# BIM-Constrained Optimization for Accurate Localization and Deviation Correction in Construction Monitoring

arXiv: https://arxiv.org/abs/2504.17693 · HTML: https://mlsanity.com/paper/2504.17693/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 10 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting](https://mlsanity.com/paper/2311.11700/) (2023)
- [Nothing Stands Still: A Spatiotemporal Benchmark on 3D Point Cloud Registration Under Large Geometric and Temporal Change](https://mlsanity.com/paper/2311.09346/) (2023)
- [IR-MCL: Implicit Representation-Based Online Global Localization](https://mlsanity.com/paper/2210.03113/) (2022)
- [RTG-SLAM: Real-time 3D Reconstruction at Scale using Gaussian Splatting](https://mlsanity.com/paper/2404.19706/) (2024)
- [Self-Improving Semantic Perception for Indoor Localisation](https://mlsanity.com/paper/2105.01595/) (2021)
- [BIM-SLAM: Integrating BIM Models in Multi-session SLAM for Lifelong Mapping using 3D LiDAR](https://mlsanity.com/paper/2408.15870/) (2024)
- [vS-Graphs: Tightly Coupling Visual SLAM and 3D Scene Graphs Exploiting Hierarchical Scene Understanding](https://mlsanity.com/paper/2503.01783/) (2025)
- [Supporting Construction and Architectural Visualization through BIM and AR/VR: A Systematic Literature Review](https://mlsanity.com/paper/2306.12274/) (2023)

## Abstract (arXiv)

Augmented reality (AR) applications for construction monitoring rely on real-time environmental tracking to visualize architectural elements. However, construction sites present significant challenges for traditional tracking methods due to featureless surfaces, dynamic changes, and drift accumulation, leading to misalignment between digital models and the physical world. This paper proposes a BIM-aware drift correction method to address these challenges. Instead of relying solely on SLAM-based localization, we align ``as-built" detected planes from the real-world environment with ``as-planned" architectural planes in BIM. Our method performs robust plane matching and computes a transformation (TF) between SLAM (S) and BIM (B) origin frames using optimization techniques, minimizing drift over time. By incorporating BIM as prior structural knowledge, we can achieve improved long-term localization and enhanced AR visualization accuracy in noisy construction environments. The method is evaluated through real-world experiments, showing significant reductions in drift-induced errors and optimized alignment consistency. On average, our system achieves a reduction of 52.24% in angular deviations and a reduction of 60.8% in the distance error of the matched walls compared to the initial manual alignment by the user.