Point Cloud Registration Based on Weighting Information of Neighborhood Surface Deformation

Xinchun Li; Zhenyu Yan; Sen Lin

doi:10.3788/LOP57.141102

Laser & Optoelectronics Progress, Volume. 57, Issue 14, 141102(2020)

Point Cloud Registration Based on Weighting Information of Neighborhood Surface Deformation

Xinchun Li¹, Zhenyu Yan^1、*, and Sen Lin^1,2,3

Author Affiliations

¹School of Electronic and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125100, China

²State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China

³Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China

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Figures & Tables(9)

Fig. 1. Number of different closest points in p_i neighborhood. (a) Number of closest points is 8; (b) number of closest points is 7

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Fig. 2. Schematic of specific process of proposed registration algorithm

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Fig. 3. Curves of threshold effect on initial registration results. (a) Bunny model; (b) Dragon model

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Fig. 4. Registration results of Bunny model. (a) TICP; (b) NV-TICP; (c) ISS-TICP; (d) MR-TICP; (e) EISCS-DTICP

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Fig. 5. Registration results of Dragon model. (a) TICP; (b) NV-TICP; (c) ISS-TICP; (d) MR-TICP; (e) EISCS-DTICP

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Fig. 6. Registration results of Bottle model. (a) TICP; (b) NV-TICP; (c) ISS-TICP; (d) MR-TICP; (e) EISCS-DTICP

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Table 1. Accurate registration results of Bunny model under different noise conditions

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Table 1. Accurate registration results of Bunny model under different noise conditions

Algorithm	Registration error /(10^-4 mm)			Registration time /s
Algorithm	25 dB	30 dB	35 dB	25 dB	30 dB	35 dB
TICP	64.8090	63.3140	65.8800	0.21	0.28	0.35
NV-TICP	45.3250	45.5238	5.3005	52.91	40.03	11.84
ISS-TICP	44.2087	6.5982	5.3004	20.21	22.14	11.93
MR-TICP	7.6317	6.6079	5.3005	41.50	39.49	38.34
EISCS-DTICP	0.1616	0.1051	0.0059	32.21	28.13	23.16

Table 2. Accurate registration results of Dragon model under different data loss situations

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Table 2. Accurate registration results of Dragon model under different data loss situations

Algorithm	Registration error /(10^-4 mm)			Registration time /s
Algorithm	10%	20%	30%	10%	20%	30%
TICP	77.1000	76.7330	76.6850	1.19	0.88	0.79
NV-TICP	63.6743	69.5212	61.8752	210.24	98.64	101.82
ISS-TICP	3.7032	63.8209	88.4215	34.85	182.20	70.93
MR-TICP	3.7033	3.3238	3.7356	85.98	73.21	64.62
EISCS-DTICP	0.0213	0.0203	0.0244	69.06	46.88	33.62

Table 3. Accurate registration results of Bottle model under different environments

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Table 3. Accurate registration results of Bottle model under different environments

Algorithm	Registration error /(10^-4 mm)		Registration time /s
Algorithm	Ideal	Non-ideal	Ideal	Non-ideal
TICP	43.0950	44.8670	1.12	0.69
NV-TICP	3.5682	5.5839	3.95	4.70
ISS-TICP	3.5682	31.4097	4.34	11.42
MR-TICP	3.7578	5.5835	22.21	62.24
EISCS-DTICP	0.0446	0.0677	5.79	30.83

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Xinchun Li, Zhenyu Yan, Sen Lin. Point Cloud Registration Based on Weighting Information of Neighborhood Surface Deformation[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141102

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Paper Information

Category: Imaging Systems

Received: Nov. 11, 2019

Accepted: Dec. 11, 2019

Published Online: Jul. 28, 2020

The Author Email: Zhenyu Yan (yanzhyngu@163.com)

DOI:10.3788/LOP57.141102

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology