室内障碍物点云分割的可变阈值联合聚类算法研究  

Research on variable threshold joint clustering algorithm for indoor obstacle point cloud segmentation

作　　者：刘洪凯 王少红[1] 左云波[1] 谷玉海[1] Liu Hongkai;Wang Shaohong;Zuo Yunbo;Gu Yuhai(Key Laboratory of Modern Measurement&Control Technology,Ministry of Education,Beijing Information Science and Technology University,Beijing 100192,China)

机构地区：[1]北京信息科技大学现代测控技术教育部重点实验室,北京100192

出　　处：《电子测量技术》2024年第9期70-78,共9页Electronic Measurement Technology

基　　金：国家重点研发计划(2020YFB1713203)项目资助。

摘　　要：激光雷达点云分割技术在智能车辆环境识别中扮演着重要角色。由于激光雷达存在点云近密远疏、分布不均匀的问题以及存在噪点的情况,导致出现点云分割不准确的现象。针对上述问题,提出了一种可变阈值联合聚类算法。该方法首先对点云数据进行预处理,使用直通滤波、体素滤波和立方体滤波对点云进行提取、稀疏和降噪,再联合自适应DBSCAN算法和改进后的可变阈值欧式聚类算法对点云进行聚类分割。采集真实场景数据进行测试,结果显示,在C-H系数、轮廓系数、D-B系数及轮廓系数等评价指标上均有所提高。这表明,可变阈值联合聚类算法显著提高了点云分割的准确性,有效的提高了聚类结果的类内一致性和类间差异性,为目标检测和识别提供了更可靠的基础。Lidar point cloud segmentation technology plays an important role in intelligent vehicle environment recognition.Due to the problems of near dense and far sparse point clouds,uneven distribution,and the presence of noise in LiDAR,inaccurate point cloud segmentation occurs.A self adaptation DBSCAN with Euclidean joint clustering algorithm is proposed to address the above issues.This method first preprocesses the point cloud data,using through filtering,voxel filtering,and cube filtering to extract,sparse,and denoise the point cloud.Then,it combines the adaptive DBSCAN algorithm and an improved variable threshold Euclidean clustering algorithm to cluster and segment the point cloud.Real scene data was collected for testing,and the results showed improvements in evaluation indicators such as C-H coefficient,contour coefficient,D-B coefficient,and contour coefficient.This indicates that the variable threshold joint clustering algorithm significantly improves the accuracy of point cloud segmentation,effectively improves the intra class consistency and inter class differences of clustering results,and provides a more reliable foundation for object detection and recognition.

关 键 词：激光雷达 障碍物检测 点云分割 DBSCAN 

分 类 号：TN959.5[电子电信—信号与信息处理] TP393[电子电信—信息与通信工程]