G-Loc: tightly-coupled graph localization with prior topo-metric information

Montano-Oliván, Lorenzo ; Placed, Julio A. (Universidad de Zaragoza) ; Montano, Luis (Universidad de Zaragoza) ; Lázaro, María T.

Resumen: 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 letter, 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.

Idioma: Inglés

DOI: 10.1109/LRA.2024.3457383

Año: 2024

Publicado en: IEEE Robotics and Automation Letters 9, 11 (2024), 9167-9174
ISSN: 2377-3766

Factor impacto JCR: 5.3 (2024)

Categ. JCR: ROBOTICS rank: 12 / 48 = 0.25 (2024) - Q1 - T1

Factor impacto SCIMAGO: 1.481 - Artificial Intelligence (Q1) - Biomedical Engineering (Q1) - Computer Science Applications (Q1) - Mechanical Engineering (Q1) - Control and Optimization (Q1) - Control and Systems Engineering (Q1) - Human-Computer Interaction (Q1) - Computer Vision and Pattern Recognition (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FSE/T73_23R

Tipo y forma: Artículo (PostPrint)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)