Indoor Localization Using Radio, Vision and Audio Sensors: Real-Life
  Data Validation and Discussion
Ilayda Yaman Guoda Tian Erik Tegler Patrik Persson Nikhil Challa Fredrik Tufvesson Ove Edfors Kalle Astrom Steffen Malkowsky Liang Liu
This paper investigates indoor localization methods using radio, vision, and
audio sensors, respectively, in the same environment. The evaluation is based
on state-of-the-art algorithms and uses a real-life dataset. More specifically,
we evaluate a machine learning algorithm for radio-based localization with
massive MIMO technology, an ORB-SLAM3 algorithm for vision-based localization
with an RGB-D camera, and an SFS2 algorithm for audio-based localization with
microphone arrays. Aspects including localization accuracy, reliability,
calibration requirements, and potential system complexity are discussed to
analyze the advantages and limitations of using different sensors for indoor
localization tasks. 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.