一种鲁棒的飞机零件多尺度特征点检测方法

Robust Scheme for Multi-scale Features Detection from Aircraft Parts

针对飞机零件几何模型多尺度特征检测问题,提出一种鲁棒的多尺度特征点检测算法。算法首先设计一种L_(1)中值滤波算法获取无结构点云精准法线;然后基于计算得到的法线,计算各点的局部邻域波动,提取初始特征点;最后针对初始特征点数据冗余问题,提出一种收缩优化策略,计算最终特征数据点。试验结果表明,与传统的点云特征点检测方法相比,算法检测精度明显提升;其次,对于存在较大噪声的点云数据仍然能够有较好的提取效果,算法鲁棒性高。

Aiming at the problem of multi-scale feature detection of aircraft part geometric models,a robust multi-scale feature point detection algorithm is proposed.The algorithm first designs a median filter algorithm to obtain the precise normals of unstructured point clouds.Then the local neighborhood fluctuations of each point is calculated based on the calculated normals,and the initial feature points is extractd.Finally,for the data redundancy of the initial feature points,a shrinkage optimization strategy is proposed to calculate the final feature data points.Experimental results show that compared with the traditional point cloud feature point detection method,the algorithm proposed in this paper has significantly improved detection accuracy.Secondly,this method is able to extract precise feature points,even when the input is corrupted by heavy noise.