High-Precision Registration of Non-Homologous Point Clouds in Laser Scanning and Photogrammetry

Chunmei Hu; Huajie Fei; Guofang Xia; Xi Liu; Xinjian Ma

doi:10.3788/LOP202259.2415007

Laser & Optoelectronics Progress, Volume. 59, Issue 24, 2415007(2022)

High-Precision Registration of Non-Homologous Point Clouds in Laser Scanning and Photogrammetry

Chunmei Hu^1,2, Huajie Fei^1,2、*, Guofang Xia³, Xi Liu⁴, and Xinjian Ma⁵

¹School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

²Representative Architecture and Ancient Architecture Database Engineering Research Center of the Ministry of Education, Beijing 100044, China

³China Cultural Relics Information Consulting Center, Beijing 100029, China

⁴Heilongjiang Surveying and Mapping Measuring Instrument Verification Station, Harbin 150081, Heilongjiang, China

⁵Beijing Institute of Surveying and Mapping Design, Beijing 100038, China

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Abstract Get PDF(in Chinese)

Figures & Tables(14)

Fig. 1. Algorithm flow

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Fig. 2. FPFH calculation principle

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Fig. 3. Schematic diagram of octree structure

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Fig. 4. Point cloud visualization. (a) Face point cloud; (b) ear point cloud; (c) buddha head point cloud

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Fig. 5. Rough registration results of different algorithms. (a) Overall point cloud after down sampling; (b) 4PCS algorithm; (c) RANSAC algorithm; (d) proposed algorithm

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Fig. 6. Iterative estimation of grid size

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Fig. 7. Octree grid rendering. (a) Face point cloud; (b) ear point cloud; (c) buddha head point cloud

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Fig. 8. Corresponding point matching diagrams. (a) Before removing false matching; (b) after removing false matching; (c) the number of point pairs corresponding to the removal of errors varying with the number of iterations

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Fig. 9. Registration results of different methods. (a) Grid centroid; (b) nearest point of grid centroid ; (c) point with the smallest Euclidean distance in the grid

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Fig. 10. Registration results of different algorithms. (a) ICP algorithm; (b) proposed algorithm

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Table 1. Rough matching result of model point cloud

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Table 1. Rough matching result of model point cloud

Date	Point cloud	Number of point clouds	Number of down sampled point clouds	Registration time /s	Registration error / $(10^{- 3} m)$
Face	Source point cloud	10818	934	22.01	$3.53$
Face	Target point cloud	158344	1009	22.01	$3.53$
Ear	Source point cloud	20096	1135	24.24	$2.29$
Ear	Target point cloud	114603	1160	24.24	$2.29$
Head	Source point cloud	176315	2280	56.17	$2.39$
Head	Target point cloud	1587694	2456	56.17	$2.39$

Table 2. Comparison of registration results of different coarse registration algorithms
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Table 2. Comparison of registration results of different coarse registration algorithms
Method Face Ear Head
Time /s $R_{M S E}$ / $(10^{- 3} m)$ Time /s $R_{M S E}$ / $(10^{- 3} m)$ Time /s $R_{M S E}$ / $(10^{- 3} m)$
4PCS 25.18 $4.34$ 30.29 $2.91$ 65.38 $3.34$
RANSAC 41.54 $4.11$ 59.73 $2.66$ 78.75 $2.78$
Proposed method 22.01 $3.53$ 24.24 $2.29$ 56.17 $2.39$

Table 3. Comparison of registration errors of different corresponding point pair selection methods

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Table 3. Comparison of registration errors of different corresponding point pair selection methods

Data	Grid centroid	Nearest point of grid centroid	Point with the smallest Euclidean distance in the grid
Face	$2.32 \times 10^{- 3}$	$1.01 \times 10^{- 3}$	$5.72 \times 10^{- 5}$
Ear	$3.97 \times 10^{- 3}$	$2.04 \times 10^{- 3}$	$1.59 \times 10^{- 4}$
Head	$4.36 \times 10^{- 3}$	$1.53 \times 10^{- 3}$	$1.55 \times 10^{- 4}$

Table 4. Comparison of registration performance between proposed algorithm and classical algorithm
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Table 4. Comparison of registration performance between proposed algorithm and classical algorithm
Algorithm Face Ear Head
Time /s $R_{M S E}$ /m Time /s $R_{M S E}$ /m Time /s $R_{M S E}$ /m
ICP 106.44 $4.09 \times 10^{- 4}$ 94.76 $4.46 \times 10^{- 4}$ 1416.99 $1.71 \times 10^{- 3}$
Proposed algorithm 28.34 $5.72 \times 10^{- 5}$ 30.42 $1.59 \times 10^{- 4}$ 478.78 $1.55 \times 10^{- 4}$

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Chunmei Hu, Huajie Fei, Guofang Xia, Xi Liu, Xinjian Ma. High-Precision Registration of Non-Homologous Point Clouds in Laser Scanning and Photogrammetry[J]. Laser & Optoelectronics Progress, 2022, 59(24): 2415007

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Paper Information

Category: Machine Vision

Received: Aug. 12, 2022

Accepted: Oct. 9, 2022

Published Online: Nov. 30, 2022

The Author Email: Huajie Fei (979227434@qq.com)

DOI:10.3788/LOP202259.2415007

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Rough matching result of model point cloud

Table 1. Rough matching result of model point cloud

Table 2. Comparison of registration results of different coarse registration algorithms

Table 2. Comparison of registration results of different coarse registration algorithms

Table 3. Comparison of registration errors of different corresponding point pair selection methods

Table 3. Comparison of registration errors of different corresponding point pair selection methods

Table 4. Comparison of registration performance between proposed algorithm and classical algorithm

Table 4. Comparison of registration performance between proposed algorithm and classical algorithm