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基于切片采样和质心距直方图特征的室外大场景三维点云分类

Three-Dimensional Point Cloud Classification of Large Outdoor Scenes Based on Vertical Slice Sampling and Centroid Distance Histogram

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摘要

三维(3D)点云数据在智能驾驶、遥感测量和虚拟现实等领域的应用越来越广泛。针对室外大场景, 提出了一种兼顾快速性与准确性的三维点云分类算法, 该算法首先对原始点云进行离群点去除, 并在现有地面滤波算法的基础上,结合点云法向量差信息滤除地面点; 然后再使用具有噪声的基于密度(DBSCAN)的聚类算法对非地面点云进行分割, 同时针对点云的过分割问题采用了就近融合的策略; 再提取出不同物体点云的全局特征, 包括垂直方向切片采样直方图和质心距直方图, 以及点云的二维投影图像方向梯度直方图(HOG)特征; 最后, 通过支持向量机(SVM)分类器分类, 得到较为精确的三维点云分类结果。实验结果表明:所提算法可以将复杂的室外大场景分类为较为准确的单个物体, 并且具有较高的精确率以及召回率; 相较于其他算法, 所提算法的效率有了较大提高。

Abstract

Three-dimensional (3D) point cloud data are widely used in intelligent driving, remote sensing, and virtual reality. This study presents a 3D point cloud classification algorithm that classifies large outdoor scenes effectively and accurately. First, the algorithm eliminates outliers from the original point cloud. Then, based on the off-the-shelf ground-filtering algorithm, it leverages difference of norms to filter ground points. Then, it uses the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to segment non-ground point cloud. The nearest fusion strategy is used to solve the oversegmentation problem of the point cloud. Then, the proposed algorithm extracts global features that represent different objects from the point cloud, including vertical slice sampling and centroid distance histograms, as well as histogram of oriented gradient (HOG) features representing a two-dimensional projected image of the point cloud. Finally, a support vector machine (SVM) classifier is used to obtain the accurate 3D point cloud classification results. The experimental results reveal that the proposed algorithm can classify complex large outdoor scenes into accurate single objects with high accuracy and high recall rate. The proposed algorithm is more efficient compared with other algorithms.

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中图分类号:TN958.98

DOI:10.3788/cjl201845.1004001

所属栏目:测量与计量

基金项目:国家自然科学基金(61175031)、国家863计划子课题(2012AA041402)、国家科技支撑计划子课题(2015BAF13B00-5)

收稿日期:2018-02-03

修改稿日期:2018-04-22

网络出版日期:2018-04-27

作者单位    点击查看

佟国峰:东北大学信息科学与工程学院, 辽宁 沈阳 110819
杜宪策:东北大学信息科学与工程学院, 辽宁 沈阳 110819
李勇:东北大学信息科学与工程学院, 辽宁 沈阳 110819
陈槐嵘:东北大学信息科学与工程学院, 辽宁 沈阳 110819
张庆春:东北大学信息科学与工程学院, 辽宁 沈阳 110819

联系人作者:李勇(842384077@qq.com); 佟国峰(tongguofeng@ise.neu.edu.cn);

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引用该论文

Tong Guofeng,Du Xiance,Li Yong,Chen Huairong,Zhang Qingchun. Three-Dimensional Point Cloud Classification of Large Outdoor Scenes Based on Vertical Slice Sampling and Centroid Distance Histogram[J]. Chinese Journal of Lasers, 2018, 45(10): 1004001

佟国峰,杜宪策,李勇,陈槐嵘,张庆春. 基于切片采样和质心距直方图特征的室外大场景三维点云分类[J]. 中国激光, 2018, 45(10): 1004001

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