A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction
High-accuracy indoor positioning is a prerequisite to satisfy the increasing demands of position-based services in complex indoor scenes. Current indoor visual-positioning methods mainly include image retrieval-based methods, visual landmarks-based methods, and learning-based methods. To better over...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/11/21/2572 |
| _version_ | 1818941442596274176 |
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| author | Runzhi Wang Wenhui Wan Kaichang Di Ruilin Chen Xiaoxue Feng |
| author_facet | Runzhi Wang Wenhui Wan Kaichang Di Ruilin Chen Xiaoxue Feng |
| author_sort | Runzhi Wang |
| collection | DOAJ |
| description | High-accuracy indoor positioning is a prerequisite to satisfy the increasing demands of position-based services in complex indoor scenes. Current indoor visual-positioning methods mainly include image retrieval-based methods, visual landmarks-based methods, and learning-based methods. To better overcome the limitations of traditional methods such as them being labor-intensive, of poor accuracy, and time-consuming, this paper proposes a novel indoor-positioning method with automated red, green, blue and depth (RGB-D) image database construction. First, strategies for automated database construction are developed to reduce the workload of manually selecting database images and ensure the requirements of high-accuracy indoor positioning. The database is automatically constructed according to the rules, which is more objective and improves the efficiency of the image-retrieval process. Second, by combining the automated database construction module, convolutional neural network (CNN)-based image-retrieval module, and strict geometric relations-based pose estimation module, we obtain a high-accuracy indoor-positioning system. Furthermore, in order to verify the proposed method, we conducted extensive experiments on the public indoor environment dataset. The detailed experimental results demonstrated the effectiveness and efficiency of our indoor-positioning method. |
| first_indexed | 2024-12-20T06:55:36Z |
| format | Article |
| id | doaj.art-3b368c4887b147bc84443b900faaed8f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T06:55:36Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3b368c4887b147bc84443b900faaed8f2022-12-21T19:49:21ZengMDPI AGRemote Sensing2072-42922019-11-011121257210.3390/rs11212572rs11212572A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database ConstructionRunzhi Wang0Wenhui Wan1Kaichang Di2Ruilin Chen3Xiaoxue Feng4State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20A, Datun Road, Chaoyang District, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20A, Datun Road, Chaoyang District, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20A, Datun Road, Chaoyang District, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20A, Datun Road, Chaoyang District, Beijing 100101, ChinaInstitute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, ChinaHigh-accuracy indoor positioning is a prerequisite to satisfy the increasing demands of position-based services in complex indoor scenes. Current indoor visual-positioning methods mainly include image retrieval-based methods, visual landmarks-based methods, and learning-based methods. To better overcome the limitations of traditional methods such as them being labor-intensive, of poor accuracy, and time-consuming, this paper proposes a novel indoor-positioning method with automated red, green, blue and depth (RGB-D) image database construction. First, strategies for automated database construction are developed to reduce the workload of manually selecting database images and ensure the requirements of high-accuracy indoor positioning. The database is automatically constructed according to the rules, which is more objective and improves the efficiency of the image-retrieval process. Second, by combining the automated database construction module, convolutional neural network (CNN)-based image-retrieval module, and strict geometric relations-based pose estimation module, we obtain a high-accuracy indoor-positioning system. Furthermore, in order to verify the proposed method, we conducted extensive experiments on the public indoor environment dataset. The detailed experimental results demonstrated the effectiveness and efficiency of our indoor-positioning method.https://www.mdpi.com/2072-4292/11/21/2572visual positioningindoor scenesautomated database constructionimage retrieval |
| spellingShingle | Runzhi Wang Wenhui Wan Kaichang Di Ruilin Chen Xiaoxue Feng A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction Remote Sensing visual positioning indoor scenes automated database construction image retrieval |
| title | A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction |
| title_full | A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction |
| title_fullStr | A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction |
| title_full_unstemmed | A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction |
| title_short | A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction |
| title_sort | high accuracy indoor positioning method with automated rgb d image database construction |
| topic | visual positioning indoor scenes automated database construction image retrieval |
| url | https://www.mdpi.com/2072-4292/11/21/2572 |
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