无人机载LiDAR点云滤波算法研究  

Research on LiDAR point cloud filtering algorithm for unmanned aerial vehicles

作　　者：程绮霞 罗碧娟 CHENG Qixia;LUO Bijuan(Surveying and Mapping Institute Lands and Resource Department of Guangdong Province,Guangzhou,Guangdong 510663,Chiina;Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China,Ministry of natural resources,Guangzhou,Guangdong 510663,Chiina;Guangdong Science and Technology Collaborative Innovation Center for Natural Resources,Guangzhou,Guangdong 510663,Chiina)

机构地区：[1]广东省国土资源测绘院,广东广州510663 [2]自然资源部华南热带亚热带自然资源监测重点实验室,广东广州510663 [3]广东省自然资源科技协同创新中心,广东广州510663

出　　处：《北京测绘》2024年第11期1541-1546,共6页Beijing Surveying and Mapping

基　　金：广东省科技计划(2021B1111610001)。

摘　　要：针对经典渐进三角网加密(PTD)滤波算法在选择地面种子点时存在随机性,导致点云滤波精度受限的问题,本文提出了一种改进渐进三角网加密(IPTD)滤波算法。在机载点云滤波的实现中,该算法的关键流程包含:首先,采用扩展局部极小值法,在格网内进行深度搜索,精确识别出极小值点,将其作为潜在的地面种子点,从而解决了传统算法中直接将格网内最低点作为地面种子点的问题;其次,采用局部薄板样条函数(TPS)插值法,对格网的高程数据进行高精确度的拟合和计算;最后,基于筛选出的优质种子点,构建不规则三角网(TIN),从而实现点云的精确分类。为了验证本文提出的改进滤波算法的效果,通过设置对比实验,对本文提出的改进滤波算法与传统滤波算法的实验结果进行对比,结果表明,本文提出的改进滤波算法在提升点云滤波精度方面表现出了显著的优势。To address the issue of limited point cloud filtering accuracy due to the randomness in selecting ground seed points by the classical progressive triangulated irregular network(TIN)densification(PTD)filtering algorithm,this paper proposed an improved PTD(IPTD)filtering algorithm.In the implementation of airborne point cloud filtering,the key processes of this algorithm were as follows:Firstly,the extended local minimum method was used to conduct a deep search within the grid and precisely identify the minimum points as potential ground seed points,thus overcoming the limitation of traditional algorithms that directly take the lowest point within the grid as the ground seed point.Secondly,the local thin plate spline(TPS)function interpolation method was adopted to achieve high-precision fitting and calculation of the elevation data within the grid.Finally,based on the screened high-quality seed points,a TIN was constructed to achieve an accurate classification of the point clouds.To verify the effectiveness of the proposed improved filtering algorithm,a comparative experiment was conducted to compare the experimental results of the improved filtering algorithm with those of the traditional filtering algorithm.The results show that the proposed improved filtering algorithm exhibits significant advantages in improving the accuracy of point cloud filtering.

关 键 词：机载激光雷达(LiDAR) 点云滤波 渐进三角网加密(PTD)滤波 薄板样条函数(TPS) 

分 类 号：P231[天文地球—摄影测量与遥感]