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A 3D MAP AIDED DEEP LEARNING BASED INDOOR LOCALIZATION SYSTEM FOR SMART DEVICES
| Content Provider | Scilit |
|---|---|
| Author | Yang, Y. Toth, C. Brzezinska, D. |
| Copyright Year | 2020 |
| Description | Indoor positioning technologies represent a fast developing field of research due to the rapidly increasing need for indoor location-based services (ILBS); in particular, for applications using personal smart devices. Recently, progress in indoor mapping, including 3D modeling and semantic labeling started to offer benefits to indoor positioning algorithms; mainly, in terms of accuracy. This work presents a method for efficient and robust indoor localization, allowing to support applications in large-scale environments. To achieve high performance, the proposed concept integrates two main indoor localization techniques: Wi-Fi fingerprinting and deep learning-based visual localization using 3D map. The robustness and efficiency of technique is demonstrated with real-world experiences. |
| Ending Page | 397 |
| Page Count | 7 |
| Starting Page | 391 |
| e-ISSN | 21949034 |
| DOI | 10.5194/isprs-archives-xliii-b4-2020-391-2020 |
| Journal | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume Number | XLIII-B4-2 |
| Language | English |
| Publisher | Copernicus GmbH |
| Publisher Date | 2020-08-25 |
| Access Restriction | Open |
| Subject Keyword | Cybernetical Science Indoor Localization Deep Learning Based 3d Map |
| Content Type | Text |
| Resource Type | Article |
