  This paper presents a framework addressing the challenge of global
localization in autonomous mobile robotics by integrating LiDAR-based
descriptors and Wi-Fi fingerprinting in a pre-mapped environment. This is
motivated by the increasing demand for reliable localization in complex
scenarios, such as urban areas or underground mines, requiring robust systems
able to overcome limitations faced by traditional Global Navigation Satellite
System (GNSS)-based localization methods. By leveraging the complementary
strengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluate
the confidence of each prediction as an indicator of potential degradation, we
propose a redundancy-based approach that enhances the system's overall
robustness and accuracy. The proposed framework allows independent operation of
the LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining the
predictions while considering their confidence levels, we achieve enhanced and
consistent performance in localization tasks.
