.png){kind=icon}
返回
Registration Algorithm for Point Cloud Based on Normalized Cross-Correlation
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2942127.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose a registration algorithm based on neighborhood similarity for 3D point clouds collected by optical measurement and without prior information. The algorithm first applies the improved minimum spanning tree (Prim algorithm) to classify the point cloud in order to obtain the topology information of the data. Specifically, vectors among root nodes and child nodes are processed, and the points on nodes are classified into different levels according to their scanning angle to simplify data and preserve the most representative points. Then, through the perspective conversion between 2D and 3D and according to the corresponding point set obtained by previous classification, the fast normalized cross-correlation (a 2D matching criterion) is applied to determine the relationship between initial characteristic points. Finally, distance constraints remove the errors between point pairs and allow calculating the registration parameters. Experimental results show that the algorithm has high registration accuracy and is suitable for point cloud data obtained by laser and structured light acquisition.
Keyword:
Minimum spanning tree
normalized cross-correlation
optical measurement
point cloud classification
point cloud registration
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
Research on classified real-time flood forecasting framework based on K-means cluster and rough set
PREAI
分享收藏
查看更多内容