  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.
