Journal of Applied Optics, Volume. 46, Issue 4, 805(2025)
Point cloud registration algorithm based on feature extraction and improved ICP
Figures & Tables(13)
Fig. 7. Detailed diagrams of registration effect of proposed algorithm
Fig. 8. Comparison of registration results of different algorithms
Table 1. Comparison of point cloud quantity before and after voxel down-sampling
View tableView in ArticleTable 1. Comparison of point cloud quantity before and after voxel down-sampling
点云数目 Bunny Dragon Happy Buddha Office 滤波前点云数目 35947 437645 543652 223276 体素下采样后点云数目 10803 11057 11539 9105 Octree加速的体素下采样后点云数目 10823 11069 11647 9154
Table 2. Bunny rotation errors
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配准算法 旋转误差/rad X Y Z 经典ICP 0.133725 0.023341 − 0.098621 3DSC+ICP 1.73479 ×10−31.70325 ×10−30.016454 文献[ 16 ]2.38122 ×10−20.111085 0.345353 文献[ 17 ]3.10758 ×10−21.71309 ×10−31.74167 ×10−2本文算法 2.80863 ×10−41.46857 ×10−48.9103 ×10−4
Table 3. Dragon rotation errors
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配准算法 旋转误差/rad X Y Z 经典ICP 3.8583 ×10−57.93745 ×10−5− 0.00370514 3DSC+ICP 3.34844 ×10−30.016831 0.025531 文献[ 16 ]1.84899 ×10−23.82977 ×10−20.110419 文献[ 17 ]5.89098 ×10−49.26305 ×10−41.19257 ×10−3本文算法 6.32509 ×10−55.92008 ×10−57.62284 ×10−5
Table 4. Happy Buddha rotation errors
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配准算法 旋转误差/rad X Y Z 经典ICP 0.017150 0.019726 − 0.0030386 3DSC+ICP 0.033449 0.029895 0.0023111 文献[ 16 ]1.75665 0.860802 0.901115 文献[ 17 ]4.72674 ×10−25.28701 ×10−27.40129 ×10−3本文算法 2.11837 ×10−56.66402 ×10−5− 3.54886 ×10−4
Table 5. Office rotation errors
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配准算法 旋转误差/rad X Y Z 经典ICP 0.174755 0.264086 − 0.626325 3DSC+ICP 0.027321 0.061032 7.76237 ×10−3文献[ 16 ]0.150873 0.340236 − 0.167988 文献[ 17 ]0.503297 0.401866 − 0.698001 本文算法 7.17528 ×10−51.57768 ×10−5− 3.07625 ×10−4
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Ruiyu WANG, Huicheng JING, Qilong BAO. Point cloud registration algorithm based on feature extraction and improved ICP[J]. Journal of Applied Optics, 2025, 46(4): 805
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Received: Feb. 29, 2024
Accepted: --
Published Online: Sep. 16, 2025
The Author Email: Ruiyu WANG (wry18251780190@163.com)
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