基于DBSCAN的改进RANSAC点云平面拟合算法  

Improved RANSAC Point Cloud Plane Fitting Algorithm Based on DBSCAN

作　　者：叶锦华[1,2] 林旭敏 吴海彬 YE Jinhua;LIN Xumin;WU Haibin(School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou 350116,China;Fujian Key Laboratory of Special Intelligent Equipment Safety Measurement and Control,Fuzhou 350007,China)

机构地区：[1]福州大学机械工程及自动化学院,福建福州350116 [2]福建省特种智能装备安全与测控重点实验室,福建福州350007

出　　处：《湖南大学学报(自然科学版)》2025年第2期76-87,共12页Journal of Hunan University:Natural Sciences

基　　金：国家重点研发计划资助项目(2018YFB1308603);福建省高校产学合作项目(2022H6016);教育部产学合作协同育人项目(231003084265411)。

摘　　要：针对随机采样一致性(random sample consensus,RANSAC)算法对含有噪声的点云数据进行平面拟合时效果不佳和容易产生误识别的问题,对算法进行改进.通过基于密度的噪声应用空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法改变RANSAC算法初始点集合的选择策略,并使用主成分分析法(principal component analysis,PCA)计算点云各点法向量,以点到平面距离以及点的法向量与平面法向量夹角两个约束条件同时作为RANSAC算法平面拟合模型内点判定的准则.采用无噪声与分别含有300个噪声点和500个噪声点的点云仿真数据进行测试,本文算法拟合结果均接近理论值且内点距离标准差分别为1.007×10-8、0.003、0.007,优于RANSAC算法.采用实际工件点云数据在两种工况场景下进行测试,本文算法拟合平面内点比率相对于传统RANSAC算法分别提高24.7%和24.6%,平面提取完整度及准确率同样优于RANSAC算法.仿真模拟及实例分析证明了本文算法的有效性.To address the problem of poor point cloud plane fitting effect and easy misidentification of the random sample consensus(RANSAC)algorithm for noisy point cloud data,improvements to the algorithm are necessary.The proposed algorithm employs density-based spatial clustering of applications with noise(DBSCAN)to modify the selection strategy of the initial point set in the RANSAC algorithm and uses principal component analysis(PCA)to compute the normal vectors of each point in the point cloud.Two constraint conditions,the distance from the point to the plane and the angle between the normal vectors of the point and the plane,are simultaneously used as the criteria for determining the points within the RANSAC algorithm model.The point cloud simulation data with no noise and 300 and 500 noisy points are used for testing.The fitting results of the proposed algorithm are all approximate to the theoretical values,and the standard deviations of the inner point distance are 1.007×10-8,0.003 and 0.007,respectively,better than those of RANSAC algorithm.Using actual workpiece point cloud data for testing in two operating scenarios,the proposed algorithm improves the fitting ratio of in-plane points by 24.7%and 24.6%compared to the traditional RANSAC algorithm,respectively.The completeness and accuracy of plane extraction are also superior to those of the RANSAC algorithm.Simulation and case analysis validate the effectiveness of the proposed algorithm.

关 键 词：点云平面拟合 随机采样一致性 噪声 密度聚类 主成分分析 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]