A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision
Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with imag...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/9/2641 |
| _version_ | 1827717364673675264 |
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| author | Anca Morar Alin Moldoveanu Irina Mocanu Florica Moldoveanu Ion Emilian Radoi Victor Asavei Alexandru Gradinaru Alex Butean |
| author_facet | Anca Morar Alin Moldoveanu Irina Mocanu Florica Moldoveanu Ion Emilian Radoi Victor Asavei Alexandru Gradinaru Alex Butean |
| author_sort | Anca Morar |
| collection | DOAJ |
| description | Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage (use of known environment data), sensing devices, type of detected elements, and localization method. It groups 70 of the most recent and relevant image based indoor localization methods according to the proposed classification and discusses their advantages and drawbacks. It highlights localization methods that also offer orientation information, as this is required by an increasing number of applications of indoor localization (e.g., augmented reality). |
| first_indexed | 2024-03-10T20:01:24Z |
| format | Article |
| id | doaj.art-811fc29613c44ebab18cf114f360ad83 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:01:24Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-811fc29613c44ebab18cf114f360ad832023-11-19T23:33:36ZengMDPI AGSensors1424-82202020-05-01209264110.3390/s20092641A Comprehensive Survey of Indoor Localization Methods Based on Computer VisionAnca Morar0Alin Moldoveanu1Irina Mocanu2Florica Moldoveanu3Ion Emilian Radoi4Victor Asavei5Alexandru Gradinaru6Alex Butean7Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, 060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, 060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, 060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, 060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, 060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, 060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, 060042 Bucharest, RomaniaFaculty of Engineering, Lucian Blaga University of Sibiu, 550024 Sibiu, RomaniaComputer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage (use of known environment data), sensing devices, type of detected elements, and localization method. It groups 70 of the most recent and relevant image based indoor localization methods according to the proposed classification and discusses their advantages and drawbacks. It highlights localization methods that also offer orientation information, as this is required by an increasing number of applications of indoor localization (e.g., augmented reality).https://www.mdpi.com/1424-8220/20/9/2641indoor localizationcomputer visionQR codesfiducial markers3D reconstruction |
| spellingShingle | Anca Morar Alin Moldoveanu Irina Mocanu Florica Moldoveanu Ion Emilian Radoi Victor Asavei Alexandru Gradinaru Alex Butean A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision Sensors indoor localization computer vision QR codes fiducial markers 3D reconstruction |
| title | A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision |
| title_full | A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision |
| title_fullStr | A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision |
| title_full_unstemmed | A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision |
| title_short | A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision |
| title_sort | comprehensive survey of indoor localization methods based on computer vision |
| topic | indoor localization computer vision QR codes fiducial markers 3D reconstruction |
| url | https://www.mdpi.com/1424-8220/20/9/2641 |
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