  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.
