广东工业大学学报

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引入栅格连续性约束的单光子激光雷达点云信号提取

李欣宇1, 余俊鹏1, 吴伟东2   

  1. 1. 广东工业大学 土木与交通工程学院, 广东 广州 510006;
    2. 广东省国土资源测绘院, 广东 广州 510000
  • 收稿日期:2023-11-03 出版日期:2024-09-27 发布日期:2024-09-27
  • 通信作者: 余俊鹏(1982–)，男，高级工程师，博士，主要研究方向为卫星摄影测量与遥感技术，E-mail：strength701@163.com
  • 作者简介:李欣宇(1996–)，女，硕士，主要研究方向为摄影测量与遥感技术，E-mail：2446060811@qq.com
  • 基金资助:
    国家自然科学基金资助项目(41704019)

A Grid-Continuity-Constraint Method for Extracting Single-Photon Lidar Point Cloud

Li Xin-yu1, Yu Jun-peng1, Wu Wei-dong2   

  1. 1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China;
    2. Guangdong Institute of Land Resources Surveying and Mapping, Guangzhou 510000, China
  • Received:2023-11-03 Online:2024-09-27 Published:2024-09-27

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摘要： 以ICESat-2/ATLAS为代表的单光子激光雷达具有观测灵敏度高、背景噪声大的特点，需要合适的滤波方法去除噪声点。本文基于点云密度去噪原理，提出了一种自适应光子点云信号提取方法。该方法首先根据地表地形对点云进行栅格划分，以栅格为单元对点云进行连续性检验，实现点云粗滤波；利用K均值聚类算法和布料模拟滤波完成点云精细分类和光子高程点提取。对ATL03光子点云实验结果表明，本文方法对于不同地形变化的点云数据均可取得良好的处理效果，信号点召回率(Recall)、精确率(Precision)及F值分别达到99.0%、99.9%、99.5%。基于参考点云的配准实验结果表明，三组光子高程点的高程中误差分别为0.960 m、0.957 m、0.872 m，优于官方提供的ATL08对照组。

关键词: 单光子激光雷达, ICESat-2, 点密度, K均值聚类, CSF算法

Abstract: The existing single-photon LiDAR, such as ICESat-2/ATLAS, are with high observation sensitivity and significant background noise, which usually require effective filtering methods for removing noise. This paper proposes an improved adaptive photon point cloud signal extraction method based on the principle of point cloud density denoising. The proposed method first adaptively determines grid width and height according to the point cloud distribution characteristics to partition point cloud data into grids. Then, it conducts grid continuity tests as units for point cloud filtering. Finally, it employs K-means clustering and cloth simulation filter algorithms to accurately extract reliable vertical control points. Experimental results on ATL03 photon point clouds show that the proposed method achieves promising effectiveness for point cloud data with different terrain variations by achieving approximately 99.0%, 99.9%, and 99.5% in terms of signal point recall rate (Recall) , precision rate (Precision) , and F-measure, respectively. From the registration experimental results with reference point clouds, the elevation errors of photon elevation points are 0.960m, 0.957m, and 0.872m, respectively, outperforming the official ATL08 control group provided by the authorities.

Key words: single-photon lidar, ICESat-2, point density, K-means clustering, CSF (Cloth Simulaton Filter)

中图分类号: 

  • TP79
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[1] 李云, 鲍鸿. 语音分组识别技术的研究[J]. 广东工业大学学报, 2014, 31(2): 54-57.
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