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This paper presents a framework addressing the challenge of globallocalization in autonomous mobile robotics by integrating LiDAR-baseddescriptors and Wi-Fi fingerprinting in a pre-mapped environment. This ismotivated by the increasing demand for reliable localization in complexscenarios, such as urban areas or underground mines, requiring robust systemsable to overcome limitations faced by traditional Global Navigation SatelliteSystem (GNSS)-based localization methods. By leveraging the complementarystrengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluatethe confidence of each prediction as an indicator of potential degradation, wepropose a redundancy-based approach that enhances the system's overallrobustness and accuracy. The proposed framework allows independent operation ofthe LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining thepredictions while considering their confidence levels, we achieve enhanced andconsistent performance in localization tasks.

Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Robust Global Localization in Autonomous Mobile Robotics