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Several studies rely on the de facto standard Adaptive Monte CarloLocalization (AMCL) method to localize a robot in an Occupancy Grid Map (OGM)extracted from a building information model (BIM model). However, most of thesestudies assume that the BIM model precisely represents the real world, which israrely true. Discrepancies between the reference BIM model and the real world(Scan-BIM deviations) are not only due to furniture or clutter but also theusual as-planned and as-built deviations that exist with any model created inthe design phase. These deviations affect the accuracy of AMCL drastically.This paper proposes an open-source method to generate appropriate PoseGraph-based maps from BIM models for robust 2D-LiDAR localization in changingand dynamic environments. First, 2D OGMs are automatically generated fromcomplex BIM models. These OGMs only represent structural elements allowingindoor autonomous robot navigation. Then, an efficient technique converts these2D OGMs into Pose Graph-based maps enabling more accurate robot pose tracking.Finally, we leverage the different map representations for accurate, robustlocalization with a combination of state-of-the-art algorithms. Moreover, weprovide a quantitative comparison of various state-of-the-art localizationalgorithms in three simulated scenarios with varying levels of Scan-BIMdeviations and dynamic agents. More precisely, we compare two Particle Filter(PF) algorithms: AMCL and General Monte Carlo Localization (GMCL); and twoGraph-based Localization (GBL) methods: Google's Cartographer and SLAM Toolbox,solving the global localization and pose tracking problems. The numerousexperiments demonstrate that the proposed method contributes to a robustlocalization with an as-designed BIM model or a sparse OGM in changing anddynamic environments, outperforming the conventional AMCL in accuracy androbustness.

crc press ebooks

Occupancy Grid Map to Pose Graph-based Map: Robust BIM-based 2D-LiDAR  Localization for Lifelong Indoor Navigation in Changing and Dynamic  Environments