基于关键点提取与优化迭代最近点的点云配准  

Accurate Registration of 3D Point Clouds Based on Keypoint Extraction and Improved Iterative Closest Point Algorithm

作　　者：彭真[1,2] 吕远健 渠超 朱大虎[1,2] Peng Zhen;LüYuanjian;Qu Chao;Zhu Dahu(Hubei Key Laboratory of Advanced Automotive Components Technology,Wuhan University of Technology,Wuhan,Hubei 430070,China;Hubei Collaborative Innovation Center for Automotive Components Technology,Wuhan University of Technology,Wuhan,Hubei 430070,China)

机构地区：[1]武汉理工大学现代汽车零部件技术湖北省重点实验室,湖北武汉430070 [2]武汉理工大学汽车零部件技术湖北省协同创新中心,湖北武汉430070

出　　处：《激光与光电子学进展》2020年第6期60-71,共12页Laser & Optoelectronics Progress

基　　金：国家重点研发计划(2017YFB1303403);国家自然科学基金(51675394);数字制造装备与技术国家重点实验室(DMETKF2018018);“111”工程(B17034)。

摘　　要：对强噪声且密度不均匀的点云进行高效、高精度配准是一个难题。针对此难题,提出一种基于关键点提取与优化迭代最近点(ICP)的点云配准算法。在粗配准中,将体素格滤波与法向距离关键点的提取相结合,计算关键点的快速点特征直方图以进行特征匹配,然后采用对应关系估计优化随机采样一致性(RANSAC)算法以进行误匹配剔除。在精配准中,采用最优节点优先(BBF)算法搜索k-d tree最近点,设定动态阈值消除误配对,最后利用基于"点到三角面"模型的加速ICP算法计算配准向量。通过对模型点云和建筑物点云进行配准,将所提算法与其他常用的算法进行比较分析。实验表明,所提算法具有良好的稳健性和抗噪性,能显著提升配准速度和配准精度。Registering highly efficient and accurate point clouds with strong noise and inhomogeneous density remains a challenging task.In this paper,we propose a point cloud registration algorithm based on keypoint extraction and the improved iterative closest point(ICP).In coarse registration,we first fused the voxel grid filtering and normal distance keypoint extraction and then computed the fast point feature histogram of keypoints for feature matching.Then the random sampling consistency(RANSAC)algorithm was estimated and optimized by correspondent relation for eliminating mismatches.In fine registration,we implemented the best bin first(BBF)algorithm to search for the nearest point of k-d tree and set the dynamic threshold to eliminate wrong point pairs.Finally,we used the improved accelerated ICP algorithm based on the"point-to-triangle plane"model to obtain the registration vector.By registering the model point cloud and building point cloud,we compared the proposed algorithm with other commonly used algorithms.The results demonstrate that the proposed algorithm is robust against noise,and in particular,the running speed and registration accuracy are enhanced.

关 键 词：图像处理 点云配准 特征点提取 迭代最近点 快速点特征直方图 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术]