Digital twin-driven pose estimation and trajectory prediction for modular integrated construction on-site stacking via spatio-temporal feature fusion modeling

High stacking precision is essential to prevent structural collisions, reduce safety accidents, and enhance project efficiency in modular integrated construction. However, current stacking precision is constrained by delayed hoisting control, limited perception, and fragmented information sharing. Therefore, this paper proposes a digital twin-driven pose estimation and trajectory prediction system via spatio-temporal feature fusion modeling, leveraging real-time information from high-fidelity digital twins. First, physical entities are transformed into smart stacking objects that combine static inherent properties with real-time dynamic activities captured by Ultra-Wideband sensors and Inertial Measurement Units. Then, a digital twin of stacking operations is used to interact with their physical counterparts in real time, enabling seamless mapping and visualization of the stacking process. Third, the PosTraFormer network is proposed for 6-dimensional module pose estimation and trajectory prediction based on spatio-temporal fusion modeling of trajectory point relations, to provide stacking guidance and improve installation precision. Both visualization and quantitative analysis from comparative experiments and case studies demonstrate the significant advantages of the proposed approach.