LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization
In this article, we present a unique comparative analysis, and evaluation of vision-, radio-, and audio-based localization algorithms. We create the first baseline for the aforementioned sensors using the recently published Lund University Vision, Radio, and Audio dataset, where all the sensors are...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Journal of Indoor and Seamless Positioning and Navigation |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10599608/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850035975744389120 |
|---|---|
| author | Ilayda Yaman Guoda Tian Erik Tegler Jens Gulin Nikhil Challa Fredrik Tufvesson Ove Edfors Kalle Astrom Steffen Malkowsky Liang Liu |
| author_facet | Ilayda Yaman Guoda Tian Erik Tegler Jens Gulin Nikhil Challa Fredrik Tufvesson Ove Edfors Kalle Astrom Steffen Malkowsky Liang Liu |
| author_sort | Ilayda Yaman |
| collection | DOAJ |
| description | In this article, we present a unique comparative analysis, and evaluation of vision-, radio-, and audio-based localization algorithms. We create the first baseline for the aforementioned sensors using the recently published Lund University Vision, Radio, and Audio dataset, where all the sensors are synchronized and measured in the same environment. Some of the challenges of using each specific sensor for indoor localization tasks are highlighted. Each sensor is paired with a current state-of-the-art localization algorithm and evaluated for different aspects: localization accuracy, reliability and sensitivity to environment changes, calibration requirements, and potential system complexity. Specifically, the evaluation covers the Oriented FAST and Rotated BRIEF simultaneous localization and mapping (SLAM) algorithm for vision-based localization with an RGB-D camera, a machine learning algorithm for radio-based localization with massive multiple-input multiple-output (MIMO) technology, and the StructureFromSound2 algorithm for audio-based localization with distributed microphones. The results can serve as a guideline and basis for further development of robust and high-precision multisensory localization systems, e.g., through sensor fusion, and context- and environment-aware adaptations. |
| format | Article |
| id | doaj-art-3dc8d764b3cb4481b6072cda3760733b |
| institution | DOAJ |
| issn | 2832-7322 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Indoor and Seamless Positioning and Navigation |
| spelling | doaj-art-3dc8d764b3cb4481b6072cda3760733b2025-08-20T02:57:19ZengIEEEIEEE Journal of Indoor and Seamless Positioning and Navigation2832-73222024-01-01224025010.1109/JISPIN.2024.342911010599608LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor LocalizationIlayda Yaman0https://orcid.org/0000-0002-2416-2077Guoda Tian1https://orcid.org/0000-0003-2466-4621Erik Tegler2Jens Gulin3https://orcid.org/0000-0002-3656-759XNikhil Challa4https://orcid.org/0000-0003-2985-247XFredrik Tufvesson5https://orcid.org/0000-0003-1072-0784Ove Edfors6https://orcid.org/0000-0001-5966-8468Kalle Astrom7https://orcid.org/0000-0002-8689-7810Steffen Malkowsky8https://orcid.org/0000-0003-2902-6071Liang Liu9https://orcid.org/0000-0001-9491-8821LTH, Lund University, Lund, SwedenLTH, Lund University, Lund, SwedenLTH, Lund University, Lund, SwedenLTH, Lund University, Lund, SwedenLTH, Lund University, Lund, SwedenLTH, Lund University, Lund, SwedenLTH, Lund University, Lund, SwedenLTH, Lund University, Lund, SwedenLTH, Lund University, Lund, SwedenLTH, Lund University, Lund, SwedenIn this article, we present a unique comparative analysis, and evaluation of vision-, radio-, and audio-based localization algorithms. We create the first baseline for the aforementioned sensors using the recently published Lund University Vision, Radio, and Audio dataset, where all the sensors are synchronized and measured in the same environment. Some of the challenges of using each specific sensor for indoor localization tasks are highlighted. Each sensor is paired with a current state-of-the-art localization algorithm and evaluated for different aspects: localization accuracy, reliability and sensitivity to environment changes, calibration requirements, and potential system complexity. Specifically, the evaluation covers the Oriented FAST and Rotated BRIEF simultaneous localization and mapping (SLAM) algorithm for vision-based localization with an RGB-D camera, a machine learning algorithm for radio-based localization with massive multiple-input multiple-output (MIMO) technology, and the StructureFromSound2 algorithm for audio-based localization with distributed microphones. The results can serve as a guideline and basis for further development of robust and high-precision multisensory localization systems, e.g., through sensor fusion, and context- and environment-aware adaptations.https://ieeexplore.ieee.org/document/10599608/Computer visionindoor localizationmassive multiple-input multiple-output (MIMO)multisensor datasetsimultaneous localization and mapping (SLAM) |
| spellingShingle | Ilayda Yaman Guoda Tian Erik Tegler Jens Gulin Nikhil Challa Fredrik Tufvesson Ove Edfors Kalle Astrom Steffen Malkowsky Liang Liu LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization IEEE Journal of Indoor and Seamless Positioning and Navigation Computer vision indoor localization massive multiple-input multiple-output (MIMO) multisensor dataset simultaneous localization and mapping (SLAM) |
| title | LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization |
| title_full | LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization |
| title_fullStr | LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization |
| title_full_unstemmed | LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization |
| title_short | LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization |
| title_sort | luvira dataset validation and discussion comparing vision radio and audio sensors for indoor localization |
| topic | Computer vision indoor localization massive multiple-input multiple-output (MIMO) multisensor dataset simultaneous localization and mapping (SLAM) |
| url | https://ieeexplore.ieee.org/document/10599608/ |
| work_keys_str_mv | AT ilaydayaman luviradatasetvalidationanddiscussioncomparingvisionradioandaudiosensorsforindoorlocalization AT guodatian luviradatasetvalidationanddiscussioncomparingvisionradioandaudiosensorsforindoorlocalization AT eriktegler luviradatasetvalidationanddiscussioncomparingvisionradioandaudiosensorsforindoorlocalization AT jensgulin luviradatasetvalidationanddiscussioncomparingvisionradioandaudiosensorsforindoorlocalization AT nikhilchalla luviradatasetvalidationanddiscussioncomparingvisionradioandaudiosensorsforindoorlocalization AT fredriktufvesson luviradatasetvalidationanddiscussioncomparingvisionradioandaudiosensorsforindoorlocalization AT oveedfors luviradatasetvalidationanddiscussioncomparingvisionradioandaudiosensorsforindoorlocalization AT kalleastrom luviradatasetvalidationanddiscussioncomparingvisionradioandaudiosensorsforindoorlocalization AT steffenmalkowsky luviradatasetvalidationanddiscussioncomparingvisionradioandaudiosensorsforindoorlocalization AT liangliu luviradatasetvalidationanddiscussioncomparingvisionradioandaudiosensorsforindoorlocalization |