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.

翻译：本文提出了一种框架，通过将基于激光雷达的描述符与Wi-Fi指纹定位相结合，在预建图环境中解决自主移动机器人的全局定位难题。该研究源于复杂场景（如城市区域或地下矿井）对可靠定位日益增长的需求，这些场景需要能够克服传统全球导航卫星系统（GNSS）定位方法局限性的鲁棒系统。通过利用激光雷达和Wi-Fi传感器的互补优势生成定位预测，并将各预测的置信度评估作为潜在退化的指标，我们提出了一种基于冗余的方法来增强系统的整体鲁棒性和准确性。该框架允许激光雷达与Wi-Fi传感器独立运行，确保系统冗余。通过结合各预测结果并考虑其置信度水平，我们在定位任务中实现了增强且一致的性能表现。