| 引用本文: | 陆军,陈坤,范哲君.点邻域尺度差异描述的点云配准算法.[J].国防科技大学学报,2021,43(3):128-134.[点击复制] |
| LU Jun,CHEN Kun,FAN Zhejun.Point cloud registration algorithm based on scale difference descriptor of point neighborhood[J].Journal of National University of Defense Technology,2021,43(3):128-134[点击复制] |
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| 点邻域尺度差异描述的点云配准算法 |
| 陆军1,2,陈坤1,2,范哲君1,2 |
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(1. 哈尔滨工程大学 自动化学院, 黑龙江 哈尔滨 150001;2. 哈尔滨工程大学 船海装备智能化技术与应用教育部重点实验室, 黑龙江 哈尔滨 150001)
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| 摘要: |
| 针对传统特征描述符计算复杂度高、配准精度低的问题,提出一种基于不同尺度点邻域特征信息差异的点云配准算法。在特征描述符方面,对关键点选取不同尺度的邻域空间,计算各尺度空间之间的特征值归一化向量差异和法向量夹角,建立点邻域尺度差异描述符,特征描述符计算简单且节省时间。在关键点选取方面,根据曲面形状指数设计了一种寻找关键点的方法,提取的点具有很好的代表性。在对应关系寻找方面,提出一种基于欧式距离的对应点二重筛选方法,找出对应点对集,设计了基于全局距离的全局最优点云变换矩阵求取方法。实验结果表明,点邻域尺度差异描述的点云配准算法具有良好的配准精度和稳健的噪声鲁棒性。 |
| 关键词: 点云配准 点邻域尺度差异 形状指数 二重筛选 全局最优 |
| DOI：10.11887/j.cn.202103015 |
| 投稿日期：2019-10-14 |
| 基金项目:黑龙江省自然科学基金资助项目(F201123) |
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| Point cloud registration algorithm based on scale difference descriptor of point neighborhood |
| LU Jun1,2, CHEN Kun1,2, FAN Zhejun1,2 |
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(1. College of Automation, Harbin Engineering University, Harbin 150001, China;2. Key Laboratory of Ministry of Education on Intelligent Technology and Application of Marine Equipment, Harbin Engineering University, Harbin 150001, China)
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| Abstract: |
| Aiming at the problem of high computational complexity of feature descriptors and low registration accuracy, a point cloud registration algorithm based on the differences of region′s feature information in different scales was proposed. In the aspect of feature descriptor, the neighborhood spaces with different scales were selected for the key points. The normalized eigenvalue vector differences and normal vector angles between the scales were calculated. The descriptor of key point based on neighborhood scale differences was created. It is simple and time-saving. For the key points searching, a key point extraction method based on shape index was designed. The obtained key points have better representative ability. For searching the corresponding relationship, a double screening method based on Euclidean distance was proposed to find the correspondence set. The global optimal searching algorithm based on global distance was designed to find the transformation matrix between two point clouds. The experimental results show that the registration algorithm has good accuracy and robust noise robustness. |
| Keywords: point cloud registration point neighborhood scale difference shape index double screening global optimum |
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