Abstract
Three-dimensional information acquisition is crucial for the intelligent control and safe operation of bulk material transportation systems. However, existing visual measurement methods face challenges, including difficult stereo matching due to indistinct surface features, error accumulation in multi-camera calibration, and unreliable depth information fusion. This paper proposes a three-dimensional reconstruction method based on multi-camera hierarchical calibration. The method establishes a measurement framework centered on a core camera, enhances material surface features through speckle structured light projection, and implements a 'monocular-binocular-multi-camera association' calibration strategy with global optimization to reduce error accumulation. Additionally, a depth information fusion algorithm based on multi-epipolar geometric constraints improves reconstruction completeness through multi-view information integration. Experimental results demonstrate excellent precision with absolute errors within 1 mm for features as small as 15 mm and relative errors between 0.02% and 2.54%. Compared with existing methods, the proposed approach shows advantages in point cloud completeness, reconstruction accuracy, and environmental adaptability, providing reliable technical support for intelligent monitoring of bulk material transportation systems.