面向模型和室内外点云的高效配准算法  

Efficient registration algorithm for models and indoor and outdoor point clouds

作　　者：李俊杰 雷臣 李伟诚 余肖慧 杨宇焓 朱文利 LI Junjie;LEI Chen;LI Weicheng;YU Xiaohui;YANG Yuhan;ZHU Wenli(College of Mechanical Engineering,Wuhan Polytechnic University,Wuhan 430048,China)

机构地区：[1]武汉轻工大学机械工程学院,湖北武汉430048

出　　处：《液晶与显示》2025年第3期493-504,共12页Chinese Journal of Liquid Crystals and Displays

基　　金：国家自然科学基金(No.52371074)。

摘　　要：针对现有点云配准算法对不同场景的点云进行配准时存在适用性弱、鲁棒性差及配准效率低下等问题,本文提出面向模型和室内外点云的高效配准算法。首先,采用体素网格滤波对点云进行下采样,并使用内部形态描述子(ISS)提取点云特征。然后,由快速点特征直方图(FPFH)对特征点进行特征描述,采用随机采样一致性(RANSAC)算法对点云进行粗配准。最后,通过图形处理器(GPU)并行加速的体素化广义迭代最近点(VGICP)算法实现精配准。实验结果表明,在含有噪声点的三维模型、室内及低重叠率室外点云中,本文算法在达到较高配准精度的同时仅耗时0.118 s、0.306 s和0.648 s。相比于现有的配准算法,配准效率提高了79.12%、82.41%和88.28%。本文算法在不同的应用场景下均具有较高的配准精度和配准效率,且适用性更强、鲁棒性更高。In response to the problems of weak applicability,low registration efficiency and poor robustness of existing point cloud registration algorithms when registering point clouds from different scenes,this paper proposes an efficient registration algorithm for models,indoor scene and outdoor scene point clouds.Firstly,voxel grid filtering is used to downsample the point cloud,and intrinsic shape signatures(ISS)is used to extract point cloud features.Then,fast point feature histograms(FPFH)are used to describe the feature points,and random sample consensus(RANSAC)algorithm is used for rough registration of point clouds.Finally,the voxelized generalized iterative closest point(VGICP)algorithm accelerated by a graphics processing unit(GPU)is used to achieve precise registration.Experimental results show that in the three-dimensional model,indoor and low overlap outdoor point clouds with noise,the proposed algorithm achieves high registration accuracy while only consuming 0.118 s,0.306 s,and 0.648 s,respectively.Compared with existing registration algorithms,the registration efficiency is improved by 79.12%,82.41%,and 88.28%,respectively.The proposed algorithm has high registration accuracy and efficiency in different application scenarios,and has stronger applicability and higher robustness.

关 键 词：点云配准 随机采样一致性 图形处理器 体素化广义迭代最近点 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]