机载激光雷达测深相邻条带拼接分层加密匹配算法

doi:10.11947/j.AGCS.2025.20240458

测绘学报 ›› 2025, Vol. 54 ›› Issue (8): 1439-1451.doi: 10.11947/j.AGCS.2025.20240458

机载激光雷达测深相邻条带拼接分层加密匹配算法

普东东¹^,²(), 柴洪洲¹(), 欧阳永忠³, 董超²^,⁴

^1.信息工程大学地理空间信息学院，河南　郑州　450001
^2.自然资源部海洋环境探测技术与应用重点实验室，广东　广州　510300
^3.福建理工大学智慧海洋科学与技术学院，福建　福州　350118
^4.自然资源部南海调查中心，广东　广州　510300

收稿日期:2024-11-11 修回日期:2025-05-14 出版日期:2025-09-16 发布日期:2025-09-16
通讯作者: 柴洪洲 E-mail:pudd2022@163.com;chaihz1969@163.com
作者简介:普东东（1993—），男，博士生，研究方向为海洋大地测量。E-mail：pudd2022@163.com
基金资助:
国家自然科学基金(42074014)

Hierarchical encryption matching algorithm for adjacent strip splicing in airborne LiDAR bathymetry

Dongdong PU¹^,²(), Hongzhou CHAI¹(), Yongzhong OUYANG³, Chao DONG²^,⁴

^1.Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China
^2.Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources of the People's Republic of China, Guangzhou 510300, China
^3.School of Smart Marine and Technology, Fujian University of Technology, Fuzhou, 350118, China
^4.South China Sea Marine Survey Center, Ministry of Natural Resources of the People's Republic of China, Guangzhou 510300, China

Received:2024-11-11 Revised:2025-05-14 Online:2025-09-16 Published:2025-09-16
Contact: Hongzhou CHAI E-mail:pudd2022@163.com;chaihz1969@163.com
About author:PU Dongdong (1993—), male, PhD candidate, majors in marine geodesy. E-mail: pudd2022@163.com
Supported by:
The National Natural Science Foundation of China(42074014)

摘要/Abstract

摘要：

复杂的系统集成误差和海洋环境的高动态性，使得机载激光雷达测深（ALB）相邻条带拼接面临诸多的挑战。已有的方法多基于空间几何特征确定对应关系，缺乏针对对应关系的分析和优化，而潜在的异常对应严重影响了条带拼接的精度，为此本文设计了ALB相邻条带拼接分层加密匹配算法，利用图空间的稳健性和动态优化机制克服潜在的误匹配的影响。首先，计算ALB点云数据的多尺度特征值，筛选出特征值稳定和显著的点作为特征点；其次，基于特征点对相邻条带构建全局图匹配实现二者的粗对齐；然后，在全局匹配的节点邻域内构建加密的局部图模型；最后，采用一对多和双向匹配的动态机制，寻找最佳对匹配关系，移除异常对应点。该算法将ALB相邻条带拼接划分成粗对齐和精配准两个层次，其中粗对齐充分利用了全局图结构的稳定性，精配准采用局部邻域加密图模型的动态优化策略，充分利用局部信息有效增强了对应点的稳健性。试验采用包含不同载体、不同形态的3个区域上的实测点云数据，对比RANSAC-ICP、3D-NDT、MCM算法和基于特征值匹配的算法进行验证，并采用旋转误差、平移误差和总体误差进行精度评估。兼顾精度和效率，试验结果表明本文算法具有明显的优势。

关键词: 机载激光雷达测深, 条带拼接, 分层图匹配, 动态优化, 对应关系

Abstract:

The complex system integration errors and the high dynamism of the marine environment pose numerous challenges for the adjacent strip splicing in airborne LiDAR Bathymetry (ALB). Existing methods primarily rely on spatial geometric features to establish correspondence, lacking thorough analysis and optimization of this correspondence. Potential outliers in the correspondence significantly impact the accuracy of spatial alignment. To address this, a hierarchical encryption matching algorithm for adjacent strip splicing in ALB is proposed. Our algorithm leverages the robustness of graph space and dynamic optimization mechanism to mitigate the effects of potential mismatches. Firstly, calculate the multi-scale features of ALB point cloud and select points with stable and significant features as feature points. Secondly, based on feature points, a global graph matching is constructed to facilitate the coarse alignment between adjacent strips. Then, an encrypted local graph model is constructed within the neighborhood of the globally matched nodes. Finally, a dynamic mechanism featuring one-to-many and bidirectional matching is employed to identify the optimal matching and eliminate outliers of correspondence. Our algorithm divides the process of adjacent ALB strips splicing into two levels: coarse alignment and fine registration. In the coarse alignment phase, the stability of the global graph structure is fully leveraged. For fine registration, a dynamic optimization strategy utilizing a local neighborhood encryption graph model is employed, effectively bolstering the robustness of correspondence by harnessing local information. The experiment employed measured point cloud data from three regions featuring different carriers and shapes. It compared the performance of RANSAC-ICP, 3D-NDT, MCM algorithms, and features matching algorithms for verification purposes. The accuracy was assessed using rotation error, translation error, and overall error. Balancing accuracy and efficiency, the results demonstrate that the algorithm proposed exhibits significant advantages.

Key words: airborne LiDAR bathymetry, strip splicing, hierarchical graph matching, dynamic optimization, correspondence

中图分类号:

P229

普东东, 柴洪洲, 欧阳永忠, 董超. 机载激光雷达测深相邻条带拼接分层加密匹配算法[J]. 测绘学报, 2025, 54(8): 1439-1451.

Dongdong PU, Hongzhou CHAI, Yongzhong OUYANG, Chao DONG. Hierarchical encryption matching algorithm for adjacent strip splicing in airborne LiDAR bathymetry[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(8): 1439-1451.

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数据	RMSE	Max_D	Min_D
区域Ⅰ	0.893	0.921	0.321
区域Ⅱ	1.030	1.372	0.293
区域Ⅲ	0.945	1.376	0.404

数据	δ₁/m	δ₂	δ₃/(个/m³)	δ₄/m
区域Ⅰ	1.3	0.08	0.2	3.7
区域Ⅱ	0.5	0.02	0.3	2.8

数据	方法	对应点数量	RE/rad	TE/m	OE/m
区域Ⅰ	单一尺度	275	0.136	0.201	0.115
区域Ⅰ	多尺度	167	0.103	0.105	0.086
区域Ⅱ	单一尺度	496	0.140	0.173	0.103
区域Ⅱ	多尺度	289	0.091	0.090	0.062

数据	算法	RE/rad	E/m	OE/m
区域Ⅰ	RANSAC	0.251	0.364	0.388
区域Ⅰ	全局图匹配	0.109	0.136	0.105
区域Ⅱ	RANSAC	0.219	0.231	0.250
区域Ⅱ	全局图匹配	0.097	0.102	0.099

数据	方法	对应点数量	RE/rad	TE/m	OE/m
区域Ⅰ	方法1	854	0.046	0.092	0.054
	方法2	854	0.023	0.056	0.035
	方法3	612	0.015	0.040	0.031
区域Ⅱ	方法1	1121	0.038	0.081	0.046
	方法2	1121	0.020	0.045	0.032
	方法3	946	0.011	0.032	0.028

机载激光雷达测深相邻条带拼接分层加密匹配算法

Hierarchical encryption matching algorithm for adjacent strip splicing in airborne LiDAR bathymetry

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参考文献 27

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数据	方法	对应点数量	RE/rad	TE/m	OE/m
区域Ⅲ	全局图匹配	428	0.110	0.125	0.104
	方法1	1467	0.041	0.060	0.051
	方法2	1467	0.036	0.052	0.040
	方法3	1198	0.015	0.039	0.033

数据	算法	RE/rad	TE/m	OE/m
区域Ⅰ	RANSAC-ICP	0.107	0.211	0.319
	3D-NDT	0.214	0.306	0.388
	MCM	0.046	0.060	0.146
	基于特征值匹配	0.063	0.103	0.185
区域Ⅱ	RANSAC-ICP	0.198	0.315	0.350
	3D-NDT	0.301	0.401	0.421
	MCM	0.046	0.090	0.151
	基于特征值匹配	0.033	0.058	0.090
区域Ⅲ	RANSAC-ICP	0.201	0.397	0.296
	3D-NDT	0.285	0.422	0.373
	MCM	0.036	0.088	0.106
	基于特征值匹配	0.028	0.071	0.098

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