基于改进DBSCAN的单光子激光点云去噪算法

Single-photon laser point cloud noise reduction algorithm based on improved DBSCAN

针对单光子激光雷达系统观测数据中背景噪声过多的问题,文中提出了一种基于改进DBSCAN的单光子激光点云去噪算法。以ICESat-2卫星在工作运行期间所采集的ATL03数据为实验数据,通过设置合理的阈值进行粗去噪,然后基于改进DBSCAN聚类的算法对点云数据进行精去噪,并进行理论分析和实验验证。结果表明,实验区目标点云去噪精度达到99.11%,性能优于传统滤波算法。

Aiming at the problem of the excessive background noise in single-photon lidar system observation data,in this paper,a single-photon laser point cloud noise reduction algorithm was proposed based on improved DBSCAN.Taking ATL03 data collected during the operation of ICESat-2 satellite as the experimental data,the coarse noise reduction was performed by setting a reasonable threshold,and then the fine noise reduction of the point cloud data was performed based on the improved DBSCAN clustering algorithm,and theoretical analysis and experimental verification were carried out.Results showed that the noise reduction accuracy of the target point cloud reached 99.11%in the experimental area,which was better than the traditional filtering algorithm.