用BIM模型纠正施工中AR定位漂移，提升可视化精度
BIM-Constrained Optimization for Accurate Localization and Deviation Correction in Construction Monitoring
- 将实际检测平面与BIM计划平面匹配，优化坐标系转换
- 相比手动对齐，角度偏差降低52.24%，墙面对齐距离误差减少60.8%
- 适合需要高精度现场可视化与偏差检测的智能建造场景
建筑监控中的增强现实（AR）应用依赖实时环境追踪来可视化建筑构件。然而，施工现场因表面缺乏特征、动态变化和累积漂移等问题，使传统追踪方法难以准确对齐数字模型与物理世界。本文提出一种基于建筑信息模型（BIM）的漂移校正方法：不依赖单一SLAM定位，而是将实测的“实际建造”平面与BIM中的“计划”平面进行匹配，通过优化算法计算出SLAM（S）与BIM（B）坐标系间的变换（TF），有效抑制长期漂移。利用BIM作为结构先验知识，显著提升复杂噪声环境下的定位精度与AR可视化效果。真实场景实验表明，系统平均使角度偏差减少52.24%，墙面对齐距离误差降低60.8%，相比初始人工对齐有显著改进。
原文摘要 · Abstract (English)
Augmented reality (AR) applications for construction monitoring rely on real-time environmental tracking to visualize architectural elements. However, construction sites present significant challenges for traditional tracking methods due to featureless surfaces, dynamic changes, and drift accumulation, leading to misalignment between digital models and the physical world. This paper proposes a BIM-aware drift correction method to address these challenges. Instead of relying solely on SLAM-based localization, we align ``as-built" detected planes from the real-world environment with ``as-planned" architectural planes in BIM. Our method performs robust plane matching and computes a transformation (TF) between SLAM (S) and BIM (B) origin frames using optimization techniques, minimizing drift over time. By incorporating BIM as prior structural knowledge, we can achieve improved long-term localization and enhanced AR visualization accuracy in noisy construction environments. The method is evaluated through real-world experiments, showing significant reductions in drift-induced errors and optimized alignment consistency. On average, our system achieves a reduction of 52.24% in angular deviations and a reduction of 60.8% in the distance error of the matched walls compared to the initial manual alignment by the user.
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