In the robotic eye-in-hand measurement system, a hand-eye calibration method is essential. From the perspective of 3D reconstruction, this paper first analyzes the influence of the line laser sensor hand-eye calibration error on the 3D reconstructed point clouds error. Based on this, considering the influence of line laser sensor measurement errors and the need for high efficiency and convenience in robotic manufacturing systems, this paper proposes a 3D reconstruction-based robot line laser hand-eye calibration method. In this method, combined with the point cloud registration technique, the newly defined error-index more intuitively reflects the calibration result than traditional methods. To raise the performance of the calibration algorithm, a Particle Swarm Optimization - Gaussian Process (PSO-GP) method is adopted to improve the efficiency of the calibration. The experiments show that the Root Mean Square Error (RMSE) of the reconstructed point cloud can reach 0.1256 mm when using the proposed method, and the reprojection error is superior to those using traditional hand-eye calibration methods.

在机器人的手眼测量系统中，手眼校准方法必不可少。从3D重建的角度出发，本文首先分析了线激光传感器手眼校准误差对3D重建的点云误差的影响。在此基础上，考虑线型激光传感器测量误差的影响以及机器人制造系统对高效，便捷的需求，提出了一种基于3D重建的机器人线激光手眼标定方法。在这种方法中，与点云配准技术相结合，新定义的误差指标比传统方法更直观地反映了校准结果。为了提高校准算法的性能，采用粒子群优化-高斯过程（PSO-GP）方法提高了标定效率。实验表明，采用该方法重建点云的均方根误差（RMSE）可达0.1256 mm，重投影误差优于传统手眼校正方法。