Point Cloud Trajectory Planning Based on Octree and K-Dimensional Tree Algorithm

被引：0

作者：

Qian, Cheng ^{[1
]}

Lin, Weiyang ^{[1
]}

Chen, Jianhong ^{[1
]}

Li, Zhan ^{[1
]}

Gao, Huijun ^{[1
]}

机构：

[1] Harbin Inst Technol, Res Inst Intelligent Control & Syst, Harbin 150080, Peoples R China

来源：

2016 31ST YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION (YAC) | 2016年

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

k-d Tree; Octree; Point Cloud Data; Trajectory Planning;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To analyze point cloud data of a car part, massive computations need to be carried out; if it is done by conventional ways, the processing efficiency is relatively low. However, by applying partition method to point data analysis, the work is easier. Such partition method should follow the minimal-distance matching principle to find out the enclosing matching points. Then the intersections of the cutting plane and the point cloud data can be calculated by the matching points. To find the intersections and the neighbor points easier, k-d Tree (k-dimensional tree) and Octree algorithms are used to process the point cloud data. At last, Least Square Method is used to calculate the normal vector of the intersection, which leads to the trajectory of the spray gun on the surface of the car part. Experiments have been done to validate the proposed approach and the results showed that the trajectory planning efficiency was improved.

引用

页码：213 / 218

页数：6