Segmentation method for enhanced features in automatic registration of triangular mesh model of mechanical parts
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摘要:
三角网格模型配准是工业自动化检测软件中的重要一环，其配准精度对检测机械零件的形位公差有重要影响。针对三角网格模型的自动配准精度低、鲁棒性差的问题，本文提出一种面向机械零件三角网格模型自动配准中增强特征的分割方法。首先，确定三角网格模型特征分割的K值，通过拉普拉斯矩阵确定种子点进行迭代初始化。其次，本文采用合适的区域形状代理和代价函数以加速该过程，并通过多源迭代聚类得到特征分割结果。最终，在三角网格模型特征分割结果的基础上进行基于奇异值分解法的粗配准，之后再根据EM-ICP进行精配准。与传统的特征描述子粗配准结合ICP精配准的方法进行对比，结果表明，本文方法的配准误差下降了25.2%，自动配准时间缩短了62.6%，有效地提高了三角网格模型自动配准的精度和效率。
Abstract:Triangular mesh model registration is an important part of industrial automation detection software. The registration accuracy has an important influence on mechanical parts' shape and position tolerance. Aiming to solve the problems of low accuracy and poor robustness of the automatic registration of triangular mesh models, we propose a segmentation method for enhanced features in the automatic registration of triangular mesh models for mechanical parts. First, the K value of the feature segmentation of the triangular mesh model was determined, and the Laplacian matrix determined the seed points for iterative initialization. Second, the appropriate region shape agent and cost function were used to accelerate the process and perform multi-source iterative clustering to obtain the intended feature segmentation results. Finally, based on the feature segmentation results of the triangular mesh model, the coarse registration based on the singular value decomposition method was performed, then the fine registration was performed according to the EM-ICP. The experimental results show that the proposed method reduces registration error by 25.2% and shortens the automatic registration time by 62.6%, compared with the traditional feature descriptor coarse registration combined with ICP fine registration method. This effectively improves the accuracy and efficiency of the automatic registration of the triangular mesh model.
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图 3 本文所用三角网络模型的(a)局部三角网格和(b)抽象图结构
Figure 3. (a) Local triangular mesh and (b) abstract graph structur used in this paper
图 5 (a)三角面片点与投影点和(b)点至代理平面距离示意图
Figure 5. Schematic diagrams of (a) distance from triangular patch points to projection points and (b) distance from point to agent plane
图 6 边界平滑(a)前、(b)后示意图
Figure 6. Schematic diagrams (a) before and (b) after boundary smoothing
图 9 (a)源三角网格模型和(b)通过边折叠等简化算法得到的低分辨率三角网格模型
Figure 9. (a) Source triangular mesh model and (b) the low-resolution triangular mesh model obtained through simplification algorithms such as edge collapsing
表 1 自动配准RMSE和耗时结果
Table 1. RMSE and time-consuming results of automatic registration
模型
序号模型面片
数量特征描述子方法
RMSE（mm）本文方法
RMSE（mm）特征描述
子方法耗时/s本文方法
耗时/s1 7401766 0.695 0.249 83.43 29.76 2 34946 0.002 0.002 6.73 0.13 3 14764242 0.387 0.386 197.46 50.48 4 29686649 \ 0.296 \ 113.42 5 13262362 0.473 0.473 208.32 47.32 6 1920009 0.254 0.253 26.54 7.75 7 1794486 0.086 0.085 30.66 8.47 8 4418964 0.858 0.375 50.37 15.93 9 5081628 0.572 0.572 63.20 18.62 10 13136277 0.418 0.418 147.81 46.68 均值 9150133 0.416 0.311 90.50 33.86 
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