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一种主流选择。本文提出了一种创建WiFi指纹数据集的方法，以增强室内定位系统并弥补现有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。我们开发了软件应用程序以促进数据采集、指纹数据集收集及精确地面实况地图构建。随后，我们将通过SLAM生成的空间信息与WiFi扫描数据对齐，从而创建了一个全面的WiFi指纹数据集。所构建的数据集用于训练深度神经网络（DNN）进行室内定位，这验证了网格密度的有效性。我们在办公室环境中进行了实验验证，以证明所提方法的有效性，包括展示环境中测试接入点的WiFi信号强度空间分布的数据集热力图。值得注意的是，相较于现有方法，我们的方法具有显著优势：无需预定义环境地图、无需准备步骤、减少人工干预、创建更密集的指纹数据集，并缩短了WiFi指纹数据集的创建时间。与传统方法相比，我们的方法定位精度提高26%，且仅需三分之一的时间即可创建密度提升六倍的指纹数据集。总之，利用基于SLAM的机器人平台采集的WiFi RSSI指纹数据，我们可以将训练好的DNN模型自适应应用于任何动态环境中的室内定位，并提升其在真实场景中的可扩展性与适用性。