Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach
After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/17/12/2847 |
| _version_ | 1811303540092567552 |
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| author | Mengyun Liu Ruizhi Chen Deren Li Yujin Chen Guangyi Guo Zhipeng Cao Yuanjin Pan |
| author_facet | Mengyun Liu Ruizhi Chen Deren Li Yujin Chen Guangyi Guo Zhipeng Cao Yuanjin Pan |
| author_sort | Mengyun Liu |
| collection | DOAJ |
| description | After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to “see” which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for indoor scene model training and communication with an Android client. To evaluate the performance, comparison experiments are conducted and the results demonstrate that a positioning accuracy of 1.32 m at 95% is achievable with the proposed solution. Both positioning accuracy and robustness are enhanced compared to approaches without scene constraint including commercial products such as IndoorAtlas. |
| first_indexed | 2024-04-13T07:49:53Z |
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| id | doaj.art-1ed77d95a5564a29b3c2c22e7ba070bd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:49:53Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1ed77d95a5564a29b3c2c22e7ba070bd2022-12-22T02:55:34ZengMDPI AGSensors1424-82202017-12-011712284710.3390/s17122847s17122847Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion ApproachMengyun Liu0Ruizhi Chen1Deren Li2Yujin Chen3Guangyi Guo4Zhipeng Cao5Yuanjin Pan6State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, ChinaAfter decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to “see” which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for indoor scene model training and communication with an Android client. To evaluate the performance, comparison experiments are conducted and the results demonstrate that a positioning accuracy of 1.32 m at 95% is achievable with the proposed solution. Both positioning accuracy and robustness are enhanced compared to approaches without scene constraint including commercial products such as IndoorAtlas.https://www.mdpi.com/1424-8220/17/12/2847indoor scene recognitiondeep learningindoor localizationWiFimagnetic field strengthparticle filtersmartphone |
| spellingShingle | Mengyun Liu Ruizhi Chen Deren Li Yujin Chen Guangyi Guo Zhipeng Cao Yuanjin Pan Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach Sensors indoor scene recognition deep learning indoor localization WiFi magnetic field strength particle filter smartphone |
| title | Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach |
| title_full | Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach |
| title_fullStr | Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach |
| title_full_unstemmed | Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach |
| title_short | Scene Recognition for Indoor Localization Using a Multi-Sensor Fusion Approach |
| title_sort | scene recognition for indoor localization using a multi sensor fusion approach |
| topic | indoor scene recognition deep learning indoor localization WiFi magnetic field strength particle filter smartphone |
| url | https://www.mdpi.com/1424-8220/17/12/2847 |
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