Rapid and Precise Online Surface Reconstruction Method for Digital Modeling of Bulk Material Flow

Abstract

Digital twins and visual monitoring of conveyor systems require accurate digital models of dynamic bulk material flows, but existing methods struggle to achieve both speed and precision. This study develops a rapid online method to reconstruct dynamic bulk material flows on conveyor belts. First, a standardized online reconstruction scheme using visual detection of material flow contour lines is presented. Then, a feature detection algorithm is proposed to extract more refined points from laser line skeleton to accelerate the reconstruction process. An iterative-filtering interpolation algorithm that generates smooth interframe point clouds is introduced to improve mesh quality. Experimental results demonstrate that our method outperforms traditional corner detection-based reconstruction techniques in feature point detection, accuracy, mesh quality, and runtime performance. This research provides a practical solution for material handling digitalization, promoting the advancement of conveyor system digital twins and potentially improving operational efficiency and predictive maintenance in bulk material handling industries.