Dimension-aware six degrees of freedom pose estimation for robotic rebar tying in prefabricated construction

Despite significant progress in automation within prefabricated construction, rebar tying remains a labor-intensive task, contributing to production delays and ergonomic risks. Although robotic automation offers potential solutions, existing six degrees of freedom (6-DoF) tying pose estimation methods often struggle with variations in rebar dimensions, which can lead to tying inaccuracies and necessitate rework due to structural non-compliance. To address this problem, a dimension-aware 6-DoF tying pose estimation framework (DimenPose) is proposed in this study. DimenPose integrates rebar dimension measurement into the pose estimation process. First, a coarse-to-fine point cloud reconstruction method is developed to integrate multiple viewpoints to establish a comprehensive three-dimensional (3D) representation. Next, a bidirectional density anomaly detection-based automatic rebar segmentation approach is designed for geometric information extraction from dense and repetitive rebar arrangements. Finally, a geometric analysis approach determines dimensional parameters and tying poses based on spatial and geometric information. By enabling dimension-aware robotic perception through point cloud processing, DimenPose offers a perception basis for improving pose estimation accuracy and supporting dimensional compliance assessment in robotic rebar tying, while its generalizability to broader industrial scenarios requires further validation.