基于点云去噪算法的发动机舱段测量模型优化  

Optimization of Engine Bay Segment Measurement Model Based on Point Cloud Denoising Algorithm

作　　者：王琬琪 周淦 张世轩 徐志刚[1,2] 王军义[1,2] WANG Wan-qi;ZHOU Gan;ZHANG Shi-xuan;XU Zhi-gang;WANG Jun-yi(Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110169,China;不详)

机构地区：[1]中国科学院沈阳自动化研究所,沈阳110016 [2]中国科学院机器人与智能制造创新研究院,沈阳110169 [3]华北计算机系统工程研究所,北京100083 [4]中国科学院大学,北京100049

出　　处：《组合机床与自动化加工技术》2022年第1期39-42,共4页Modular Machine Tool & Automatic Manufacturing Technique

摘　　要：由于激光测量得到的航天发动机舱段点云中通常包含噪声,为提高后续路径规划中模型的精确度,提出一种针对噪声的点云去噪算法。首先,根据张量投票算法,通过点的张量矩阵得到扩散张量;其次,通过扩散张量设计各向异性扩散滤波在不同方向的速率,实现点云的大尺度噪声去噪;最后,通过点云的半径和标准差来实现双边滤波的自适应调参,进一步实现小尺度噪声去噪。对本算法进行了对比实验验证,结果表明该算法和传统算法相比,在有效剔除噪声点的同时,更好地保持了点云的几何特征,是一种高效的去噪算法。Since the point clouds of space engine nacelles obtained from laser measurements usually contain noise,a point cloud denoising algorithm for noise is proposed in this paper to improve the accuracy of the model in subsequent path planning.Firstly,according to the tensor voting algorithm,the diffusion tensor is obtained through the tensor matrix of points;secondly,the rate of anisotropic diffusion filtering in different directions is designed through the diffusion tensor to achieve large scale noise denoising of the point cloud;finally,the radius and standard deviation of the point cloud are used to achieve adaptive tuning of the bilateral filtering to further achieve small scale noise denoising.This algorithm is validated by comparison experiments,and the results show that the algorithm is an efficient denoising algorithm compared with the traditional algorithm,which can effectively eliminate noise points while better maintaining the geometric features of the point cloud.

关 键 词：图像处理 航天发动机舱段 点云去噪 张量投票 逆向工程 

分 类 号：TH16[机械工程—机械制造及自动化] TG502[金属学及工艺—金属切削加工及机床]