基于散乱点云的列车关键部件高效滤波平滑算法  

Efficient Filtering and Smoothing Algorithm For Train Key Components Based on Scattered Point Clouds

作　　者：曾妮 李金龙[1] 高晓蓉[1] 张渝[1] 罗林[1] Zeng Ni;Li Jinlong;Gao Xiaorong;Zhang Yu;Luo Lin(School of Physical Science and Technology,Southwest Jiaotong University,Chengdu 610031,Sichuan,China)

机构地区：[1]西南交通大学物理科学与技术学院,四川成都610031

出　　处：《激光与光电子学进展》2023年第14期104-111,共8页Laser & Optoelectronics Progress

基　　金：国家自然科学基金国际合作项目(61960206010)。

摘　　要：针对三维激光扫描设备实际采集的列车关键部件点云模型通常存在噪声点和采集误差等问题,提出一种高效的点云小尺度噪声滤波平滑算法。首先利用K-D树构建点与点之间的几何关系,设置中心点并利用K近邻算法查询邻域信息;然后假设输入点云与滤波后输出的点云之间存在线性关系,从而利用两片点云构造代价函数;最后通过求解代价函数最小值确定线性参数,从而得到平滑后的点云模型。代价函数构建过程中,根据点与其K近邻内点的欧氏距离的方差与整体欧氏距离方差的比值大小自适应地调节权重,达到动态调整线性模型参数的目的。实验结果表明,所提算法能够快速矫正小尺度噪声、平滑边界轮廓、提高质量,为点云识别、重建等后期任务奠定基础。An efficient point cloud small-scale noise filtering and smoothing algorithm is proposed to address the issues of noise points and acquisition errors in the point cloud model of the key components of the train acquired by 3D laser scanning equipment.First,a K-D tree was used to construct the geometric relationship between points,set the center point,and query the neighborhood information using the K nearest neighbor algorithm.Then,it was assumed that there was a linear relationship between the input point cloud and the filtered output point cloud.Finally,the linear parameters were determined by solving the cost function’s minimum value,and the smoothed point cloud model was obtained.To adjust the linear model’s parameters dynamically,the weight was adaptively adjusted based on the ratio of the Euclidean distance variance of the point and its K nearest neighbor to the overall Euclidean distance variance during the construction of the cost function.The experimental results reveal that the proposed algorithm can quickly correct small-scale noise,smooth the boundary contour,improve the quality,and thereby lay the groundwork for future tasks such as point cloud recognition and reconstruction.

关 键 词：图像处理 点云 去噪 列车关键部件 自适应加权引导滤波 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]