基于改进3DSIFT算法的点云配准方法  

Point cloud registration based on improved 3DSIFT algorithm

作　　者：张平均 赵浩 ZHANG Ping-jun;ZHAO Hao(School of Electronic,Electrical Engineering and Physics,Fujian University of Technology,Fuzhou 350118,China)

机构地区：[1]福建理工大学电子电气与物理学院,福建福州350118

出　　处：《激光与红外》2025年第2期296-303,共8页Laser & Infrared

摘　　要：点云配准是三维数据处理的一个关键步骤。针对配准过程中特征点代表性和描述性弱导致配准效率低的问题,本文提出了一种基于改进三维尺度不变特征(3DSIFT)算法的点云配准方法。首先,结合信息熵理论对3DSIFT算法提取出的特征点进行精简,保留代表性和描述性强的点作为待配准点;其次,对特征点添加唯一形状上下文(USC)描述;然后,基于渐近采样一致性(PROSAC)算法完成粗匹配;最后,对源点云和目标点云建立双向KD树以减少搜索时间,加速迭代最近点(ICP)完成精配准。实验结果表明,与3种比较算法相比,该方法的平均配准误差分别降低了87.2%、61.3%、22.5%,且配准后的点云重叠率更高。Point cloud registration is a key step in 3D data processing.Aiming at the problem of low registration efficiency due to the weak representativeness and descriptiveness of feature points in the registration process,a point cloud registration method based on the improved 3D scale-invariant features(3DSIFT)algorithm is put forward in this paper.Firstly,the feature points extracted by the 3DSIFT algorithm are streamlined by combining the information entropy theory,and the representative and descriptive points are retained as the points to be registered.Secondly,the unique shape context(USC)description is added to the feature points.Then,coarse matching is completed based on the progressive sample consensus(PROSAC)algorithm.Finally,a bidirectional KD-tree is established for the source and target point clouds to reduce the search time and accelerate the iterative closest point(ICP)to complete the fine registration.

关 键 词：点云配准 三维尺度不变特征(3DSIFT) 特征点精简 唯一形状上下文(USC) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TN958.98[自动化与计算机技术—计算机科学与技术]