BIM-Constrained Optimization for Accurate Localization and Deviation Correction in Construction Monitoring (2504.17693v1)

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 withas-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.

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

Overview

The paper discusses an innovative approach to augmenting AR-based construction monitoring using a BIM-constrained drift correction method. The primary focus is realigning digital models with corresponding physical environments effectively, particularly on construction sites, through improved localization and deviation correction. The core premise of this research is to integrate BIM with real-time SLAM data to address typical challenges such as featureless surfaces and drift accumulation, thus improving the accuracy of AR visualizations over extended periods.

Methodology

The proposed system identifies and matches planar structures in the real world to those in BIM models, enabling more accurate visual alignment. The key components include:

1. Environment Mapping: Utilizing an RGB-D camera to detect and gather data related to planar structures within the construction environment.
2. BIM Data Processing: Extracting critical structural data from BIM to align with real-world planes detected by cameras.
3. Plane Matching and Transformation Estimation: Aligning and matching detected planes with architectural planes guided by a robust BIM-constrained methodology using Mahalanobis distance for matching precision. This stage is critical for achieving accurate alignment by computing a transformation matrix that minimizes both orientation and positional discrepancies.

A significant aspect of the discussed methodology is an optimization-driven strategy for drift correction aimed at maintaining alignment over time by dynamically updating the transformation between SLAM and BIM frames. This is achieved via a least-squares solution to optimize rotational and translational transformations relative to detected planes.

Experimental Evaluation

Motivated by the need to evaluate this method's efficacy in real-world scenarios, the researchers conducted experiments in four distinct environments. Evaluations focused on angular and distance error metrics provided comparative insight across manual, global, and local transformation strategies. The results highlighted a 52.24% reduction in angular deviations and 60.8% reduction in distance errors using their proposed local transformation approach, signifying a substantial enhancement over conventional methods.

The robust nature of the proposed alignment strategy was evident, especially under conditions typical of a construction site where dynamic changes and noise are prevalent. Furthermore, despite the lack of true trajectory ground truth, qualitative analyses reinforced the efficacy of the method in maintaining realistic visualization and alignment over time.

Implications and Future Directions

The integration of BIM data within AR applications presents practical advantages, notably in real-time construction monitoring, offering potential reductions in costly errors due to misalignment. Future research endeavors could enhance this work by integrating uncertainties for possible construction deviations and extending the framework beyond planar matching to incorporate additional construction elements and scenarios.

The proposed approach aligns well with the increasing demand for precision and efficiency in construction processes, hinting at broader applicability in various domains where real-world and digital world reconciliation are paramount. As the technology evolves, there is potential for further integration with other emerging technologies, thus enhancing the robustness and feasibility of AR applications in complex and dynamic environments. In summary, while the paper presents a focused technical advancement, the methodological groundwork laid offers a springboard for continued research and development in AI-driven localization and real-time visualization systems.