Fi-Vi: Large-Area Indoor Localization Scheme Combining ML/DL-Based Wireless Fingerprinting and Visual Positioning
Due to recent technological developments such as online navigation, augmented reality (AR), virtual reality (VR), and digital twins, and the high demand from users for various location-based services (LBS), research on location estimation techniques is being actively conducted. As a result, there is...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9970318/ |
| _version_ | 1811184568396414976 |
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| author | Sangwoo Park Dong Ho Kim Cheolwoo You |
| author_facet | Sangwoo Park Dong Ho Kim Cheolwoo You |
| author_sort | Sangwoo Park |
| collection | DOAJ |
| description | Due to recent technological developments such as online navigation, augmented reality (AR), virtual reality (VR), and digital twins, and the high demand from users for various location-based services (LBS), research on location estimation techniques is being actively conducted. As a result, there is an increasing demand for effective localization technologies that can be used in places where the use of Global Positioning System (GPS) is limited, especially in indoor spaces with very large areas. In this paper, a new structure for an indoor localization system in which wireless fingerprinting and visual-based positioning are hierarchically combined—the so-called Fi-Vi system—is proposed. This scheme consists of two steps: fingerprint-based localization (FBL) followed by visual-based localization (VBL). In the first positioning step (i.e., the FBL stage), the entire area of a significantly broad range for localization is divided into multiple regions, the size and the number of which depend on the target accuracy of this step. Moreover, a machine-learning (ML) or deep-learning (DL) model trained on a Wi-Fi fingerprint radio map selects suitable candidate regions among these multiple regions. In the second positioning step (i.e., the VBL stage), the final location is precisely estimated through visual-based positioning based on the received information regarding the candidate regions. The FBL stage uses a sparse radio map (SRM) for fingerprinting, which can be constructed with relatively little effort and cost compared to radio maps used in conventional fingerprinting methods. As a result, it can be easily combined with existing visual-based positioning methods with almost negligible implementation complexity. Because of the hierarchical structure and SRM, the proposed scheme shows a significant performance improvement in terms of computational load and time required for indoor localization compared to the use of the existing visual-based indoor positioning method alone. In addition, it provides high accuracy and robustness even in a dynamically changing indoor wireless environment where conventional wireless fingerprinting methods show significant performance degradation. Finally, the performance analysis of the proposed scheme was performed using the UJIIndoorLoc dataset. Experiments and theoretical analysis have shown that when the estimation accuracy of the candidate region for the test dataset was achieved at about 99% through the FBL stage, the average computational amount of the VBL stage for the final position estimation was only about 16% of that in cases where the visual-based positioning method was used alone. |
| first_indexed | 2024-04-11T13:15:35Z |
| format | Article |
| id | doaj.art-22136d6a434e4765ad9d493a62d0f8a7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T13:15:35Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-22136d6a434e4765ad9d493a62d0f8a72022-12-22T04:22:25ZengIEEEIEEE Access2169-35362022-01-011012709412711610.1109/ACCESS.2022.32268169970318Fi-Vi: Large-Area Indoor Localization Scheme Combining ML/DL-Based Wireless Fingerprinting and Visual PositioningSangwoo Park0https://orcid.org/0000-0002-9704-3669Dong Ho Kim1https://orcid.org/0000-0001-6571-5865Cheolwoo You2https://orcid.org/0000-0003-3519-3490Department of Information and Communications Engineering, Myongji University, Yongin, South KoreaDepartment of Electronic and IT Media Engineering, Seoul National University of Science and Technology, Seoul, South KoreaDepartment of Information and Communications Engineering, Myongji University, Yongin, South KoreaDue to recent technological developments such as online navigation, augmented reality (AR), virtual reality (VR), and digital twins, and the high demand from users for various location-based services (LBS), research on location estimation techniques is being actively conducted. As a result, there is an increasing demand for effective localization technologies that can be used in places where the use of Global Positioning System (GPS) is limited, especially in indoor spaces with very large areas. In this paper, a new structure for an indoor localization system in which wireless fingerprinting and visual-based positioning are hierarchically combined—the so-called Fi-Vi system—is proposed. This scheme consists of two steps: fingerprint-based localization (FBL) followed by visual-based localization (VBL). In the first positioning step (i.e., the FBL stage), the entire area of a significantly broad range for localization is divided into multiple regions, the size and the number of which depend on the target accuracy of this step. Moreover, a machine-learning (ML) or deep-learning (DL) model trained on a Wi-Fi fingerprint radio map selects suitable candidate regions among these multiple regions. In the second positioning step (i.e., the VBL stage), the final location is precisely estimated through visual-based positioning based on the received information regarding the candidate regions. The FBL stage uses a sparse radio map (SRM) for fingerprinting, which can be constructed with relatively little effort and cost compared to radio maps used in conventional fingerprinting methods. As a result, it can be easily combined with existing visual-based positioning methods with almost negligible implementation complexity. Because of the hierarchical structure and SRM, the proposed scheme shows a significant performance improvement in terms of computational load and time required for indoor localization compared to the use of the existing visual-based indoor positioning method alone. In addition, it provides high accuracy and robustness even in a dynamically changing indoor wireless environment where conventional wireless fingerprinting methods show significant performance degradation. Finally, the performance analysis of the proposed scheme was performed using the UJIIndoorLoc dataset. Experiments and theoretical analysis have shown that when the estimation accuracy of the candidate region for the test dataset was achieved at about 99% through the FBL stage, the average computational amount of the VBL stage for the final position estimation was only about 16% of that in cases where the visual-based positioning method was used alone.https://ieeexplore.ieee.org/document/9970318/Indoor localizationwireless fingerprintingmachine learningdeep learningradio mapvisual-based localization |
| spellingShingle | Sangwoo Park Dong Ho Kim Cheolwoo You Fi-Vi: Large-Area Indoor Localization Scheme Combining ML/DL-Based Wireless Fingerprinting and Visual Positioning IEEE Access Indoor localization wireless fingerprinting machine learning deep learning radio map visual-based localization |
| title | Fi-Vi: Large-Area Indoor Localization Scheme Combining ML/DL-Based Wireless Fingerprinting and Visual Positioning |
| title_full | Fi-Vi: Large-Area Indoor Localization Scheme Combining ML/DL-Based Wireless Fingerprinting and Visual Positioning |
| title_fullStr | Fi-Vi: Large-Area Indoor Localization Scheme Combining ML/DL-Based Wireless Fingerprinting and Visual Positioning |
| title_full_unstemmed | Fi-Vi: Large-Area Indoor Localization Scheme Combining ML/DL-Based Wireless Fingerprinting and Visual Positioning |
| title_short | Fi-Vi: Large-Area Indoor Localization Scheme Combining ML/DL-Based Wireless Fingerprinting and Visual Positioning |
| title_sort | fi vi large area indoor localization scheme combining ml dl based wireless fingerprinting and visual positioning |
| topic | Indoor localization wireless fingerprinting machine learning deep learning radio map visual-based localization |
| url | https://ieeexplore.ieee.org/document/9970318/ |
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