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.

本文研究使用无线电、视觉和音频传感器进行室内定位的方法。通过对最先进的算法和真实数据集的评估，我们更具体地评估了一种基于无线电的机器学习算法与大规模 MIMO 技术、基于视觉的 ORB-SLAM3 算法与 RGB-D 相机以及基于音频的 SFS2 算法与麦克风阵列进行室内定位。讨论了定位精度、可靠性、校准要求和潜在系统复杂性等方面，以分析不同传感器在室内定位任务中使用的优势和局限性。结果可作为鲁棒高精度多传感器定位系统进一步发展的指南和基础，例如通过传感器融合、上下文和环境感知适应。