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.

翻译：本文研究在同一环境中分别使用无线电、视觉和音频传感器进行室内定位的方法。评估基于最先进算法，并使用真实数据集。具体而言，我们评估了基于大规模MIMO技术的无线电定位机器学习算法、基于RGB-D摄像头的视觉定位ORB-SLAM3算法，以及基于麦克风阵列的音频定位SFS2算法。讨论包括定位精度、可靠性、标定要求以及潜在系统复杂度等方面，以分析使用不同传感器进行室内定位任务的优缺点。研究结果可为开发鲁棒且高精度的多传感器融合定位系统（例如通过传感器融合及上下文与环境感知自适应）提供指导与基础。