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

2025-04-24

AI Summary

To address severe SLAM drift and long-term misalignment between BIM models and physical construction sites—caused by textureless, dynamically changing environments—this paper proposes a robust drift correction method leveraging BIM-derived geometric priors. The core innovation embeds BIM-defined planar structures as hard constraints into a nonlinear optimization framework, replacing pure kinematic tracking with architectural geometric consistency to achieve cross-domain alignment between the SLAM and BIM coordinate systems. The method integrates RGB-D SLAM, RANSAC-based planar segmentation, a robust variant of ICP for plane-to-plane matching, and Ceres Solver for constrained optimization. Experimental results demonstrate a 52.24% reduction in average angular deviation and a 60.8% decrease in wall-matching distance error, significantly improving AR visualization fidelity and long-term localization stability in construction environments.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Motion & Tracking

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Corrects drift in AR construction monitoring using BIM alignment
• Improves localization accuracy in featureless, dynamic construction sites
• Reduces errors between digital models and physical environments

Innovation

• BIM-aware drift correction for AR alignment
• Robust plane matching between real and BIM
• Optimization minimizes drift in noisy environments