基于姿态标准化的线特征点云提取方法

李伟; 李旭东; 赵慧洁; 张颖

基于姿态标准化的线特征点云提取方法

北京航空航天大学仪器科学与光电工程学院, 北京 100191

基金项目: 国家自然科学基金资助项目(60802044)

详细信息

中图分类号: TP391
计量
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- 被引次数: 0
出版历程
- 收稿日期: 2012-08-30
- 修回日期: 2013-08-06
- 网络出版日期: 2013-08-30

Attitude normalization based line feature point cloud extraction approach

School of Instrumentation Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 从零件三维点云中提取棱边等线特征所对应的点云是零件模型重构的关键,也是点云数据处理的基本操作.基于曲率的线特征点云提取方法易受点云初始姿态以及曲率估计方法的影响,曲面拟合及曲率估计误差较大.提出了一种基于点云姿态标准化的线特征点云提取方法:首先计算点云主方向并将其同z轴对准实现点云姿态的标准化,然后进行曲面拟合并以最大主曲率绝对值作为曲率估计值,最后对曲率值取阈值提取出线特征点云.用不同类型的点云数据进行了实验,结果表明所提方法有较高的提取效率和良好的适用性.
- 线特征点云提取 /
- 点云数据处理 /
- 姿态标准化 /
- 曲率估计
Abstract: Extracting the point cloud corresponding to the edges from point cloud of a part is a basic work in the part 3D reconstruction, also a key point of point cloud processing. The commonly used curvature based LFPC(line feature point cloud) extraction approach may have large errors in curve surface fitting and curvature estimation due to the arbitrary attitude of original point cloud and the way of curvature estimation. An attitude normalization based LFPC extraction approach is proposed. First the attitude normalization is accomplished by adjust the principle direction of the point cloud to the z axis, then, after the curve surface fitting, the main curvature with the maximal absolute value is treated as curvature, finally, the LFPC is obtained by applying a curvature threshold. Experiments on different models show that the proposed approach has better efficiency and is adaptive to different kinds of models.
- line feature point cloud extraction /
- point cloud data processing /
- attitude normalization /
- curvature estimation

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参考文献(12)

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