LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization
This work addresses the need for robust multi-sensory localization systems, offering a comparative guideline for researchers and developers, though it is incremental as it builds on existing datasets and algorithms.
The paper tackled the problem of comparing vision, radio, and audio sensors for indoor localization by evaluating state-of-the-art algorithms on the synchronized LuViRA dataset, providing a baseline analysis of accuracy, reliability, and other factors.
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, context, and environment-aware adaptation.