Accurate Registration of 3D Point Clouds Based on Keypoint Extraction and Improved Iterative Closest Point Algorithm

Zhen Peng; Yuanjian Lü; Chao Qu; Dahu Zhu

doi:10.3788/LOP57.061002

Laser & Optoelectronics Progress, Volume. 57, Issue 6, 061002(2020)

Accurate Registration of 3D Point Clouds Based on Keypoint Extraction and Improved Iterative Closest Point Algorithm

Zhen Peng^1,2, Yuanjian Lü^1,2, Chao Qu^1,2, and Dahu Zhu^1,2、*

¹Hubei Key Laboratory of Advanced Automotive Components Technology, Wuhan University of Technology, Wuhan, Hubei 430070, China

²Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, Hubei 430070, China

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Figures & Tables(16)

Fig. 1. Flowchart of registration of point clouds P and Q

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Fig. 2. Keypoint extraction. (a)Voxel grid filtering; (b) extracting keypoints using normal distance

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Fig. 3. Distribution of keypoints under different parameters. (a) a₀=0.3 mm, r=1.0 mm, t_hr=10%, m=5, the number of keypoints is 658; (b) a₀=0.4 mm, r=2.0 mm, t_hr=10%, m=5, the number of keypoints is 597; (c) a₀=0.4 mm, r=2.0 mm, t_hr=10%, m=10, the number of keypoints is 364

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Fig. 4. Different nearest point models. (a) “Point to point” model; (b) “point to triangle plane” model

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Fig. 5. Coarse registration of model point clouds. (a) Feature matching; (b) correct correspondences after improved RANSAC; (c) results of coarse registration

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Fig. 6. Coarse registration of building point clouds. (a) Feature matching; (b) correct correspondences after improved RANSAC; (c) results of coarse registration

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Fig. 7. Fine registration of model point clouds. (a) Results of fine registration by proposed method; (b) chromatographic comparison of point clouds distance deviation under fine registration; (c) registration error comparison of fine registration among different methods

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Fig. 8. Fine registration of building point clouds. (a) Results of fine registration by proposed method; (b) chromatographic comparison of point clouds distance deviation under fine registration; (c) registration error comparison of fine registration among different methods

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Fig. 9. Registration experiment comparison of Gaussian noise point clouds under different methods. (a) Bunny; (b) happy; (c) armadillo

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Fig. 10. Registration results of different point clouds with Gaussian noise σ=0.02 in proposed method. (a) Bunny; (b) happy; (c) armadillo

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Table 1. Coarse registration results of model point clouds

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Table 1. Coarse registration results of model point clouds

Dataset	Size ofpoint cloud	Numberof keypoints	Number ofcorrespondences	Number of correctcorrespondenses	R_MS/mm
Happy024Happy048	7558269158	433381	102	75	0.44
Dragon120Dragon144	2183323530	382411	80	53	1.04
Armadillo15Armadillo45	3220824813	405371	97	64	0.96

Table 2. Comparison of coarse registration results of model point clouds by different methods

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Table 2. Comparison of coarse registration results of model point clouds by different methods

Method	Happy		Armadillo		Dragon
Method	Time /s	R_MS /mm	Time /s	R_MS /mm	Time /s	R_MS /mm
Uniform+FPFH+SAC-IA	42.90	2.06	6.40	1.64	4.48	1.14
NARF+FPFH+SAC-IA	28.67	2.18	16.40	1.76	8.43	1.91
ISS+FPFH+SAC-IA	17.80	2.53	9.09	1.15	10.17	1.02
KFPCS	6.70	1.29	3.28	1.03	2.71	1.09
Proposed method	1.23	0.44	1.80	0.96	0.61	1.04

Table 3. Coarse registration results of building point clouds

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Table 3. Coarse registration results of building point clouds

Dataset	Size ofpoint cloud	Number ofkeypoints	Number ofcorrespondences	Number of correctcorrespondences	R_MS / (10^-2 m)
Dagstuhl000Dagstuhl001	8135981360	453404	113	46	2.07
Hokuyo_0Hokuyo_1	370261370277	26953283	565	123	1.82

Table 4. Comparison of coarse registration results of building point clouds by different methods

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Table 4. Comparison of coarse registration results of building point clouds by different methods

Method	Dagstuhl		Hokuyo
Method	Time /s	R_MS /m	Time /s	R_MS /m
Uniform+FPFH+SAC-IA	27.20	0.0415	93.80	0.0267
NARF+FPFH+SAC-IA	4.33	0.0360	63.70	0.0439
ISS+FPFH+SAC-IA	12.04	0.0239	77.40	0.0206
KFPCS	5.23	0.0249	29.70	0.0279
Proposed method	0.72	0.0207	12.09	0.0182

Table 5. Comparison of fine registration results of model point clouds under different methods

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Table 5. Comparison of fine registration results of model point clouds under different methods

Method	Happy		Armadillo		Dragon
Method	Time /s	R_MS /mm	Time /s	R_MS /mm	Time /s	R_MS /mm
Standard ICP	18.90	0.082	7.60	0.170	5.80	0.230
GICP	32.23	0.055	15.10	0.091	11.50	0.167
LM-ICP	20.24	0.079	6.97	0.150	10.17	0.183
NDT	5.30	0.087	2.35	0.150	1.69	0.180
Proposed method	11.2	0.053	6.47	0.084	4.70	0.173

Table 6. Comparison of fine registration results of building point clouds by different methods
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Table 6. Comparison of fine registration results of building point clouds by different methods
Method Dagstuhl Hokuyo
Time /s R_MS /(10^-3 m) Time /s R_MS /(10^-3 m)
Standard ICP 15.70 4.77 105.6 2.82
GICP 23.97 4.31 121.7 2.43
LM-ICP 60.60 4.52 203.6 4.56
NDT 10.67 5.73 49.6 2.57
Proposed method 13.60 3.58 70.9 1.61

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Zhen Peng, Yuanjian Lü, Chao Qu, Dahu Zhu. Accurate Registration of 3D Point Clouds Based on Keypoint Extraction and Improved Iterative Closest Point Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061002

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Paper Information

Category: Image Processing

Received: Jun. 17, 2019

Accepted: Aug. 20, 2019

Published Online: Mar. 6, 2020

The Author Email: Dahu Zhu (dhzhu@whut.edu.cn)

DOI:10.3788/LOP57.061002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Coarse registration results of model point clouds

Table 1. Coarse registration results of model point clouds

Table 2. Comparison of coarse registration results of model point clouds by different methods

Table 2. Comparison of coarse registration results of model point clouds by different methods

Table 3. Coarse registration results of building point clouds

Table 3. Coarse registration results of building point clouds

Table 4. Comparison of coarse registration results of building point clouds by different methods

Table 4. Comparison of coarse registration results of building point clouds by different methods

Table 5. Comparison of fine registration results of model point clouds under different methods

Table 5. Comparison of fine registration results of model point clouds under different methods

Table 6. Comparison of fine registration results of building point clouds by different methods

Table 6. Comparison of fine registration results of building point clouds by different methods