G-Loc: Tightly-coupled Graph Localization with Prior Topo-metric Information

TL;DR

This paper introduces G-Loc, a graph-based localization framework that leverages prior topological and metric information, integrating multiple sensors for robust, accurate, and versatile localization in complex environments.

Contribution

It reformulates map-based localization by reusing prior topological and metric data within a modular, sensor-agnostic graph framework, enhancing robustness and adaptability.

Findings

Accurate localization in real-world automotive scenarios

Effective integration of LiDAR, inertial, and GNSS data

Robust performance across diverse environmental conditions

Abstract

Localization in already mapped environments is a critical component in many robotics and automotive applications, where previously acquired information can be exploited along with sensor fusion to provide robust and accurate localization estimates. In this work, we offer a new perspective on map-based localization by reusing prior topological and metric information. Thus, we reformulate this long-studied problem to go beyond the mere use of metric maps. Our framework seamlessly integrates LiDAR, inertial and GNSS measurements, and cloud-to-map registrations in a sliding window graph fashion, which allows to accommodate the uncertainty of each observation. The modularity of our framework allows it to work with different sensor configurations (e.g., LiDAR resolutions, GNSS denial) and environmental conditions (e.g., mapless regions, large environments). We have conducted several validation experiments, including the deployment in a real-world automotive application, demonstrating the accuracy, efficiency, and versatility of our system in online localization.