Reference : S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations
E-prints/Working papers : Already available on another site
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/54383
S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations
English
Bavle, Hriday mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation >]
Sanchez Lopez, Jose Luis mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation >]
Shaheer, Muhammad mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation >]
Civera, Javier []
Voos, Holger mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation > ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit]
Dec-2022
1
8
No
[en] SLAM ; Localization
[en] In this paper, we present an evolved version of the Situational Graphs, which jointly models in a single optimizable factor graph, a SLAM graph, as a set of robot keyframes, containing its associated measurements and robot poses, and 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 those elements. Our proposed 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 the high-level information of the environment. To extract such 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, simulations of distinct indoor environments, on real datasets captured over several construction sites and office environments, and on a real public dataset of indoor office environments. S-Graphs+ outperforms relevant baselines in the majority of the datasets while extending the robot situational awareness by a four-layered scene model. Moreover, we make the algorithm available as a docker file.
http://hdl.handle.net/10993/54383
https://arxiv.org/abs/2212.11770

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