散乱点云中规则曲面的识别和特征参数提取研究  

Research on the Identification and Extraction of Characteristic Parameters of Regular Surfaces in Scattered Point Clouds

作　　者：王炯宇 王建军[1] 李旭辉 程霄霄 聂栋栋 王光彬 周宝柱 Wang Jiongyu;Wang Jianjun;LI Xuhui;Cheng Xiaoxiao;Nie Dongdong;Wang Guangbin;Zhou Baozhu(School of Mechanical Engineering,Shandong University of Technology,Zibo 255049,Shandong,China;Zibo Tianjun Cleaning Equipment Co.,Ltd.,Zibo 255000,Shandong,China;Zhejiang Datang Wushashan Pozwer Generation Co.,Ltd.,Ningbo 315700,Zhejiang,China)

机构地区：[1]山东理工大学机械工程学院,山东淄博255049 [2]淄博天骏清洁设备有限公司,山东淄博255000 [3]浙江大唐乌沙山发电有限责任公司,浙江宁波315700

出　　处：《应用激光》2024年第11期158-173,共16页Applied Laser

基　　金：国家自然科学基金(51575326);淄博市重点研发项目(2021SNCG0053);张店区校城融合项目(2021JSCG0020);山东省重点实验室开放课题项目-山东省精密制造与特种加工重点实验室;淄博市中小企业创新能力提升项目(2022tsgc0059)。

摘　　要：在无人驾驶技术中,一般采用激光雷达对周围环境进行扫描探测实现三维成像、目标识别避障、地图构建及自主导航。其中,基于激光扫描点云提取三维物体表面的曲面特征,有利于对物体表面形态进行精确三维重建,是进行三维物体识别、避障和自主导航的必要前提和重要手段。因此,为从激光点云中有效提取三维物体表面的曲面特征,提出一种曲面特征提取组合优化算法,即在点云处理的顺序环节,融合多种措施实现最优处理,提高各环节处理算法的可靠性和精度。具体优化措施为:首先,采用RANSAC(随机采样一致性)抽样策略优化点邻域;其次,采用Harris-3D算法从点云数据中提取出关键点,并结合基于法向量夹角与欧式距离双阈值的区域生长法,将点云进行聚类分割;最后,对聚类分割后的点云面片进行三维物体曲面的特征提取,实现对三维物体表面形态的特征标识。通过对点云中的规则曲面的提取重建实验,结果表明,提出的融合优化算法可有效提高三维物体点云中规则曲面特征提取的精度和效率,如对于平面、圆柱体、圆锥体二次曲面的提取重建误差小于0.075 mm,对于球形面的提取重建误差小于2 mm。另外,对拥有大量点云数据的真实无人驾驶激光扫描场景进行了实验验证,结果表明该算法也有很好的曲面特征提取效果,可有效实现三维物体表面形态的识别和重建。In driverless technology,LiDAR is used to scan and detect the surrounding environment to achieve 3D imaging,target recognition,obstacle avoidance,map construction,and autonomous navigation.Among them,surface feature extraction based on laser scanning point cloud is conducive to the accurate 3D reconstruction of the surface shape of objects,which is a necessary prerequisite and important means for 3D object recognition,obstacle avoidance,and autonomous navigation.Therefore,to effectively extract the surface features of 3D objects from laser point clouds,a combined optimization algorithm for surface feature extraction is proposed in this paper,that is,in the sequence of point cloud processing,a variety of measures are integrated to achieve the optimal processing,to improve the reliability and accuracy of each link processing algorithm.The specific optimization measures are as follows.First,the RANSAC(random sampling consistency)sampling strategy is used to optimize the point neighborhood,Secondly,the Harris-3D algorithm is used to extract key points from point cloud data,and combined with the region growth method based on the angle of the normal vector and Euclidean distance double threshold,the point cloud is segmented by clustering.Finally,feature extraction of the 3D object surface is carried out on the point cloud surface after clustering and segmentation,and feature identification of the 3D object surface form is realized.Through experiments on the extraction and reconstruction of regular surfaces in point clouds,the results show that the proposed integration optimization algorithm can effectively improve the surface accuracy and efficiency of feature extraction of the regular surfaces of the 3D object from point clouds,for the plane,cylinder,cone,the extraction of quadric surface reconstruction error is less than 0.075 mm,and for the spherical surface,the reconstruction error is less than 2 mm.In addition,the experimental verification of the real unmanned laser scanning scene with a large amount of point clou

关 键 词：激光点云 点云聚类分割 特征提取 区域生长法 融合优化算法 

分 类 号：TN958.98[电子电信—信号与信息处理]