顾及地形特征的大规模机载LiDAR点云高效滤波方法

doi:10.11947/j.AGCS.2025.20240484

测绘学报 ›› 2025, Vol. 54 ›› Issue (6): 1082-1093.doi: 10.11947/j.AGCS.2025.20240484

顾及地形特征的大规模机载LiDAR点云高效滤波方法

徐联中¹(), 陈传法¹(), 陈东兴², 王兴杰¹, 杨子明¹, 杨淑凡¹, 洪壮壮¹, 郝劲达¹

^1.山东科技大学测绘与空间信息学院，山东　青岛　266590
^2.山东省煤田地质局第二勘探队，山东　济宁　272100

收稿日期:2024-11-30 修回日期:2025-04-28 出版日期:2025-07-14 发布日期:2025-07-14
通讯作者: 陈传法 E-mail:XU_Lianzhong@163.com;chencf@sdust.edu.cn
作者简介:徐联中（1999—），男，硕士生，研究方向为三维点云智能处理。E-mail：XU_Lianzhong@163.com
基金资助:
国家自然科学基金(42271438);山东省自然科学基金(ZR2024MD040)

An efficient filtering method considering terrain features for large-scale airborne LiDAR point clouds

Lianzhong XU¹(), Chuanfa CHEN¹(), Dongxing CHEN², Xingjie WANG¹, Ziming YANG¹, Shufan YANG¹, Zhuangzhuang HONG¹, Jinda HAO¹

^1.College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
^2.Shandong Coal Geology Bureau Second Exploration Team, Jining 272100, China

Received:2024-11-30 Revised:2025-04-28 Online:2025-07-14 Published:2025-07-14
Contact: Chuanfa CHEN E-mail:XU_Lianzhong@163.com;chencf@sdust.edu.cn
About author:XU Lianzhong (1999—), male, postgraduate, majors in intelligent processing of 3D point clouds. E-mail: XU_Lianzhong@163.com
Supported by:
The National Natural Science Foundation of China(42271438);Shandong Provincial Natural Science Foundation, China(ZR2024MD040)

摘要/Abstract

摘要：

针对现有滤波方法在复杂场景区大规模点云数据滤波精度低、计算效率慢，以及地形自适应差等难题，本文提出了一种顾及地形特征的大规模机载LiDAR点云高效滤波方法。首先，采用半径滤波和高程直方图法对原始点云预处理以剔除异常值；其次，借助滑动网格技术与M估计样本一致性算法相结合的策略，高效提取大量均匀分布的精确地面种子点；然后，根据地面点构建地面参考面网格，并采用基于网格的全局加权有限差分薄板样条方法快速生成地面参考面；最后，设计一种顾及地形特征的自适应高程阈值，捕捉各种类型地面点，进而完成点云滤波。为验证本文方法的有效性，采用大规模地面滤波数据集OpenGF进行试验分析。结果表明，本文方法平均总误差和Kappa系数分别为2.45%、94.54%，整体性能优于10种代表性滤波方法；同时，本文滤波方法计算效率也具有显著优势。

关键词: 点云滤波, 机载LiDAR, 地形自适应, 薄板样条

Abstract:

To solve the problems of low accuracy, slow computing speed and poor accuracy of the existing filtering methods for large-scale point cloud data in complex landscapes, an efficient filtering method considering terrain features for large-scale airborne LiDAR point clouds is proposed in this paper. Firstly, radius filtering and elevation histogram method are used to remove outliers from the raw point cloud. Secondly, a large number of evenly distributed ground seed points are efficiently extracted by combining the sliding grid with the M-estimation sample consistency algorithm. Then, the ground reference surface is constructed using a global weighted finite difference thin plate spline (TPS). Finally, a terrain-adaptive elevation threshold is designed to capture various types of ground points. In order to verify the effectiveness of the proposed method, a large-scale point cloud named OpenGF is used in the experiments. The results show that the average total error and Kappa coefficient of the proposed method are 2.45% and 94.54%, respectively, and its overall performance is much better than those of the 10 representative filtering methods. Moreover, the proposed method has a high computational efficiency.

Key words: point cloud filtering, airborne LiDAR, terrain-adaptive, thin plate spline

中图分类号:

P237

徐联中, 陈传法, 陈东兴, 王兴杰, 杨子明, 杨淑凡, 洪壮壮, 郝劲达. 顾及地形特征的大规模机载LiDAR点云高效滤波方法[J]. 测绘学报, 2025, 54(6): 1082-1093.

Lianzhong XU, Chuanfa CHEN, Dongxing CHEN, Xingjie WANG, Ziming YANG, Shufan YANG, Zhuangzhuang HONG, Jinda HAO. An efficient filtering method considering terrain features for large-scale airborne LiDAR point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(6): 1082-1093.

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样本区域	样本特征	地面点数	非地面点数	点云密度/（pts/m²）	平均地形坡度/（°）
Test 1	农村、城市、混合地形	20 599 329	26 233 254	7.09	19.30
Test 2	大型建筑物	3 095 510	3 135 353	5.41	3.77
Test 3	陡坡上的密集植被、梯田	3 602 430	16 161 901	7.19	16.16

样本区域	参数				精度
样本区域	W_s/m	h/m	k	t₀/m	T_Ⅰ/（%）	T_Ⅱ/（%）	T_E/（%）	Kappa/（%）
Test 1	12	1.00	0.10	0.06	1.73	1.84	1.79	96.37
Test 2	90	1.00	0.50	0.01	1.35	6.44	3.91	92.18
Test 3	14	1.00	0.15	0.06	2.69	1.42	1.65	94.54
平均值					1.92	3.23	2.45	94.37
标准差					0.56	2.28	1.03	1.72

样本区域	指标	本文方法	SMRF	PTD	MASF	PMF	MCC	CSF	MHC	SegBF	SBF	MSF
Test 1	总误差	1.79	2.24	5.19	3.21	5.39	3.67	8.57	5.99	5.58	4.82	12.35
Test 1	Kappa系数	96.37	95.45	89.49	93.52	89.00	92.55	82.61	87.91	88.78	90.32	75.56
Test 2	总误差	3.91	6.98	6.35	9.94	8.94	15.53	12.48	15.64	23.90	23.61	17.64
Test 2	Kappa系数	92.18	86.05	87.29	80.12	82.11	69.80	80.27	68.77	52.29	52.88	64.71
Test 3	总误差	1.65	1.93	2.52	2.01	3.47	3.03	5.61	3.26	3.00	4.15	7.57
Test 3	Kappa系数	94.54	93.57	91.47	93.45	88.17	89.55	81.28	89.46	90.37	86.99	75.68
平均值	总误差	2.45	3.72	4.69	5.05	5.93	7.41	8.02	8.30	10.83	10.86	12.52
平均值	Kappa系数	94.37	91.69	89.42	89.03	86.43	83.97	81.39	82.05	77.15	76.73	71.98

样本区域	本文方法	SMRF	PTD	MASF	PMF	MCC	CSF	MHC	SegBF	SBF	MSF
Test 1	7.62	21.60	4.53	135.63	143.50	77.00	27.65	599.36	17.24	3.82	1.18
Test 2	1.87	3.63	0.45	16.46	40.99	1 715.00	3.36	224.97	2.14	0.38	0.26
Test 3	4.25	6.32	1.28	29.83	81.80	71.12	10.90	288.46	7.20	0.78	0.39
平均值	4.58	10.52	2.09	60.64	88.76	621.04	13.97	370.93	8.86	1.66	0.61

样本区域	本文方法	无贡献点①	无贡献点②	无贡献点③
Test 1	1.67	2.13	1.95	1.78
Test 2	4.23	4.95	4.37	5.39
Test 3	1.80	2.31	2.31	1.98

顾及地形特征的大规模机载LiDAR点云高效滤波方法

An efficient filtering method considering terrain features for large-scale airborne LiDAR point clouds

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