Point cloud registration with normal feature histogram

SHEN Ying-hua; LI Zhuo-jia; YANG Cheng; LI Hao-yong

doi:10.3788/OPE.20152313.0592

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Point cloud registration with normal feature histogram

更新时间：2020-08-13

- Point cloud registration with normal feature histogram
- Optics and Precision Engineering Vol. 23, Issue 10z, Pages: 591-598(2015)
- 作者机构：
  
  中国传媒大学信息工程学院, 北京 100024
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152313.0592
  CLC： TP242.6;TP391.41
- Received：18 May 2015，
  
  Revised：10 June 2015，
  
  Published：14 November 2015
- 稿件说明：
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沈萦华, 李卓嘉, 杨成等. 基于法向特征直方图的点云配准算法[J]. 光学精密工程, 2015,23(10z): 591-598

SHEN Ying-hua, LI Zhuo-jia, YANG Cheng etc. Point cloud registration with normal feature histogram[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 591-598
沈萦华, 李卓嘉, 杨成等. 基于法向特征直方图的点云配准算法[J]. 光学精密工程, 2015,23(10z): 591-598 DOI： 10.3788/OPE.20152313.0592.

SHEN Ying-hua, LI Zhuo-jia, YANG Cheng etc. Point cloud registration with normal feature histogram[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 591-598 DOI： 10.3788/OPE.20152313.0592.

摘要

为了提高点云配准速度并减少描述子的维度

本文首先提出了一种查找点云重叠区域的预处理办法。该方法使用基于八叉树的区域生长K-means聚类算法对点云进行分块

通过近似三角形查询点云的重叠区域。另外

在关键点描述中提出了基于点云特征直方图的低维度描述子邻域点积直方图(LDFH)算法。提出的预处理方法可以使初始配准的点云数据量减少10%~20%

去除了不必要的冗余运算过程。与快速点特征直方图(FPFH)描述子的点云配准算法相比

提出的邻域点积直方图算法将维度降低至24维

同时使描述速度提高了15%左右。利用本文算法对实际扫描获取到的点云数据进行配准时

可以在5 min内完成小于1 m

数据的准确配准。本文算法减少了配准耗时

降低了描述子维度

在实际的点云配准中有好的效果。

Abstract

To reduce the time cost of point cloud registration and to decrease the dimension of a descriptor

this paper proposes a pre-processing method to look up the overlap region of a point cloud. This method uses the region growing variant K-means clustering based on octree structure to block the point cloud

and then get the overlap region of point cloud by the triangle & point number decision formula. Moreover

a lower dimension descriptor named Local Dot Feature Histogram(LDFH) is also created based on a point cloud feature histogram in the key point description. The pre-processing method decreases about 10%-20% data volume of point cloud and removes some unnecessary redundant operation. As compared with the Fast Point Feature Histogram(FPFH) descriptor

the proposed LDFH algorithm just has 24-dimension and takes the computation time by 15%. When the methods proposed in this paper are used to register point cloud data in practice

the proposed method can complete small geometry solid registration for one cubic meter in less than five minute. The proposed algorithm achieves the goals of reducing the cost time

lowering descriptor dimension

and has a good effect in actual registration.

关键词

Keywords

references

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