Simultaneous Localization and Mapping (SLAM) is a key tool for monitoring construction sites, where aligning the evolving as-built state with the as-planned design enables early error detection and reduces costly rework. LiDAR-based SLAM achieves high geometric precision, but its sensors are typically large and power-demanding, limiting their use on portable platforms. Visual SLAM offers a practical alternative with lightweight cameras already embedded in most mobile devices. however, visually mapping construction environments remains challenging: repetitive layouts, occlusions, and incomplete or low-texture structures often cause drift in the trajectory map. To mitigate this, we propose an RGB-D SLAM system that incorporates the Building Information Model (BIM) as structural prior knowledge. Instead of relying solely on visual cues, our system continuously establishes correspondences between detected wall and their BIM counterparts, which are then introduced as constraints in the back-end optimization. The proposed method operates in real time and has been validated on real construction sites, reducing trajectory error by an average of 23.71% and map RMSE by 7.14% compared to visual SLAM baselines. These results demonstrate that BIM constraints enable reliable alignment of the digital plan with the as-built scene, even under partially constructed conditions.

翻译：同步定位与建图（SLAM）是施工现场监测的关键工具，通过将动态变化的竣工状态与预设设计方案进行对齐，能够实现早期误差检测并减少昂贵的返工。基于激光雷达的SLAM虽能实现高几何精度，但其传感器通常体积庞大且功耗较高，限制了在便携式平台上的应用。视觉SLAM则提供了实用的替代方案，其轻量级摄像头已内置于大多数移动设备中。然而，对施工环境进行视觉建图仍面临挑战：重复的布局、遮挡以及不完整或低纹理结构常导致轨迹地图的漂移。为缓解此问题，我们提出一种融合建筑信息模型（BIM）作为结构先验知识的RGB-D SLAM系统。该系统不仅依赖视觉线索，还持续建立检测到的墙体与其BIM对应物之间的关联，并将这些关联作为约束条件引入后端优化。所提方法可实时运行，并在真实施工现场得到验证，相较于视觉SLAM基线方法，其轨迹误差平均降低23.71%，地图均方根误差降低7.14%。这些结果表明，即使在部分施工条件下，BIM约束仍能实现数字方案与竣工场景的可靠对齐。