BIM Informed Visual SLAM for Construction Monitoring

2025-09-17

AI Summary

To address trajectory drift in visual SLAM caused by repetitive structures, occlusions, and low-texture regions in construction sites, this paper proposes a BIM-augmented RGB-D visual SLAM method. The approach explicitly incorporates geometric constraints from Building Information Modeling (BIM) wall structures as hard constraints into the graph optimization backend. It jointly leverages ORB feature matching, ICP-based point-cloud registration, and BIM model projection alignment to construct a BIM-enhanced pose-graph optimization framework. This work represents the first explicit integration of structural BIM priors into the visual SLAM backend via constrained optimization. As a result, localization robustness and mapping accuracy are significantly improved in unfinished and texture-deprived environments. Evaluated on real-world construction site data, the method reduces average trajectory error by 23.71% and map root-mean-square error by 7.14%, while maintaining real-time performance.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Aligning as-built construction state with as-planned BIM design
• Reducing visual SLAM drift in repetitive construction environments
• Enabling real-time construction monitoring using lightweight RGB-D sensors

Innovation

• Uses BIM as structural prior knowledge
• Establishes correspondences between walls and BIM
• Introduces constraints in back-end optimization