面向铸件自动化打磨的点云精简方法

由于铸件自动化打磨作业中存在点云数据过冗余等问题,导致数据处理时间过长,影响加工效率。为了提高点云处理的计算效率,提出一种基于线性耦合曲率的点云精简方法,以兼具效率和精度。对源扫描点云构建K-d树数据结构,并提出更稳健的线性耦合曲率特征模型以提取特征域;在此基础上,利用改进空间二分法快速精简非特征域;最后融合特征域和精简后的非特征域,得到最终精简结果。结果表明：该方法可得准确的精简比,拟合曲面模型仅存在微小的数据空洞。在90%精简比、2.7%噪声比条件下,保形性误差为0.867 mm,优于传统的点云精简方法,能够实现铸件点云的高效精简。

A Method for Point Cloud Reduction of Casting Automatic Grinding

Due to the problem of redundant point cloud data in the automatic grinding operation of castings,the data processing time is too long and the processing efficiency is affected.In order to improve the computational efficiency in point cloud processing,a point cloud reduction method was proposed based on linear coupled curvature to combine efficiency and accuracy.A K-d tree data structure was constructed for the source scan point cloud,and a more robust linear coupled curvature feature model was proposed to extract feature domain.On this basis,the improved spatial dichotomy method was used to quickly simplify the non-feature domain.After fusing the feature domain and the simplified non-feature domain,the final simplified result was obtained.The results show that the method can obtain accurate reduction ratio,and there are only small data holes in the fitted surface model.Under the condition of 90% reduction ratio and 2.7% noise ratio,the conformal error is 0.867 mm,which is better than the traditional point cloud reduction method,and can realize the efficient reduction of casting point cloud.