BIM Informed Visual SLAM for Construction Monitoring

TL;DR

The paper tackles drift in visual SLAM for construction monitoring by integrating BIM priors into a real-time RGB-D SLAM system. It introduces wall-based associations between as-built observations and BIM, which are encoded as BIM-to-SLAM constraints in a back-end factor graph, while walls act as stable anchors for alignment. The approach, built on the vS-Graphs backbone, achieves a mean ATE improvement of $23.71\%$ and RMSE improvement of $7.14\%$ over visual SLAM baselines across real-world office and construction-site sequences, and maintains real-time operation (approximately $23.3$ FPS) even with BIM constraints. The results demonstrate robust alignment of evolving as-built maps with the as-planned BIM under partially built conditions, enabling reliable progress monitoring and AR applications in construction settings.

Abstract

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.

Figures (4)

• Figure 1: Visualization of the proposed BIM-informed RGB-D SLAM system. The figure shows the as-planned BIM and the reconstructed as-built map, with detected walls matched to their BIM counterparts. A 2D projection illustrates the correct alignment between the SLAM map and the BIM layout.
• Figure 2: System architecture of ivS-Graphs. The pipeline takes BIM and RGB-D camera data as inputs. The SLAM backbone front-end processes visual data into keyframes, map points, and wall segments. Our contributions are highlighted in green: (1) initial alignment followed by a (2) continuous wall association and (3) the integration of BIM in the back-end of the system. These establish BIM-to-SLAM correspondences ($W_A \leftrightarrow W_S$) that are introduced as constraints into the back-end graph. The resulting optimized map (right) aligns the evolving as-built structure with the as-planned BIM.
• Figure 3: Qualitative comparison of estimated trajectories on the office1-1 sequence. Left: proposed method with BIM constraints; right: baseline without BIM constraints, just initial alignment integrated.
• Figure 4: Accumulated trajectory drift error over time in office1-2 sequence, showing by the ATE for baselines and our ivs-Graphs approach except BAD SLAM due to tracking failures.