基于改进密度聚类的三维激光雷达点云滤波算法研究  

3DLiDAR Based on Improved Density Clustering Research on Point Cloud Filtering Algorithm

作　　者：陶泽宇 苏建强 董朝轶[1,2] 单馨平 Tao Zeyu;Su Jianqiang;Dong Chaoyi;Shan Xinping(School of Electric Power Inner Mongolia University of Technology,Hohhot 010080,Inner Mongolia,China;Inner Mongolia Key Laboratory of Electrical and Mechanical Control,Hohhot 010051,Inner Mongolia,China)

机构地区：[1]内蒙古工业大学电力学院,内蒙古呼和浩特010080 [2]内蒙古自治区机电控制重点实验室,内蒙古呼和浩特010051

出　　处：《应用激光》2023年第7期87-93,共7页Applied Laser

基　　金：内蒙古自治区科技攻关项目(2021GG0256);内蒙古自治区自然科学基金项目(2022LHMS06007)。

摘　　要：三维激光雷达是无人驾驶、机器人等领域环境感知部分的重要组成,其扫描周围环境获取的点云数据中存在大量离群点等噪声数据。针对噪声点滤除不准确的问题,创新地提出一种改进密度聚类(DBSCAN)算法,利用栅格化网络寻找点云密度最大栅格,并在其中筛选初始点,自适应调整聚类半径和参数,最后完成聚类滤波。试验结果表明,该算法在保留原始点云特征的情况下有效滤除了离群点,在滤波时间基本不变的情况下,效果优于其他传统的滤波算法。3DLiDAR is an important part of the environment perception in the fields of unmanned driving and robots.There is a large number of noise data such as outliers in the point cloud data obtained by scanning the surrounding environment.Aiming at the problem of inaccurate filtering of noise points,this paper innovatively proposes an improved density clustering(DBSCAN)algorithm,which uses a grid network to find the grid with the highest density of point clouds,selects the initial points in it,and adjusts the clustering radius and parameters adaptively,and finally complete the clustering filtering.The experimental results show that the algorithm can effectively filter out outliers while retaining the original point cloud features,and the effect is better than other traditional filtering algorithms when the filtering time is basically unchanged.

关 键 词：三维激光雷达 点云数据 DBSCAN 统计滤波 半径阈值滤波 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]