WiFi and Vision-Integrated Fingerprint for Smartphone-Based Self-Localization in Public Indoor Scenes
Smartphone-based indoor localization systems are increasingly needed in various types of applications. This article proposes a novel WiFi and vision-integrated fingerprint (Wi-Vi fingerprint) for accurate and robust indoor localization. The method consists of two steps of fingerprint mapping and fin...
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| Veröffentlicht in: | IEEE internet of things journal 2020-08, Vol.7 (8), p.6748-6761 |
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| creator | Huang, Gang Hu, Zhaozheng Wu, Jie Xiao, Hanbiao Zhang, Fan |
| description | Smartphone-based indoor localization systems are increasingly needed in various types of applications. This article proposes a novel WiFi and vision-integrated fingerprint (Wi-Vi fingerprint) for accurate and robust indoor localization. The method consists of two steps of fingerprint mapping and fingerprint localization. In the mapping step, the Wi-Vi fingerprints for all the sampling sites are computed by using EXIT signs as landmarks. In the localization step, a multiscale localization strategy is proposed, which includes coarse localization from weighted access points (WAPs)-based WiFi matching, the Gaussian weighted KNN (GW-KNN)-based image-level localization from holistic visual features, and finally, the metric localization for refinement. The proposed method has been tested in an indoor office building of 12 000 m 2 and a mega-mall of 7200 m 2 with different types of smartphones. The experimental results demonstrate that the proposed method can achieve 95% and 98% site recognition rates from image-level localization. The final localization errors after metric localization are less than a half meter on average. |
| doi_str_mv | 10.1109/JIOT.2020.2974928 |
| format | Article |
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This article proposes a novel WiFi and vision-integrated fingerprint (Wi-Vi fingerprint) for accurate and robust indoor localization. The method consists of two steps of fingerprint mapping and fingerprint localization. In the mapping step, the Wi-Vi fingerprints for all the sampling sites are computed by using EXIT signs as landmarks. In the localization step, a multiscale localization strategy is proposed, which includes coarse localization from weighted access points (WAPs)-based WiFi matching, the Gaussian weighted KNN (GW-KNN)-based image-level localization from holistic visual features, and finally, the metric localization for refinement. The proposed method has been tested in an indoor office building of 12 000 m 2 and a mega-mall of 7200 m 2 with different types of smartphones. The experimental results demonstrate that the proposed method can achieve 95% and 98% site recognition rates from image-level localization. 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This article proposes a novel WiFi and vision-integrated fingerprint (Wi-Vi fingerprint) for accurate and robust indoor localization. The method consists of two steps of fingerprint mapping and fingerprint localization. In the mapping step, the Wi-Vi fingerprints for all the sampling sites are computed by using EXIT signs as landmarks. In the localization step, a multiscale localization strategy is proposed, which includes coarse localization from weighted access points (WAPs)-based WiFi matching, the Gaussian weighted KNN (GW-KNN)-based image-level localization from holistic visual features, and finally, the metric localization for refinement. The proposed method has been tested in an indoor office building of 12 000 m 2 and a mega-mall of 7200 m 2 with different types of smartphones. The experimental results demonstrate that the proposed method can achieve 95% and 98% site recognition rates from image-level localization. The final localization errors after metric localization are less than a half meter on average.</description><subject>Fingerprint recognition</subject><subject>Fingerprints</subject><subject>Indoor self-localization</subject><subject>Localization</subject><subject>Mapping</subject><subject>multiscale localization</subject><subject>Object recognition</subject><subject>Office buildings</subject><subject>Sensors</subject><subject>Smart phones</subject><subject>Smartphones</subject><subject>Vision</subject><subject>visual features</subject><subject>Visualization</subject><subject>Wi-Vi fingerprint</subject><subject>Wireless communication</subject><subject>Wireless fidelity</subject><subject>Wireless sensor networks</subject><issn>2327-4662</issn><issn>2327-4662</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkE1LAzEQhoMoWLQ_QLwEPG_NV5PmqMXqSqFCqx5Dmo-asmZrsj3orzdLiziXmcP7zDAPAFcYjTBG8va5XqxGBBE0IlIwSSYnYEAoERXjnJz-m8_BMOctQqhgYyz5ALj3MAtQRwvfQg5trOrYuU3SnbNwFuLGpV0KsYO-TXD5qVO3-2ijq-51LoGla3w1b41uwo_uCg1DhC_7dRMMrKNte8a46PIlOPO6yW547Bfgdfawmj5V88VjPb2bV4aOZVdZwSjWjjEh6Rgzizxn1GJp1nzCvOHCEIGJLyX1ZCKxoAZJap3UhHtLOL0AN4e9u9R-7V3u1Lbdp1hOKsIow4QRykoKH1ImtTkn51X5sfz2rTBSvVDVC1W9UHUUWpjrAxOcc395WUwigekvEsNxJQ</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Huang, Gang</creator><creator>Hu, Zhaozheng</creator><creator>Wu, Jie</creator><creator>Xiao, Hanbiao</creator><creator>Zhang, Fan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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This article proposes a novel WiFi and vision-integrated fingerprint (Wi-Vi fingerprint) for accurate and robust indoor localization. The method consists of two steps of fingerprint mapping and fingerprint localization. In the mapping step, the Wi-Vi fingerprints for all the sampling sites are computed by using EXIT signs as landmarks. In the localization step, a multiscale localization strategy is proposed, which includes coarse localization from weighted access points (WAPs)-based WiFi matching, the Gaussian weighted KNN (GW-KNN)-based image-level localization from holistic visual features, and finally, the metric localization for refinement. The proposed method has been tested in an indoor office building of 12 000 m 2 and a mega-mall of 7200 m 2 with different types of smartphones. The experimental results demonstrate that the proposed method can achieve 95% and 98% site recognition rates from image-level localization. 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| subjects | Fingerprint recognition Fingerprints Indoor self-localization Localization Mapping multiscale localization Object recognition Office buildings Sensors Smart phones Smartphones Vision visual features Visualization Wi-Vi fingerprint Wireless communication Wireless fidelity Wireless sensor networks |
| title | WiFi and Vision-Integrated Fingerprint for Smartphone-Based Self-Localization in Public Indoor Scenes |
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