Infrared and Laser Engineering, Volume. 51, Issue 5, 20210342(2022)
Local neighborhood feature point extraction and matching for point cloud alignment
Figures & Tables(21)
Fig. 1. Changes in the surface of a local neighborhoods. (a) Relatively flat surface; (b) Undulating surface
Fig. 2. Feature point extraction of Dragon model under different artificially selected thresholds Dragon模型在人为选取不同阈值 下的特征点提取情况
Fig. 3. Feature point extraction for Dragon 0°. (a) ISS; (b) SIFT; (c) Harris3D; (d) Proposed method
Fig. 4. Feature point extraction for Bunny 0°. (a) ISS; (b) SIFT; (c) Harris3D; (d) Proposed method
Fig. 5. Influence of neighborhood radius
Fig. 6. Rough matching results of Dragon in different feature point extraction methods. (a) ISS; (b) SIFT; (c) Harris3D; (d) Proposed method
Fig. 7. Rough matching results of Bunny in different feature point extraction methods. (a) ISS; (b) SIFT; (c) Harris3D; (d) Proposed method
Fig. 8. Results of fine registration for Dragon and Bunny. (a) ICP algorithm; (b) Proposed ICP algorithm
Fig. 9. Rough matching results of Bunny with 10% noise under different methods. (a) ISS; (b) SIFT; (c) Harris3D; (d) Proposed method
Fig. 10. Rough matching results of Bunny with 20% noise under different methods. (a) ISS; (b) SIFT; (c) Harris3D; (d) Proposed method
Fig. 11. Results of fine registration for Bunny with 10% and 20% noise. (a) ICP algorithm; (b) Proposed ICP algorithm
Fig. 12. Rough matching results of Room in different feature point extraction methods. (a) ISS; (b) SIFT; (c) Harris3D; (d) Proposed method
Fig. 13. Rough matching results of Land in different feature point extraction methods. (a) ISS; (b) SIFT; (c) Harris3D; (d) Proposed method
Fig. 14. Results of fine registration for Room and Land. (a) ICP algorithm; (b) Proposed ICP algorithm
Table 1. Point cloud registration parameter settings
View tableView in ArticleTable 1. Point cloud registration parameter settings
Parameters dmin iter_max εerror Value 10 mr 50 10−6 mr
Table 2. Alignment efficiency comparison of Dragon and Bunny for coarse matching in different methods
View tableView in ArticleTable 2. Alignment efficiency comparison of Dragon and Bunny for coarse matching in different methods
Model Bunny Dragon Matching error/10−6 m Time-consuming/s Matching error/10−6 m Time-consuming/s ISS 2.33 45.4 2.10 43.2 SIFT 2.02 85.9 2.14 76.6 Harris3D 2.14 42.6 2.62 38.4 Proposed method 1.78 25.8 1.91 23.0
Table 3. Comparison of alignment efficiency for Dragon and Bunny fine alignment
View tableView in ArticleTable 3. Comparison of alignment efficiency for Dragon and Bunny fine alignment
Model Algorithm Matching error/10−6 m Time-consuming/s Bunny ICP 0.00385 21.1 Proposed -ICP 0.00391 2.5 Dragon ICP 1.1334 20.4 Proposed -ICP 1.19131 2.1
Table 4. Alignment efficiency comparison of Bunny with 10% and 20% noise for coarse matching in different methods
View tableView in ArticleTable 4. Alignment efficiency comparison of Bunny with 10% and 20% noise for coarse matching in different methods
Model Bunny with 10% noise Bunny with 20% noise Matching error/10−6 m Time-consuming/s Matching error/10−6 m Time-consuming/s ISS 3.28 45.2 6.17 43.2 SIFT 6.39 105.4 Fail Harris3D Fail Fail Proposed method 3.11 24 4.15 23
Table 5. Alignment efficiency comparison of Bunny with 10% and 20% noise fine alignment
View tableView in ArticleTable 5. Alignment efficiency comparison of Bunny with 10% and 20% noise fine alignment
Model Algorithm Matching error/ 10−6 m Time-consuming/ s Bunny with 10% noise ICP 2.75 143.3 Proposed -ICP 2.72 34.1 Bunny with 20% noise ICP 2.42 149.7 Proposed -ICP 2.34 38.5
Table 6. Alignment efficiency comparison of Room and Land for coarse matching in different methods
View tableView in ArticleTable 6. Alignment efficiency comparison of Room and Land for coarse matching in different methods
Model Room Land Matching error/m Time-consuming/s Matching error/m Time-consuming/s ISS 0.4622 121.7 0.0328 167.4 SIFT 0.2091 182.8 0.0294 214.0 Harris3D 0.3845 89.5 0.0347 138.6 Proposed method 0.1962 67.3 0.0273 67.3
Table 7. Alignment efficiency comparison of Room and Land fine alignment
View tableView in ArticleTable 7. Alignment efficiency comparison of Room and Land fine alignment
Model Algorithm Matching error/10−6 m Time-consuming/s Room ICP 0.164108 17.1 Proposed -ICP 0.164576 189.9 Land ICP 0.023628 420.4 Proposed -ICP 0.023802 37
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Mingjun Wang, Fang Yi, Le Li, Chaojun Huang. Local neighborhood feature point extraction and matching for point cloud alignment[J]. Infrared and Laser Engineering, 2022, 51(5): 20210342
Paper Information
Category: Image processing
Received: May. 27, 2021
Accepted: --
Published Online: Jun. 14, 2022
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