Indoor Positioning on Smartphones Using Built-In Sensors and Visual Images
With the rapid development of mobile Internet technology, localization using visual image information has become a hot problem in the field of indoor localization research, which is not affected by signal multipath and fading and can achieve high accuracy localization in indoor areas with complex el...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/14/2/242 |
| _version_ | 1797619233183498240 |
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| author | Jiaqiang Yang Danyang Qin Huapeng Tang Haoze Bie Gengxin Zhang Lin Ma |
| author_facet | Jiaqiang Yang Danyang Qin Huapeng Tang Haoze Bie Gengxin Zhang Lin Ma |
| author_sort | Jiaqiang Yang |
| collection | DOAJ |
| description | With the rapid development of mobile Internet technology, localization using visual image information has become a hot problem in the field of indoor localization research, which is not affected by signal multipath and fading and can achieve high accuracy localization in indoor areas with complex electromagnetic environments. However, in practical applications, position estimation using visual images is easily influenced by the user’s photo pose. In this paper, we propose a multiple-sensor-assisted visual localization method in which the method constructs a machine learning classifier using multiple smart sensors for pedestrian pose estimation, which improves the retrieval efficiency and localization accuracy. The method mainly combines the advantages of visual image location estimation and pedestrian pose estimation based on multiple smart sensors and considers the effect of pedestrian photographing poses on location estimation. The built-in sensors of smartphones are used as the source of pedestrian pose estimation data, which constitutes a feasible location estimation method based on visual information. Experimental results show that the method proposed in this paper has good localization accuracy and robustness. In addition, the experimental scene in this paper is a common indoor scene and the experimental device is a common smartphone. Therefore, we believe that the proposed method in this paper has the potential to be widely used in future indoor navigation applications in complex scenarios (e.g., mall navigation). |
| first_indexed | 2024-03-11T08:25:08Z |
| format | Article |
| id | doaj.art-60269eefcad547a3b7d73b6cbdaa2555 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-11T08:25:08Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-60269eefcad547a3b7d73b6cbdaa25552023-11-16T22:09:29ZengMDPI AGMicromachines2072-666X2023-01-0114224210.3390/mi14020242Indoor Positioning on Smartphones Using Built-In Sensors and Visual ImagesJiaqiang Yang0Danyang Qin1Huapeng Tang2Haoze Bie3Gengxin Zhang4Lin Ma5Department of Electronic and Communication Engineering, Heilongjiang University, Harbin 150080, ChinaDepartment of Electronic and Communication Engineering, Heilongjiang University, Harbin 150080, ChinaDepartment of Electronic and Communication Engineering, Heilongjiang University, Harbin 150080, ChinaDepartment of Electronic and Communication Engineering, Heilongjiang University, Harbin 150080, ChinaDepartment of Electronic and Communication Engineering, Heilongjiang University, Harbin 150080, ChinaDepartment of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, ChinaWith the rapid development of mobile Internet technology, localization using visual image information has become a hot problem in the field of indoor localization research, which is not affected by signal multipath and fading and can achieve high accuracy localization in indoor areas with complex electromagnetic environments. However, in practical applications, position estimation using visual images is easily influenced by the user’s photo pose. In this paper, we propose a multiple-sensor-assisted visual localization method in which the method constructs a machine learning classifier using multiple smart sensors for pedestrian pose estimation, which improves the retrieval efficiency and localization accuracy. The method mainly combines the advantages of visual image location estimation and pedestrian pose estimation based on multiple smart sensors and considers the effect of pedestrian photographing poses on location estimation. The built-in sensors of smartphones are used as the source of pedestrian pose estimation data, which constitutes a feasible location estimation method based on visual information. Experimental results show that the method proposed in this paper has good localization accuracy and robustness. In addition, the experimental scene in this paper is a common indoor scene and the experimental device is a common smartphone. Therefore, we believe that the proposed method in this paper has the potential to be widely used in future indoor navigation applications in complex scenarios (e.g., mall navigation).https://www.mdpi.com/2072-666X/14/2/242indoor localizationsensorsvisual positioningmachine learning |
| spellingShingle | Jiaqiang Yang Danyang Qin Huapeng Tang Haoze Bie Gengxin Zhang Lin Ma Indoor Positioning on Smartphones Using Built-In Sensors and Visual Images Micromachines indoor localization sensors visual positioning machine learning |
| title | Indoor Positioning on Smartphones Using Built-In Sensors and Visual Images |
| title_full | Indoor Positioning on Smartphones Using Built-In Sensors and Visual Images |
| title_fullStr | Indoor Positioning on Smartphones Using Built-In Sensors and Visual Images |
| title_full_unstemmed | Indoor Positioning on Smartphones Using Built-In Sensors and Visual Images |
| title_short | Indoor Positioning on Smartphones Using Built-In Sensors and Visual Images |
| title_sort | indoor positioning on smartphones using built in sensors and visual images |
| topic | indoor localization sensors visual positioning machine learning |
| url | https://www.mdpi.com/2072-666X/14/2/242 |
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