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.

翻译：精确且鲁棒的定位是服务与巡检机器人的基本需求，尤其在缺乏显著特征点的稀疏室内环境中，传统方法因缺少明确地标而难以应对。虽然先验地图可增强鲁棒性，但针对新场景或频繁变化环境，能够捕捉真实世界细节的精确紧凑地图往往难以获取。本文提出BIM-Loc——一种新颖的差异感知激光雷达定位方法，该方法直接集成设计阶段的建筑信息模型（BIM）。BIM-Loc能同时估计与BIM坐标系对齐的轨迹，并以在线方式识别真实环境与设计BIM之间的差异。我们的核心贡献包括：(1) 一种新型多命中光线投射策略，用于高效实现BIM与点云数据关联及三维观测至二维纹理空间的投影；(2) 集成BIM因子的位姿图优化框架，确保里程计、连续扫描与BIM结构间的一致性；(3) 分层贝叶斯推理模块，通过增量更新连续二维表面表征实现差异检测，并将更新从像素级传递至结构级。在仿真与真实环境中的大量实验表明，BIM-Loc在定位精度与鲁棒性上显著优于现有基于地图的先进方法。