BIM-Loc: BIM統合型の不一致認識LiDAR屋内位置推定
BIM-Loc: BIM-Integrated Discrepancy-Aware LiDAR-based Indoor Localization
設計段階のBIM（建物情報モデル）を直接利用し、特徴が乏しい屋内環境でも高精度・高ロバストな位置推定を実現する手法を提案。実環境とBIMの不一致もオンラインで検出する。
詳しい要約
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著者: Yinqiang Zhang, Liang Lu, Yipeng Pan, Maolin Lei, Yuhan Xie, Zhanteng Xie, Xiaowei Luo, Jia Pan
分類: cs.RO
原文アブストラクト
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.
