Vision-based assembly target pose estimation and adaptation for robotic assembly in Modular Integrated Construction
Year: 2026
Type: article
Abstract: Modular Integrated Construction (MiC) accelerates building processes, yet the positioning and alignment of MiC modules on-site remain hazardous and labor-intensive. This paper formulates the robotic perception task for automated MiC module assembly as the pose estimation of assembly targets, which are categorized as either convex or concave structures. A coarse-to-fine pose estimation (C2FPE) method is proposed, where targets are detected and segmented from RGB images for coarse localization, followed by fine-grained pose estimation using RGB-D data. To address the domain gap between laboratory and construction-site environments, a training-free, multi-modal pose adaptation (T2MPA) method is introduced, enabling a pre-trained detection model to be transferred to real-world construction scenarios without manual annotation. Experimental results show that C2FPE achieves Average Distance of model points (ADD) scores of 84.25% and 98.53% on convex and concave targets, respectively, outperforming state-of-the-art methods. T2MPA achieves [email protected] scores of 74.19% and 96.00% for convex and concave targets, exceeding existing domain adaptation methods.
Source: Automation in Construction
Authors: Yuanyang Qi, Jie Hong, Tingtian Li, Chen Qian, Xiao Li, Geoffrey Qiping Shen
Institution: Hong Kong Polytechnic University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,490
OpenAlex ID: W7213763970