机载激光测深数据配准方法比较  

Comparison of airborne LiDAR bathymetry data registration methods

作　　者：张凡 徐文学 唐玲 王芳[3] 原峰 张敏 ZHANG Fan;XU Wenxue;TANG Ling;WANG Fang;YUAN Feng;ZHANG Min(Guangdong Center for Marine Development Research,Guangzhou 510220;First Institute of Oceanography,Ministry of Natural Resources,Qingdao 266061;State Oceanic Administration North Sea Marine Technology Support Center,Qingdao 266033)

机构地区：[1]广东省海洋发展规划研究中心,广州510220 [2]自然资源部第一海洋研究所,青岛266061 [3]国家海洋局北海海洋技术保障中心,青岛266033

出　　处：《南京信息工程大学学报（自然科学版）》2021年第6期678-685,共8页Journal of Nanjing University of Information Science & Technology（Natural Science Edition）

基　　金：国家自然科学基金(41871381,41401573);中央级公益性科研院所基本科研业务费专项资金(2015P13);2021年度广东省海洋综合管理专项资金。

摘　　要：机载激光测深(Airborne LiDAR Bathymetry,ALB)系统可以快速高效地获取海岛礁及其邻近区域的水上水下一体化数据,但是由于测量区域大部分位于地势变化缓慢的近岸浅水水域,点云密度低、厚度大,配准特征稀少,同名特征提取困难.针对机载激光测深数据的配准研究工作相对较少.本文以我国南海海域的机载激光测深点云为试验对象,比较基于不同几何特征的ALB点云数据配准方法,通过配准精度指标对快速点特征直方图(Fast Point Feature Histograms,FPFH)、最长公共子序列(Longest Common Subsequence,LCSS)和广义迭代最近邻点(Generalized Iterative Closest Point,GICP)三种配准方法进行评定.试验结果表明,LCSS线序列方法实现ALB点云数据配准方法的可靠性更高,能够克服对应特征匹配过程中信息单一以及噪声问题,提高特征曲线中对应点的稳健估计,增强航带数据配准的鲁棒性,是ALB数据配准的一种有效解决方案.Airborne LiDAR Bathymetry(ALB)system can quickly and efficiently obtain the integrated overwater and underwater data of sea islands,reefs and their adjacent areas.However,due to the fact that most of the measurement areas are shallow near-shore waters with slow terrain changes,the obtained point cloud is low in density and large in thickness,resulting in rare registration characteristics.Few studies have been done on the registration of ALB data due to the difficulty in extracting their homonymous features.To address this problem,we employ three registration methods including Fast Point Feature Histograms(FPFH),Longest Common Subsequence(LCSS)and Generalized Iterative Closest Point(GICP)to register the ALB point cloud data in the South China Sea.The registration performance comparison shows that the LCSS line sequence outperforms the other two methods in registration accuracy and reliability.Moreover,the LCSS can tackle the problems of single information and noise in the corresponding feature matching,improve the robust estimation of corresponding points in the feature curve,and enhance the robustness of airstrip data registration.It can be concluded that the LCSS is an effective solution for ALB data registration.

关 键 词：机载激光测深 点云配准 快速点特征直方图(FPFH) 最长公共子序列(LCSS) 广义迭代最近邻点(GICP) 

分 类 号：P229.1[天文地球—大地测量学与测量工程]