摘要地面激光雷达技术为快速、精确地测量真实世界中的三维实体提供了新的途径，但获取的点云数据密集度大、冗余度高，往往需经过人机交互式的后处理，才能得到用户需要的测量结果。本文研究了超高压线路中多组导线相间距的测量问题，通过地面激光雷达获取导线及线间隔棒的空间数据，基于主成分分析（PCA）算法对导线和线间隔棒进行自动分类，对分割出的间隔棒数据进行中心坐标及间距的自动计算，实现了数据的快速、精确获取及自动后处理，为相间隔棒的制作和安装提供了必要的数据保障，也为地面激光雷达技术的实际应用提供了实验基础。
点云是通过三维测量设备所获取的物体表面的三维空间位置数据，具有广泛应用。通过本课题的研究，了解三维测量技术，掌握点云数据的处理和PCA算法在点云分类中的应用。用Matlab编制程序，实现点云数据的读取和PCA算法，将点云数据中的物体按形状进行分类。64400
毕业论文关键词：地面激光雷达;间隔棒;相间距;主成分分析（PCA),matlabb编程
毕业设计说明书（论文）外文摘要
Title Natural Terrain Classification using 3-D Ladar Data
Abstract
Terrestrial laser scanner provides a novel approach for efficient measurement of objects in the real world, but the data acquired are high density point cloud with lots of redundancy, which usually have to be processed by human-interfered ways before being used by customers. In this paper, phase to phase distance among bundle conductors of extra-high voltage transmission line is measured. Firstly spatial data of conductors and spacers are acquired by laser scanner, then they are segmented by principle component analysis (PCA) method, and finally the spacer’s center and the phase to phase distance are calculated. Thus rapid and accurate data acquisition and automatic data post-processing are realized, which not only provides a foundation for produce and installation of phase to phase spacer, but also facilitates the application of terrestrial laser scanner.
Point cloud is obtained by three-dimensional measuring device three-dimensional surface of the object position data, and widely applied. Through this research projects, understand the three-dimensional measurement techniques, master point cloud data processing and PCA algorithm in the point cloud Classification. Using Matlab programming, achieve point cloud data read and PCA algorithm, the point cloud data are classified according to the shape of the object..
Keywords: Terrestrial laser scanner; Spacer; Principal component analysis; Matlab programming.
目次
1 引言 1
1.1 本课题的意义和背景 1
1.2 本论文的主要内容 1
2 三维激光扫描基础 2
2.1 三维激光扫描简介 2
2.2 三维激光扫描采用的基本设备 3
2.3三维激光扫描技术建模流程 5
2.4三维点云数据自动分类和参数计算研究背景 7
2.5三维点云数据自动分类和计算基本原理及步骤 9
2.6本章小结 10
3 基于matlab的算法实现 11
3.1 matlab简介 11
3.2 PCA基本原理 11
3.3 在matlab上的算法具体实现 13
3.4 实验结果 17 PCA三维物体点云数据的自动分类与参数计算:http://www.751com.cn/zidonghua/lunwen_71698.html