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

TL;DR

This paper compares vision, radio, and audio sensors for indoor localization using the new LuViRA dataset, evaluating accuracy, reliability, and complexity to guide future multi-sensory system development.

Contribution

It introduces the first baseline comparison of vision, radio, and audio sensors for indoor localization using a synchronized multi-sensor dataset.

Findings

Vision-based localization achieves high accuracy but is sensitive to environment changes.

Radio-based localization offers robustness but requires extensive calibration.

Audio-based localization is useful in low-light conditions and with distributed microphones.

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, context, and environment-aware adaptation.