Planar Feature-Constrained, Quaternion-Based Registration Algorithm for LiDAR Point Clouds

Yongbo Wang; Nanshan Zheng; Zhengfu Bian

doi:10.3788/AOS202040.2310001

Acta Optica Sinica, Volume. 40, Issue 23, 2310001(2020)

Planar Feature-Constrained, Quaternion-Based Registration Algorithm for LiDAR Point Clouds

Yongbo Wang^1,2、*, Nanshan Zheng^1,2, and Zhengfu Bian^1,2

¹Key Laboratory of Land Environment and Disaster Monitoring, Ministry of Natural Resources, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China

²Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China

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Figures & Tables(9)

Fig. 1. Four-tuple representation of a plane in three-dimensional space

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Fig. 2. Facade LiDAR point clouds of the same building collected from different perspectives. (a) LiDAR point cloud from the reference station; (b) LiDAR point cloud from the un-registered station

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Fig. 3. Visual effects of the point clouds from the two neighboring stations before and after registration. (a) Before registration; (b) after registration

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Fig. 4. Facade LiDAR point clouds of the same part of another building collected from different perspectives. (a) LiDAR point cloud from the reference station; (b) LiDAR point cloud from the un-registered station

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Fig. 5. Visual effects of the point clouds from the two neighboring stations before and after registration. (a) Before registration; (b) after registration

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Table 1. Planar features separately extracted from the reference and the un-registered stations by least square fitting algorithm

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Table 1. Planar features separately extracted from the reference and the un-registered stations by least square fitting algorithm

Station	l_x	l_y	l_z	x	y	z
Reference station	-0.7060	0.7081	-0.0128	-70.7593	-6.3887	26.4681
	-0.7062	0.7079	-0.0108	-50.4330	13.7874	22.2993
	-0.7103	-0.7039	-0.0006	-50.5877	14.9477	22.2911
	-0.0060	0.0090	0.9999	-61.8226	24.8605	25.7601
	-0.7044	0.7097	-0.0113	-63.6772	26.7793	16.8218
	-0.7072	-0.7070	0.0013	-63.2206	27.6485	16.8952
	-0.0024	0.0142	0.9999	-61.5702	25.1852	22.5930
Un-registered station	-0.2579	0.9648	-0.0522	-63.6731	-7.8920	15.1750
	-0.2586	0.9647	-0.0503	-36.1147	-0.4410	16.7593
	-0.9412	-0.2605	-0.2152	-35.7476	0.6642	17.2299
	-0.2194	-0.0081	0.9756	-41.3592	13.9261	19.8014
	-0.2560	0.9654	-0.0508	-40.0006	17.5009	10.9515
	-0.9401	-0.2659	-0.2132	-39.2034	18.0633	11.1137
	-0.2123	-0.0054	0.9772	-40.4619	14.6743	16.7639

Table 2. Registration results and residuals between each pair of conjugate planar features after registration

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Table 2. Registration results and residuals between each pair of conjugate planar features after registration

Algorithm	R	$\|T\|$ /m	Scale factor μ	Δl_x	Δl_y	Δl_z	m_Δ_l	Δm/m	m_Δ_m
Proposed algorithm	$[\begin{array}{l} 0.8504 & - 0.4943 & 0.1802 \\ 0.4790 & 0.8691 & 0.1234 \\ - 0.2176 & - 0.0186 & 0.9759 \end{array}]$	-23.008529.3766-2.2902	1.0000	-0.0004	-0.0004	0.0000	0.0008	0.0012	0.0307
				-0.0004	-0.0004	0.0000		-0.0071
				0.0001	-0.0001	-0.0002		-0.0391
				0.0008	0.0008	0.0000		-0.0352
				-0.0004	-0.0004	0.0005		0.0062
				-0.0007	0.0007	-0.0001		0.0394
				-0.0006	0.0000	0.0000		0.0352
Algorithm in Ref. [8]	$[\begin{array}{l} 0.8504 & - 0.4945 & 0.1796 \\ 0.4794 & 0.8690 & 0.1227 \\ - 0.2168 & - 0.0183 & 0.9761 \end{array}]$	-22.965629.3962-2.2652	1.0004	-0.0002	-0.0002	-0.0001	0.0011	0.0163	0.0428
				-0.0002	-0.0002	-0.0001		0.0079
				-0.0001	0.0001	0.0007		0.0018
				0.0014	0.0015	-0.0000		-0.0627
				-0.0002	-0.0002	0.0004		0.0145
				-0.0009	0.0009	0.0008		0.0802
				-0.0000	0.0007	-0.0000		0.0085

Table 3. Planar features separately extracted from the reference and the un-registered stations by least square fitting algorithm

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Table 3. Planar features separately extracted from the reference and the un-registered stations by least square fitting algorithm

Station	l_x	l_y	l_z	x	y	z
Reference station	-0.0110	-0.0054	0.9999	-12.5529	-38.3620	18.5852
	-0.0762	-0.9971	-0.0063	-8.8728	-35.7275	17.3675
	-0.9877	0.1561	-0.0092	-14.5785	-31.4954	18.4312
	-0.2056	-0.9786	-0.0069	-21.8180	-28.8210	14.7325
	-0.2069	-0.9784	-0.0019	-31.2109	-22.8085	11.7132
	-0.9791	0.2028	-0.0143	-15.7465	-16.3954	0.3823
	-0.0621	-0.3808	-0.9226	-15.9412	-16.2637	0.8802
	-0.0117	-0.0038	0.9999	-17.8117	-31.5307	8.3983
	-0.9867	0.1623	-0.0113	-22.0955	-31.5778	7.0024
Un-registered station	-0.0117	0.0054	0.9999	-37.1151	-16.1259	18.5837
	-0.7944	-0.6073	-0.0062	-32.5529	-17.1743	17.3245
	-0.5419	0.8404	-0.0097	-33.2499	-10.1385	18.3661
	-0.8666	-0.4990	-0.0069	-35.8191	-3.2936	15.4112
	-0.8673	-0.4978	-0.0015	-37.7634	8.0171	11.8171
	-0.5018	0.8649	-0.0144	-22.6710	0.8429	0.3696
	-0.3241	-0.2110	-0.9222	-22.7286	1.0535	0.8693
	-0.0111	0.0069	0.9999	-35.2226	-8.1253	8.3990
	-0.5393	0.8420	-0.0118	-38.4293	-4.6610	7.1060

Table 4. Registration results and residuals between each pair of conjugate planar features after registration

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Table 4. Registration results and residuals between each pair of conjugate planar features after registration

Algorithm	R	$\|T\|$ /m	Scale factor μ	Δl_x	Δl_y	Δl_z	m_Δ_l	Δm/m	m_Δ_m
Proposed algorithm	$[\begin{array}{l} 0.6670 & - 0.7451 & 0.0009 \\ 0.7451 & 0.6670 & - 0.0004 \\ - 0.0003 & 0.0010 & 1.0000 \end{array}]$	0.0058-0.08080.0137	0.9979	-0.0001	0.0001	-0.0000	0.0015	0.0252	0.0315
				0.0012	-0.0001	0.0003		0.0013
				-0.0001	-0.0007	-0.0005		0.0316
				0.0006	-0.0001	0.0002		0.0018
				0.0007	-0.0001	-0.0002		-0.0110
				-0.0000	-0.0002	-0.0009		0.0350
				-0.0023	0.0010	-0.0003		0.0240
				-0.0001	0.0003	0.0000		-0.0030
				0.0004	0.0025	-0.0005		-0.0662
Algorithm in Ref.[8]	$[\begin{array}{l} 0.6663 & - 0.7457 & 0.0005 \\ 0.7457 & 0.6663 & - 0.0002 \\ - 0.0001 & 0.0005 & 1.0000 \end{array}]$	-0.0014-0.00640.0011	1.0000	-0.0001	0.0001	-0.0000	0.0017	-0.0014	0.0440
				0.0012	-0.0001	0.0003		-0.0009
				-0.0001	-0.0007	-0.0005		-0.0067
				0.0006	-0.0001	0.0002		0.0049
				0.0007	-0.0001	-0.0002		0.0001
				-0.0000	-0.0002	-0.0009		-0.0126
				-0.0023	0.0010	-0.0003		0.0271
				-0.0001	0.0003	0.0000		-0.0085
				0.0004	0.0025	-0.0005		-0.1203

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Yongbo Wang, Nanshan Zheng, Zhengfu Bian. Planar Feature-Constrained, Quaternion-Based Registration Algorithm for LiDAR Point Clouds[J]. Acta Optica Sinica, 2020, 40(23): 2310001

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Paper Information

Category: Image Processing

Received: Jul. 6, 2020

Accepted: Aug. 3, 2020

Published Online: Nov. 23, 2020

The Author Email: Wang Yongbo (ybwang816@163.com)

DOI:10.3788/AOS202040.2310001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology