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arxiv: 2509.13972 · v3 · pith:KKPVVLXGnew · submitted 2025-09-17 · 💻 cs.RO

BIM Informed Visual SLAM for Construction Environments

classification 💻 cs.RO
keywords constructionslamrealas-builtas-plannedbuildingdriftenvironment
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Passage-Aware Structural Mapping for RGB-D Visual SLAM

    cs.RO 2026-04 unverdicted novelty 6.0

    A passage-aware structural mapping approach for RGB-D VSLAM detects doors and openings via joint geometric-semantic-topological fusion and adds passage abstractions to vS-Graphs scene graphs.