一种融合形状与纹理的彩色点云配准算法
Color Point-Cloud Registration Algorithm Integrating Shape and Texture
张元 1史泽鹏 1庞敏 1熊风光 1杨晓文1
作者信息
- 1. 中北大学计算机科学与技术学院,山西 太原 030051;机器视觉与虚拟现实山西省重点实验室,山西 太原 030051;山西省视觉信息处理及智能机器人工程研究中心,山西 太原 030051
- 折叠
摘要
针对现有点云配准算法在点云几何特征不显著时配准效果差、精度低等问题,提出一种综合利用几何和纹理特征的点云配准方法.首先,从点云表面提取几何和纹理特征变化显著的关键点,随后对关键点的形状和纹理进行特征描述,并基于特征相似度进行关键点匹配.其次,使用随机采样一致性算法剔除误匹配的点并估计位姿矩阵,实现粗配准,为后续的精配准提供良好的位姿初值.最后,运用彩色迭代最近点(ICP)算法进行精配准.实验结果表明,该算法在处理杂乱、重叠率低和几何特征不显著的彩色点云模型时具有出色的配准精度.
Abstract
A point-cloud registration method that integrates shape and texture features is proposed to address the issues of unsatisfactory registration performance and low accuracy in existing point-cloud registration algorithms when the geometric features of the point cloud are insignificant.First,keypoints with geometric and texture features change significantly on the surface of a point cloud are extracted,the shape and texture of the keypoints are characterized,and key-point matching is performed based on feature similarity.Subsequently,a random-sampling consensus algorithm is used to eliminate mismatched points and estimate the pose matrix,thus achieving coarse registration and providing favorable initial pose values for the subsequent fine registration.Finally,a color iterative closest point(ICP)registration algorithm is used for fine registration.Experimental results show that this algorithm offers high registration accuracy when used for color point-cloud models with clutter,low overlap rates,and insignificant shape features.
关键词
点云配准/彩色点云/点云关键点/局部特征描述符/特征融合Key words
point cloud registration/color point-cloud/point cloud keypoints/local feature descriptors/feature fusion引用本文复制引用
出版年
2024
NETL
NSTL
万方数据