Improved Iterative Nearest Point Point Cloud Alignment Method

Fig. 8. Extracting feature points from temple point cloud. (a) Feature point extraction result of temple source point cloud; (b) feature point extraction result of temple target point cloud

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Fig. 9. Extracting feature points from chunked temple point cloud. (a) Feature point extraction result of temple source point cloud after chunking; (b) feature point extraction result of temple target point cloud after chunking

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Fig. 10. Extraction of overlapping point cloud regions

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Fig. 11. Accurate alignment of different algorithms. (a) Precision alignment result of conventional ICP algorithm; (b) fine alignment result without extraction of overlapping areas; (c) fine alignment result after extraction of overlapping regions

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Table 1. Time taken to extract feature points with SIFT algorithm for different number of point clouds

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Table 1. Time taken to extract feature points with SIFT algorithm for different number of point clouds

Point cloud model	Number of point clouds	Time spent extracting feature points /s	Point cloud model	Number of point clouds	Time spent extracting feature points /s
Temple 1	133201	77.005	Moto 1	104907	78.435
Temple 2	92116	52.265	Moto 2	59023	43.847
Temple 3	66472	38.461	Moto 3	45884	33.744
Temple 4	51577	29.383	Moto 4	37025	27.726
Temple 5	41022	24.343	Moto 5	21998	15.804
Temple 6	31250	17.541	Moto 6	14179	10.055
Temple 7	21073	12.601	Moto 7	5674	4.008

Table 2. Point cloud matching accuracy with different overlap rates
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Table 2. Point cloud matching accuracy with different overlap rates
Parameter Overlap rate 100% Overlap rate 80% Overlap rate 50% Overlap rate 30%
Point cloud alignment visualization
Registration error /m 2.7×10-3 3.2×10-3 3.9×10-3 Matching error

Table 3. Matching accuracy after extracting overlapping regions from point clouds with different overlapping rates

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Table 3. Matching accuracy after extracting overlapping regions from point clouds with different overlapping rates

Parameter	Overlapping areas extracted at 80% overlap	Overlapping areas extracted at 50% overlap	Overlapping areas extracted at 30% overlap
Point cloud alignment visualization
Registration error /m	3.0×10-3	2.7×10-3	2.5×10-3

Table 4. Comparison of running time and registration accuracy of each registration algorithm

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Table 4. Comparison of running time and registration accuracy of each registration algorithm

Time taken to extract feature points /s			Fine registration time /s			Fine registration error /m
Point clouds are unpartitioned	Point clouds are partitioned		No overlapping area extracted	Extracted overlapping areas		No overlapping area extracted	Extracted overlapping areas
356.166	44.801		4.523	3.159		2.569×10^-5	1.497×10^-5

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Wenbo Wang, Maoyi Tian, Jiayong Yu, Chenghang Song, Jinru Li, Maolun Zhou. Improved Iterative Nearest Point Point Cloud Alignment Method[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0215007

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Paper Information

Category: Machine Vision

Received: Jul. 23, 2021

Accepted: Sep. 3, 2021

Published Online: Dec. 29, 2021

The Author Email: Tian Maoyi (tianmaoyi_zhy@126.com)

DOI:10.3788/LOP202259.0215007

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Time taken to extract feature points with SIFT algorithm for different number of point clouds

Table 1. Time taken to extract feature points with SIFT algorithm for different number of point clouds

Table 2. Point cloud matching accuracy with different overlap rates

Table 2. Point cloud matching accuracy with different overlap rates

Table 3. Matching accuracy after extracting overlapping regions from point clouds with different overlapping rates

Table 3. Matching accuracy after extracting overlapping regions from point clouds with different overlapping rates

Table 4. Comparison of running time and registration accuracy of each registration algorithm

Table 4. Comparison of running time and registration accuracy of each registration algorithm

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