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.

中文翻译：

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具有物理限制的经济型挖掘机姿态重建

挖掘机的安全性和效率对于施工进度至关重要。监控他们的 3D 姿势至关重要，但传统方法的资源和准确性问题往往会阻碍他们。本文提出了一种在考虑物理约束的情况下，使用经济高效的单目相机重建挖掘机的 3D 姿态的方法。该方法包括两个步骤：深度学习以识别 2D 关键点，然后使用挖掘机运动学模型、坐标变换和相机投影关系通过优化重建 3D 姿势。实验结果表明，该方法的平均关节位置误差为 428.58 mm，平均圆柱体长度误差为 5.12%，优于其他方法。这种方法可以经济高效地用于建筑工地挖掘机的安全监控和生产力管理。