BIM-Loc: BIM-integrated discrepancy-aware LiDAR-based indoor localization

2026-07-06

PRE

Yinqiang Zhang

Lu Liang

Yipeng Pan

Maolin Lei

Yuhan Xie

Zhanteng Xie

Xiaowei Luo

Jia Pan

DOI：10.1177/02783649261462593

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Accurate and robust localization is a fundamental requirement for service and inspection robots, particularly in feature-sparse indoor environments where traditional systems struggle due to a lack of distinct landmarks. While prior maps can enhance robustness, precise and compact maps capturing real-world details are often unavailable for new or frequently changing environments. This paper presents BIM-Loc, a novel discrepancy-aware LiDAR-based localization method that directly integrates Building Information Models (BIM) from the design phase. BIM-Loc simultaneously estimates trajectories aligned with the BIM coordinate system and identifies discrepancies between real-world observations and the as-designed BIM in an online fashion. Our core contributions include: (1) a novel multi-hit ray casting strategy for efficient BIM-point data association and projection of 3D observations into 2D texture space; (2) a pose graph optimization framework with BIM-integrated factors that enforces consistency among odometry, sequential scans, and BIM structures; and (3) a hierarchical Bayesian inference module that incrementally updates a continuous 2D surface representation for discrepancy detection, propagating updates from the pixel to the structure level. Extensive evaluations in both simulation and real-world applications demonstrate that BIM-Loc significantly outperforms state-of-the-art map-based methods in localization accuracy and robustness. More experimental results are available at our project website: https://bim-loc.github.io/bim-loc .

BIM-Loc: BIM-integrated discrepancy-aware LiDAR-based indoor localization

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