Real-time Localization and Mapping in Architectural Plans with Deviations
<|reference_start|>Real-time Localization and Mapping in Architectural Plans with Deviations: Having prior knowledge of an environment boosts the localization and mapping accuracy of robots. Several approaches in the literature have utilized architectural plans in this regard. However, almost all of them overlook the deviations between actual as-built environments and as-planned architectural designs, introducing bias in the estimations. To address this issue, we present a novel localization and mapping method denoted as deviations-informed Situational Graphs or diS-Graphs that integrates prior knowledge from architectural plans even in the presence of deviations. It is based on Situational Graphs (S-Graphs) that merge geometric models of the environment with 3D scene graphs into a multi-layered jointly optimizable factor graph. Our diS-Graph extracts information from architectural plans by first modeling them as a hierarchical factor graph, which we will call an Architectural Graph (A-Graph). While the robot explores the real environment, it estimates an S-Graph from its onboard sensors. We then use a novel matching algorithm to register the A-Graph and S-Graph in the same reference, and merge both of them with an explicit model of deviations. Finally, an alternating graph optimization strategy allows simultaneous global localization and mapping, as well as deviation estimation between both the A-Graph and the S-Graph. We perform several experiments in simulated and real datasets in the presence of deviations. On average, our diS-Graphs outperforms the baselines by a margin of approximately 43% in simulated environments and by 7% in real environments, while being able to estimate deviations up to 35 cm and 15 degrees.<|reference_end|>
@article{shaheer2024real-time,
    title={Real-time Localization and Mapping in Architectural Plans with
  Deviations},
    author={Muhammad Shaheer, Jose Andres Millan-Romera, Hriday Bavle, Marco
  Giberna, Jose Luis Sanchez-Lopez, Javier Civera and Holger Voos},
    journal={arXiv preprint arXiv:2408.01737},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01737},
    primaryClass={cs.RO}
}