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

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.