

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Qin, D., Lin, Y., Wang, S., Luo, X. (2025). EMFIR: Efficient Dense Magnetic Field Mapping Based on Implicit Representation. In: Lan, X., Mei, X., Jiang, C., Zhao, F., Tian, Z. (eds) Intelligent Robotics and Applications. ICIRA 2024. Lecture Notes in Computer Science(), vol 15202. Springer, Singapore. https://doi.org/10.1007/978-981-96-0774-7_19

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  • DOI: https://doi.org/10.1007/978-981-96-0774-7_19

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  • Print ISBN: 978-981-96-0773-0

  • Online ISBN: 978-981-96-0774-7

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