Indoor localization plays a vital role in the era of the IoT and robotics, with WiFi technology being a prominent choice due to its ubiquity. We present a method for creating WiFi fingerprinting datasets to enhance indoor localization systems and address the gap in WiFi fingerprinting dataset creation. We used the Simultaneous Localization And Mapping (SLAM) algorithm and employed a robotic platform to construct precise maps and localize robots in indoor environments. We developed software applications to facilitate data acquisition, fingerprinting dataset collection, and accurate ground truth map building. Subsequently, we aligned the spatial information generated via the SLAM with the WiFi scans to create a comprehensive WiFi fingerprinting dataset. The created dataset was used to train a deep neural network (DNN) for indoor localization, which can prove the usefulness of grid density. We conducted experimental validation within our office environment to demonstrate the proposed method's effectiveness, including a heatmap from the dataset showcasing the spatial distribution of WiFi signal strengths for the testing access points placed within the environment. Notably, our method offers distinct advantages over existing approaches as it eliminates the need for a predefined map of the environment, requires no preparatory steps, lessens human intervention, creates a denser fingerprinting dataset, and reduces the WiFi fingerprinting dataset creation time. Our method achieves 26% more accurate localization than the other methods and can create a six times denser fingerprinting dataset in one-third of the time compared to the traditional method. In summary, using WiFi RSSI Fingerprinting data surveyed by the SLAM-Enabled Robotic Platform, we can adapt our trained DNN model to indoor localization in any dynamic environment and enhance its scalability and applicability in real-world scenarios.

翻译：室内定位在物联网与机器人时代发挥着至关重要的作用，其中WiFi技术因其普及性成为主流选择。本文提出一种创建WiFi指纹数据集的方法，旨在提升室内定位系统性能并弥补现有WiFi指纹数据集构建的不足。我们采用同步定位与建图（SLAM）算法，并利用机器人平台在室内环境中构建精确地图并实现机器人定位。开发了专用软件应用程序以支持数据采集、指纹数据集收集及高精度基准地图构建。随后，通过将SLAM生成的空间信息与WiFi扫描数据对齐，构建了完整的WiFi指纹数据集。利用该数据集训练了用于室内定位的深度神经网络（DNN），验证了网格密度对定位效果的提升作用。我们在办公室环境中进行了实验验证，证明了所提方法的有效性，包括通过数据集热图展示了测试接入点在环境中的WiFi信号强度空间分布。值得注意的是，相较于现有方法，本方法具有显著优势：无需预先定义环境地图、无需准备步骤、减少人工干预、生成更密集的指纹数据集，并大幅缩短WiFi指纹数据集构建时间。实验表明，本方法定位精度较其他方法提升26%，且相较于传统方法，仅需三分之一时间即可生成六倍密度的指纹数据集。综上所述，通过采用基于SLAM的机器人平台采集的WiFi RSSI指纹数据，我们能够使训练的DNN模型适应任何动态环境中的室内定位任务，从而增强其在真实场景中的可扩展性与适用性。