基于二进制特征描述符的点云数据配准  

Point Cloud Data Registration Based on Binary Feature Descriptors

作　　者：蔡炜 岳东杰[1] 陈强 Cai Wei;Yue Dongjie;Chen Qiang(School of Earth Science and Engineering,Hohai University,Nanjing 211100,Jiangsu,China;Shanghai Institute of Surveying and Mapping,Third Branch,Shanghai 200063,China)

机构地区：[1]河海大学地球科学与工程学院,江苏南京211100 [2]上海市测绘院三分院,上海200063

出　　处：《激光与光电子学进展》2022年第10期323-330,共8页Laser & Optoelectronics Progress

摘　　要：传统点云数据特征描述符存在表达能力不足、计算效率低和鲁棒性不强等问题,针对二进制形状上下文(BSC)特征描述符不能有效检测到曲率分布较大的区域以及局部坐标系存在二义性的问题,提出了一种基于二进制特征描述符的点云数据配准算法。首先,采用内在形状签名关键点检测法和三维曲面片估计方法改进语义问题。然后,利用汉明距离与改进的几何一致性方法进行特征匹配。最后,用随机抽样一致性算法剔除误匹配。实验结果表明,相比快速点特征直方图、融合点签名的直方图和BSC算法,本算法与迭代最近邻点算法的结合能在大幅度提升配准效率的同时减小配准误差。Traditional feature descriptors of point cloud data show disadvantages such as insufficient expressiveness, low computational efficiency, and poor robustness. Aiming at the problem that the binary shape context(BSC) feature descriptors, regions with a large curvature distribution cannot be effectively detected and the ambiguity of the local coordinate system suffers. This study proposes a point cloud data registration algorithm based on binary feature descriptors. First, the intrinsic shape signature keypoint detection method and three-dimensional surface patch estimation method are used to address the problem of semantics. Then, the Hamming distance and improved geometric consistency method are used for feature matching. Finally, the random sampling consensus is used to eliminate false matches. Experimental results show that compared with the fast point feature histogram,signature of histogram of orientations, and BSC algorithms, combining the algorithm with the iterative closest point algorithm can considerably improve the registration efficiency and reduce the registration error.

关 键 词：机器视觉 二进制形状上下文算法 汉明距离 点云配准 特征匹配 迭代最近邻点算法 

分 类 号：P237[天文地球—摄影测量与遥感]