{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/s-graphs-real-time-localization-and-mapping","title":"S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations","arxiv_id":"2212.11770","date":"2022-12-22","proceeding":null,"authors":["Hriday Bavle","Jose Luis Sanchez-Lopez","Muhammad Shaheer","Javier Civera","Holger Voos"],"abstract":"In this paper, 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 robots 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.","url_abs":"https://arxiv.org/abs/2212.11770v3","url_pdf":"https://arxiv.org/pdf/2212.11770v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"s-graphs-real-time-localization-and-mapping","repo_url":"https://github.com/snt-arg/s_graphs_docker","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"s-graphs-real-time-localization-and-mapping","repo_url":"https://github.com/snt-arg/lidar_situational_graphs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2212.11770","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}