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

TL;DR

This paper introduces a redundant LiDAR-Wi-Fi integration framework for autonomous robots that improves global localization robustness and accuracy by leveraging sensor complementarity and confidence evaluation.

Contribution

It proposes a novel redundancy-based approach enabling independent operation of LiDAR and Wi-Fi sensors for more reliable localization in complex environments.

Findings

Enhanced localization accuracy in urban and underground scenarios

Robustness against sensor degradation and failures

Improved confidence assessment for sensor predictions

Abstract

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.