An Adaptive Indoor Localization Approach Using WiFi RSSI Fingerprinting with SLAM-Enabled Robotic Platform and Deep Neural Networks

TL;DR

The paper addresses indoor localization by leveraging WiFi RSSI fingerprinting and a robot-assisted data-collection workflow that uses SLAM-derived maps to densely align WiFi scans with precise positions. A DTW-based alignment step pairs RSSI samples with robot trajectories, enabling the creation of a dense fingerprinting dataset that trains a Deep Neural Network to predict 2D positions from RSSI values for online localization. The approach eliminates the need for predefined maps, reduces data-collection time, and improves localization accuracy by increasing fingerprint density, demonstrated in an office environment with a 26% improvement over baselines. The work advances practical indoor localization by enabling scalable, adaptable deployments on mobile devices in dynamic indoor settings.

Abstract

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.

Figures (9)

• Figure 1: Classification of WiFi-based Indoor Localization
• Figure 2: Method Overview
• Figure 3: ROS2 topics and nodes interaction in the robot
• Figure 4: RVIZ ROS2 experiment monitoring during the test
• Figure 5: Fingerprinting Dataset Visualization