S-Graphs+: Real-Time Localization and Mapping Leveraging Hierarchical Representations

Bavle, Hriday ; Sanchez-Lopez, Jose Luis ; Shaheer, Muhammad ; Civera, Javier (Universidad de Zaragoza) ; Voos, Holger

Resumen: In this letter, we present an evolved version of Situational Graphs, which jointly models in a single optimizable factor graph (1) a pose graph, as a set of robot keyframes comprising associated measurements and robot poses, and (2) a 3D scene graph, as a high-level representation of the environment that encodes its different geometric elements with semantic attributes and the relational information between them. Specifically, our S-Graphs+ is a novel four-layered factor graph that includes: (1) A keyframes layer with robot pose estimates, (2) a walls layer representing wall surfaces, (3) a rooms layer encompassing sets of wall planes, and (4) a floors layer gathering the rooms within a given floor level. The above graph is optimized in real-time to obtain a robust and accurate estimate of the robot's pose and its map, simultaneously constructing and leveraging high-level information of the environment. To extract this high-level information, we present novel room and floor segmentation algorithms utilizing the mapped wall planes and free-space clusters. We tested S-Graphs+ on multiple datasets, including simulated and real data of indoor environments from varying construction sites, and on a real public dataset of several indoor office areas. On average over our datasets, S-Graphs+ outperforms the accuracy of the second-best method by a margin of 10.67%, while extending the robot situational awareness by a richer scene model. Moreover, we make the software available as a docker file.

Idioma: Inglés

DOI: 10.1109/LRA.2023.3290512

Año: 2023

Publicado en: IEEE Robotics and Automation Letters 8, 8 (2023), 4927-4934
ISSN: 2377-3766

Factor impacto JCR: 4.6 (2023)

Categ. JCR: ROBOTICS rank: 12 / 46 = 0.261 (2023) - Q2 - T1

Factor impacto CITESCORE: 9.6 - Computer Science Applications (Q1) - Control and Systems Engineering (Q1) - Artificial Intelligence (Q1) - Mechanical Engineering (Q1) - Biomedical Engineering (Q1) - Computer Vision and Pattern Recognition (Q1) - Human-Computer Interaction (Q1) - Control and Optimization (Q1)

Factor impacto SCIMAGO: 2.119 - 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/T45-17R
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2021-127685NB-I00

Tipo y forma: Artículo (Versión definitiva)

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