000164189 001__ 164189
000164189 005__ 20251127172930.0
000164189 0247_ $$2doi$$a10.1109/LRA.2025.3619744
000164189 0248_ $$2sideral$$a146367
000164189 037__ $$aART-2025-146367
000164189 041__ $$aeng
000164189 100__ $$aBavle, Hriday
000164189 245__ $$aS-Graphs 2.0 – A Hierarchical-Semantic Optimization and Loop Closure for SLAM
000164189 260__ $$c2025
000164189 5060_ $$aAccess copy available to the general public$$fUnrestricted
000164189 5203_ $$aThe hierarchical nature of 3D scene graphs aligns well with the structure of man-made environments, making them highly suitable for representation purposes. Beyond this, however, their embedded semantics and geometry could also be leveraged to improve the efficiency of map and pose optimization, an opportunity that has been largely overlooked by existing methods. We introduce Situational Graphs 2.0 (S-Graphs 2.0), that effectively uses the hierarchical structure of indoor scenes for efficient data management and optimization. Our approach builds a four-layer situational graph comprising Keyframes, Walls, Rooms, and Floors. Our first contribution lies in the front-end, which includes a floor detection module capable of identifying stairways and assigning floor-level semantic relations to the underlying layers (Keyframes, Walls, and Rooms). Floor-level semantics allows us to propose a floor-based loop closure strategy, that effectively rejects false positive closures that typically appear due to aliasing between different floors of a building. Our second novelty lies in leveraging our representation hierarchy in the optimization. Our proposal consists of: (1) local optimization over a window of recent keyframes and their connected components across the four representation layers, (2) floor-level global optimization, which focuses only on keyframes and their connections within the current floor during loop closures, and (3) room-level local optimization, marginalizing redundant keyframes that share observations within the room, which reduces the computational footprint. We validate our algorithm extensively in different real multi-floor environments. Our approach shows state-of-the-art accuracy metrics in large-scale multi-floor environments, estimating hierarchical representations up to 10× faster, in average, than competing baselines.
000164189 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000164189 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000164189 700__ $$aSanchez-Lopez, Jose Luis
000164189 700__ $$aShaheer, Muhammad
000164189 700__ $$0(orcid)0000-0003-1368-1151$$aCivera, Javier$$uUniversidad de Zaragoza
000164189 700__ $$aVoos, Holger
000164189 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000164189 773__ $$g10, 12 (2025), 12461-12468$$pIEEE Robot. autom. let.$$tIEEE Robotics and Automation Letters$$x2377-3766
000164189 8564_ $$s2045392$$uhttps://zaguan.unizar.es/record/164189/files/texto_completo.pdf$$yVersión publicada
000164189 8564_ $$s3823297$$uhttps://zaguan.unizar.es/record/164189/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000164189 909CO $$ooai:zaguan.unizar.es:164189$$particulos$$pdriver
000164189 951__ $$a2025-11-27-15:16:45
000164189 980__ $$aARTICLE