基于RANSAC分割的点云数据K-近邻去噪算法研究
DOI:
作者:
作者单位:

1.装备学院 北京 101416; 2.63750部队 渭南 714000

作者简介:

通讯作者:

中图分类号:

TP391.9

基金项目:


K-nearest neighbor denoising algorithm for point cloud data based on RANSAC algorithm
Author:
Affiliation:

1.Equipment Academy of PLA, Beijing 101416, China; 2.Unit 63750 of PLA, 714000 Weinan, China

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    针对基于TOF深度相机的空间目标表面重建的点云源数据容易受到仪器本身、扫描环境、外界干扰等影响,而含有大量的无效点和噪声点,增加了计算负担且影响了重建质量等问题,提出了一种基于随机采样一致性背景分割的点云K近邻去噪方法,以消除目标数据的异常值和无效点。首先,改进RANSAC算法,通过设置不同的阙值对原始点云进行背景分割,以确保准确提取待重建目标的主要特征。然后,通过K近邻点云平均算法和双边滤波算法移除离群点,最后使用体素化网格方法实现点云大数据的下采样,简化了目标点云,保留了局部特征,加快了曲面重建速度。实验结果表明,该算法能够有效的剔除噪声点,准确率高,实时性好,满足应用的要求。

    Abstract:

    According to the data of space target surface reconstruction of TOF depth camera point cloud based on source vulnerable to the instrument itself, scanning the environment, the effect of external disturbances, invalid points and noise points and large amount, increases the computational burden and the impact of the reconstruction quality problems, this paper proposes a denoising method of random sample consensus background segmentation of point cloud based on knearest neighbor, to eliminate outliers and invalid target data. Firstly, the improved RANSAC (random sample consensus,RANSAC) algorithm, by setting different threshold of the original point cloud background segmentation, to Ensure the accuracy of extracting the main features of the reconstructed object. Then, through the Knearest neighbor point cloud average algorithm and bilateral filtering algorithm to remove outliers, finally using voxel grid method to achieve point cloud data sampling, simplified target point cloud, retains the local characteristics, to speed up the reconstruction speed. The experimental results show that the the algorithm can effectively eliminate noise, high accuracy, good realtime performance, meet the application requirements.

    参考文献
    相似文献
    引证文献
引用本文

郭宁博,陈向宁,何艳华.基于RANSAC分割的点云数据K-近邻去噪算法研究[J].电子测量技术,2017,40(12):209-213

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2018-01-30
  • 出版日期: