基于单木位置特征的多源树木三维点云配准方法

doi:10.11707/j.1001-7488.20221109

Abstract

Abstract:

Objective: The difficulties and the large registration error of the 3D point cloud registration in forested area present a challenge. Taking different types of forest point cloud data from different perspectives and platforms (ground-based and airborne) as input, and aiming at high-precision fusion of forest point cloud data, an automatic marker-less registration algorithm of point cloud for multi-types of data under forest semantic environment was proposed, focusing on point-pair matching with the single wood object as semantic feature. Method: Firstly, according to the data characteristics of different types of point clouds, the tree positions are extracted respectively. For the side view (ground-based) point cloud, based on the deviation between the main direction dispersion and the vertical angle of the main direction of the point cloud, non-trunk point cloud is eliminated, and the connected component segmentation method of voxel-based partition is used to identify the trunk of individual tree, and the place with the maximum point density of voxel vertical distribution of the single tree trunk point cloud is determined as the planar position of the tree. For the top view (airborne) point cloud, the canopy height model (CHM) is extracted, and then the marker controlled watershed method is used to segment CHM to extract individual trees and the canopy vertex is identified as the planar position of individual trees. Then, taking the extracted positions of individual trees as feature points, the registration matrix is obtained based on Laplace spectral map matching theory, and the 4-DoF (degree of freedom) point cloud coarse registration is completed. Finally, the side view point clouds are precisely registered by using the main trunk matching and the side view and top view point clouds are precisely aligned by using the global iterative closest points (ICP) method. Result: The experimental result show that the registration accuracy between point cloud obtained from ground perspective views is better than that of ground side view and airborne top views point clouds. The average coarse registration error between point clouds from both ground perspective was 0.24 m, and the average error of precision registration was 0.08 m. The average error of coarse registration between side view and top views point cloud was 1.07 m, and the average error of global ICP is 0.44 m. After airborne point cloud and side-looking point cloud are matched through the main trunk, the average error is further improved to 0.36 m. Conclusion: This study followed from coarse to fine registration routine, integrating many kinds of individual tree detection algorithms designed for ground-based and airborne point cloud. We design a framework for multiple source types of point cloud registration, and the experimental result prove the feasibility of the scheme, which is suitable for various forested environment. The marker-less automatic registration algorithm applicable to multi-type point cloud data provides precision and more complete data that is critical to forest resource investigation and evaluation.

Key words: point cloud, registration, tree segmentation, feature extracting, graph matching

CLC Number:

Hongyu Huang,Yubo Luo,Liyu Tang,Xiaoxiao Li,Wei Peng,Chongcheng Chen. Registration of Point Cloud from Different Platforms in Forested Area Based on Tree Position Features[J]. Scientia Silvae Sinicae, 2022, 58(11): 96-107.

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References 0

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样地 Plot	郁闭度 Canopy density(%)	主要树种 Main species	树高 Tree height/ m	树木间距 Distance between trees/m	坡度 Slope/(°)	点云类型 Point cloud type
样地1 Plot 1	60	大叶桉Eucalyptus robusta	15~20	1~2	20	ALS/TLS/MLS
样地2 Plot 2	40	罗汉松、桂花 Podocarpus macrophyllus，Osmanthus sp.	5~10	1~2	20	UAV-IPC/TLS

参数Parameters	Riegl VZ-400	LiBackpack 50	蜂鸟无人机激光雷达 Genius UAV LiDAR
最大测量距离 Max. range/m	400(目标反射率Target reflectivity ρ≥20%)	100	200(ρ=20%)
回波数 Number of echoes	多次 More than once	双回波 Double echo	双回波 Double echo
精度 Precision/m	0.005	0.05	0.1
激光波长 Wavelength/×10^-9 m	1 064	903	905
视场角(水平×垂直) FOV(horizontal×vertical)	360°×100°	360°×30°	360°×24°
平均点云密度 Point density/m^-2	2 000	300	90

样地 Plot	点云类型 Point cloud type	总数 Sum	错检数 Number of fault detection	漏检数 Number of the missed detection	错检率 Fault detection rate(%)	漏检率 Missed detection rate(%)
样地1 Plot 1	TLS1	148	5	15	3.4	9.5
	TLS2	128	1	20	0.8	13.6
	MLS	145	0	28	0	16.2
	ALS	130	27	36	20.8	25.9
样地2 Plot 2	TLS1	157	1	4	0.6	2.5
	TLS2	107	1	9	0.9	7.8
	TLS3	165	4	16	2.4	9.0
	UAV-IPC	682	90	39	13.2	6.2

样地 Plot	视角 View angle	站位 Station	传感器 Sensors	粗配准 Coarse registration/m	全局ICP Global ICP/m	主干点云匹配 Backbone point cloud matching/m
样地1 Plot 1	侧视/俯视 Side/top view	1	ALS-MLS	1.10	0.83	0.32
		2	ALS-TLS1	1.43	0.66	0.34
		3	ALS-TLS2	2.77	0.48	0.41
			均值Mean	1.77	0.66	0.36
	侧视/侧视 Side/side view	1	TLS1-TLS2	0.35		0.09
		2	MLS-TLS1	0.13		0.07
		3	MLS-TLS2	0.25		0.16
			均值Mean	0.24		0.11
样地2 Plot 2	侧视/俯视 Top/top view	4	UAV-TLS1	0.30	0.27	0.28
		5	UAV-TLS2	0.46	0.17	0.25
		6	UAV-TLS3	0.38	0.17	0.21
			均值Mean	0.38	0.24	0.25
	侧视/侧视 Side/side view	4	TLS1-TLS2	0.11		0.03
		5	TLS2-TLS3	0.36		0.07
		6	TLS1-TLS3	0.24		0.09
			均值Mean	0.24		0.06

样地 Plot	视角 View angle	站位 Station	传感器 Sensors	x差值 x difference/m	y差值 y difference/m	z差值 z difference/m	角度x差值 Angle x difference/(°)	角度y差值 Angle y difference/(°)	角度z差值 Angle z difference/(°)
样地1 Plot 1	侧视/俯视 Side/top view	1	ALS-MLS	0.15	1.18	3.74	2.01	6.21	0.81
		2	ALS-TLS1	0.30	0.07	0.33	0.06	0.96	0.80
		3	ALS-TLS2	0.07	0.23	0.50	0.00	0.84	0.17
			均值Mean	0.17	0.49	1.52	0.69	2.67	0.59
	侧视/侧视 Side/side view	1	TLS1-TLS2	0.00	0.03	0.23	0.47	0.83	0.13
		2	MLS-TLS1	0.04	0.10	1.31	0.32	3.52	0.25
		3	MLS-TLS2	0.01	0.07	0.60	0.20	1.60	0.13
			均值Mean	0.02	0.07	0.71	0.33	1.98	0.17
样地2 Plot 2	侧视/俯视 Top/top view	4	UAV-TLS1	0.10	0.03	0.25	0.11	0.50	0.13
		5	UAV-TLS2	0.22	0.08	0.24	0.81	0.13	0.16
		6	UAV-TLS3	0.13	0.00	0.22	0.11	0.69	0.44
			均值Mean	0.15	0.04	0.24	0.34	0.44	0.24
	侧视/侧视 Side/side view	4	TLS1-TLS2	0.01	0.02	0.09	0.57	0.30	0.04
		5	TLS2-TLS3	0.04	0.01	0.21	0.46	0.30	0.08
		6	TLS1-TLS3	0.01	0.02	0.14	0.35	0.09	0.02
			均值Mean	0.02	0.01	0.15	0.46	0.23	0.05

Registration of Point Cloud from Different Platforms in Forested Area Based on Tree Position Features

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