Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization

Yaman, Ilayda LU ; Tian, Guoda LU ; Tegler, Erik LU ; Gulin, Jens LU orcid ; Challa, Nikhil ; Tufvesson, Fredrik LU orcid ; Edfors, Ove LU orcid ; Åström, Kalle LU orcid ; Malkowsky, Steffen LU and Liu, Liang LU orcid (2024) In IEEE Journal of Indoor and Seamless Positioning and Navigation
Abstract
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 (LuViRA) 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 ORB-SLAM3 algorithm... (More)
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 (LuViRA) 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 ORB-SLAM3 algorithm for vision-based localization with an RGB-D camera, a machine-learning algorithm for radio-based localization with massive MIMO technology, and the SFS2 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 multi-sensory localization systems, e.g., through sensor fusion, and context- and environment-aware adaptation. (Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
epub
subject
in
IEEE Journal of Indoor and Seamless Positioning and Navigation
pages
11 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
ISSN
2832-7322
DOI
10.1109/JISPIN.2024.3429110
language
English
LU publication?
yes
id
23c680e7-d75b-4c38-9c15-b976da5f3e20
date added to LUP
2024-07-17 13:13:03
date last changed
2024-08-05 16:36:59
@article{23c680e7-d75b-4c38-9c15-b976da5f3e20,
  abstract     = {{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 (LuViRA) 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 ORB-SLAM3 algorithm for vision-based localization with an RGB-D camera, a machine-learning algorithm for radio-based localization with massive MIMO technology, and the SFS2 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 multi-sensory localization systems, e.g., through sensor fusion, and context- and environment-aware adaptation.}},
  author       = {{Yaman, Ilayda and Tian, Guoda and Tegler, Erik and Gulin, Jens and Challa, Nikhil and Tufvesson, Fredrik and Edfors, Ove and Åström, Kalle and Malkowsky, Steffen and Liu, Liang}},
  issn         = {{2832-7322}},
  language     = {{eng}},
  month        = {{07}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Journal of Indoor and Seamless Positioning and Navigation}},
  title        = {{LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization}},
  url          = {{http://dx.doi.org/10.1109/JISPIN.2024.3429110}},
  doi          = {{10.1109/JISPIN.2024.3429110}},
  year         = {{2024}},
}