Cost‐effective excavator pose reconstruction with physical constraints

Abstract

Excavator safety and efficiency are crucial for construction progress. Monitoring their 3D poses is vital but often hampered by resource and accuracy issues with traditional methods. This paper presents a method to reconstruct the 3D poses of excavators using a cost‐effective monocular camera while considering physical constraints. The approach involves two steps: deep learning to identify 2D key points, followed by using excavator kinematic models, coordinate transformation, and camera projection relationships to reconstruct 3D poses with optimization. Experimental results show the method achieves a mean joint position error of 428.58 mm and a mean cylinder length error of 5.12%, outperforming alternative methods. This method can be employed cost‐effectively for safety monitoring and productivity management of excavators on construction sites.