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This research introduces BIM-informed Visual SLAM to mitigate trajectory drift in construction environments by integrating structural priors into real-time mapping systems. The approach significantly improves geometric accuracy and global consistency, offering a practical advancement for robotics applications within the built environment sector. While the method addresses specific spatial challenges, its core contribution remains firmly rooted in machine learning and computer vision techniques rather than general construction management or consumer hardware.
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BIM Informed Visual SLAM for Construction Environments

arXiv2 min read

arXiv:2509.13972v4 Announce Type: replace Abstract: Monitoring building construction sites requires comparing the as-planned design with the as-built state, which can be estimated in real time using Simultaneous Localization and Mapping (SLAM) techniques. However, visual SLAM is prone to trajectory drift in construction environments, producing maps that are geometrically inaccurate with the actual environment. To address this limitation, we augment an existing RGB-D SLAM system with structural priors derived from the Building Information Model (BIM). The system associates detected walls with their BIM counterparts and includes these correspondences as geometric constraints in the back-end optimization, reducing drift and enhancing global consistency. The proposed method operates in real time and is validated on multiple real construction sites, achieving an average trajectory error reduction of 25.23% and a 7.14% improvement in map accuracy over state-of-the-art baselines. Robustness analyses further demonstrate resilience to incomplete BIM data and geometric discrepancies between as-planned models and the as-built environment.

Abstract:Monitoring building construction sites requires comparing the as-planned design with the as-built state, which can be estimated in real time using Simultaneous Localization and Mapping (SLAM) techniques. However, visual SLAM is prone to trajectory drift in construction environments, producing maps that are geometrically inaccurate with the actual environment. To address this limitation, we augment an existing RGB-D SLAM system with structural priors derived from the Building Information Model (BIM). The system associates detected walls with their BIM counterparts and includes these correspondences as geometric constraints in the back-end optimization, reducing drift and enhancing global consistency. The proposed method operates in real time and is validated on multiple real construction sites, achieving an average trajectory error reduction of 25.23% and a 7.14% improvement in map accuracy over state-of-the-art baselines. Robustness analyses further demonstrate resilience to incomplete BIM data and geometric discrepancies between as-planned models and the as-built environment.

Robotics (cs.RO)

arXiv:2509.13972 [cs.RO]

(or arXiv:2509.13972v4 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2509.13972 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 238 words

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