Local Feature Description of LiDAR Point Cloud Data Based on Hierarchical Mercator Projection

Shangtai Gu; ling Wang; Yanxin Ma; Chao Ma

doi:10.3788/AOS202040.2015001

Acta Optica Sinica, Volume. 40, Issue 20, 2015001(2020)

Local Feature Description of LiDAR Point Cloud Data Based on Hierarchical Mercator Projection

Shangtai Gu^1、*, ling Wang^1、**, Yanxin Ma², and Chao Ma¹

Author Affiliations

¹College of Electronic Science, National University of Defense Technology, PLA, Changsha, Hunan 410073, China

²College of Meteorology and Oceanography, National University of Defense Technology, PLA, Changsha, Hunan 410073, China

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Figures & Tables(9)

Fig. 1. Schematic diagram of hierarchical Mercator projection

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Fig. 2. Plane of hierarchical Mercator projection (5 layers)

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Fig. 3. Flow chart of hierarchical Mercator projection (3 layers)

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Fig. 4. Influence of the number of Mercator projection layers on the recognition performance of the algorithm (Bologna dataset)

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Fig. 5. Influence of the number of Mercator projection layers on the recognition performance of the algorithm (3DMatch dataset)

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Fig. 6. PRC of different feature extraction algorithms. (a) Noise variance is 0.3 times point cloud resolution; (b) noise variance is 0.5 times point cloud resolution; (c) noise variance is 0.8 times point cloud resolution; (d) noise variance is 1.5 times point cloud resolution rate

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Table 1. Runtime and average precision of hierarchical Mercator projection (Bologna dataset)
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Table 1. Runtime and average precision of hierarchical Mercator projection (Bologna dataset)
Number of layers Runtime /s Average accuracy /%
1 1034.8359 0.0381
3 758.0363 0.3251
5 690.6250 0.5222
10 598.6700 0.7456
20 602.9223 0.8804
30 835.4665 0.8767
50 1278.1252 0.7408
100 13052.3727 0.6823

Table 2. Runtime and average precision of hierarchical Mercator projection (3DMatch dataset)
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Table 2. Runtime and average precision of hierarchical Mercator projection (3DMatch dataset)
Number of layers Runtime /s Average accuracy /%
1 4732.5692 0.0433
3 3069.1723 0.1214
5 1135.2417 0.3044
10 625.3645 0.5471
20 858.3694 0.7182
30 25685.2360 0.5958

Table 3. Average accuracy and operation time of different feature extraction algorithms

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Table 3. Average accuracy and operation time of different feature extraction algorithms

Algorithm	Run- time /s	Average accuracy in different noise /%
Algorithm	Run- time /s	0.3	0.5	0.8	1.5
TriS	655.6498	0.979	0.885	0.600	0.803
Sgh1	1100.8573	0.986	0.974	0.777	0.142
RoPS	1593.7144	1.000	0.996	0.999	0.994
SHOT	854.8046	0.997	0.994	0.962	0.006
MaSH	219.6354	0.999	0.828	0.151	0.027
SDASS	122.6109	1.000	0.940	0.165	0.035
Toldi	25.0574	0.994	0.983	0.363	0.803
LFSH	308.2209	0.881	0.507	0.120	0.029
DLFS	913.4914	0.856	0.690	0.575	0.077
HMec-20	557.4517	1.000	1.000	1.000	0.995

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Shangtai Gu, ling Wang, Yanxin Ma, Chao Ma. Local Feature Description of LiDAR Point Cloud Data Based on Hierarchical Mercator Projection[J]. Acta Optica Sinica, 2020, 40(20): 2015001

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Paper Information

Category: Machine Vision

Received: May. 13, 2020

Accepted: Jul. 6, 2020

Published Online: Sep. 30, 2020

The Author Email: Gu Shangtai (shangtai_gu@163.com), Wang ling (wanglanne@139.com)

DOI:10.3788/AOS202040.2015001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Runtime and average precision of hierarchical Mercator projection (Bologna dataset)

Table 1. Runtime and average precision of hierarchical Mercator projection (Bologna dataset)

Table 2. Runtime and average precision of hierarchical Mercator projection (3DMatch dataset)

Table 2. Runtime and average precision of hierarchical Mercator projection (3DMatch dataset)

Table 3. Average accuracy and operation time of different feature extraction algorithms

Table 3. Average accuracy and operation time of different feature extraction algorithms