• Infrared and Laser Engineering
  • Vol. 52, Issue 2, 20220367 (2023)
Changsheng Tan1,2,3, Genghua Huang1,2,3,*, Fengxiang Wang1,2, Wei Kong1,2, and Rong Shu1,2,3
Author Affiliations
  • 1Key Laboratory of Space Active Optoelectronic Technology, Chinese Academy of Sciences, Shanghai 200083, China
  • 2Shanghai Institute of Technology Physics, Chinese Academy of Sciences, Shanghai 200083, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
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    DOI: 10.3788/IRLA20220367 Cite this Article
    Changsheng Tan, Genghua Huang, Fengxiang Wang, Wei Kong, Rong Shu. Optimization and validation of coherent point drift for planar-array-based point cloud in space pose measurement[J]. Infrared and Laser Engineering, 2023, 52(2): 20220367 Copy Citation Text show less
    Diagram of the proposed adaptive CPD registration strategy for spatial planar-array-based point clouds
    Fig. 1. Diagram of the proposed adaptive CPD registration strategy for spatial planar-array-based point clouds
    (a) Variation of RMSE with the iterations ; (b) Convergence curve of RMSE under local minimum
    Fig. 2. (a) Variation of RMSE with the iterations ; (b) Convergence curve of RMSE under local minimum
    (a) Case 1: The platform rotates around the Yaxis of the 80 m target, and the satellite moves around the Zaxis; (b) is the correspondence between the target attitude and the observation angle; (c) Case 2: The platform moves along the Yaxis from 90 m to 40 m, and the satellite moves around the Xaxis; (d) is the link between the target attitude and the detection distance
    Fig. 3. (a) Case 1: The platform rotates around the Yaxis of the 80 m target, and the satellite moves around the Zaxis; (b) is the correspondence between the target attitude and the observation angle; (c) Case 2: The platform moves along the Yaxis from 90 m to 40 m, and the satellite moves around the Xaxis; (d) is the link between the target attitude and the detection distance
    (a) Absolute simulation point cloud at a certain pose; (b) Degraded simulation point cloud with noise perturbation
    Fig. 4. (a) Absolute simulation point cloud at a certain pose; (b) Degraded simulation point cloud with noise perturbation
    Registration results when the difference between the viewing angles of adjacent point cloud is 90 °under Case 1: (a) SAC+ ICP; (b) NDT + ICP; (c) PCA + ICP; (d) Proposed method
    Fig. 5. Registration results when the difference between the viewing angles of adjacent point cloud is 90 °under Case 1: (a) SAC+ ICP; (b) NDT + ICP; (c) PCA + ICP; (d) Proposed method
    Registration results when the viewing angle difference is 30° and 60° in Case 1: (a)-(d) are the results of SAC+ICP, NDT+ICP, PCA+ICP and the proposed method when the viewing angle difference is 30°. Similarly, (e)-(f) correspond to the 60° viewing angle difference
    Fig. 6. Registration results when the viewing angle difference is 30° and 60° in Case 1: (a)-(d) are the results of SAC+ICP, NDT+ICP, PCA+ICP and the proposed method when the viewing angle difference is 30°. Similarly, (e)-(f) correspond to the 60° viewing angle difference
    Registration results when the distance between adjacent point cloud is 30 m under Case 2: (a) Initial position; (b) SAC + ICP; (c) NDT + ICP; (d) PCA + ICP; (e) Proposed method
    Fig. 7. Registration results when the distance between adjacent point cloud is 30 m under Case 2: (a) Initial position; (b) SAC + ICP; (c) NDT + ICP; (d) PCA + ICP; (e) Proposed method
    Continuous registration results for the point cloud with the distance difference between adjacent frames of 10 m and 20 m in Case 2: (a)-(b) corresponds to ∆d=10 m, (c)-(d) corresponds to ∆d=20 m工况2下点云相邻帧距离差为10 m和20 m时的连续配准结果:(a)~(b)对应于10 m,(c)~(d) 对应于20 m
    Fig. 8. Continuous registration results for the point cloud with the distance difference between adjacent frames of 10 m and 20 m in Case 2: (a)-(b) corresponds to ∆d=10 m, (c)-(d) corresponds to ∆d=20 m 工况2下点云相邻帧距离差为10 m和20 m时的连续配准结果:(a)~(b)对应于 10 m,(c)~(d) 对应于 20 m
    Registration results under extreme strong noise : (a) - (d) are the results of SAC-ICP framework under different noise level; (e)-(h) is PCA + ICP; (i) - (l) Correspond to the registration method in this paper
    Fig. 9. Registration results under extreme strong noise : (a) - (d) are the results of SAC-ICP framework under different noise level; (e)-(h) is PCA + ICP; (i) - (l) Correspond to the registration method in this paper
    MethodSAC+ICPNDT+ICPPCA+ICPProposed
    Run time/s2.031.851.722.10
    LCPRough5.1%15.1%32.0%69.2%
    Precise18.0%8.3%63.6%
    RMSERough0.6510.5040.1310.106
    Precise0.1740.4740.102
    Table 1. Evaluation of registration results when the viewing angles are 0° and 90° respectively in Case 1
    MethodSAC+ICPNDT+ICPPCA+ICPProposed
    Run time/s2.622.422.282.54
    LCP58.8%11.0%34.5%78.6%
    RMSE0.1050.4590.1460.114
    Table 2. Evaluation of registration results in Case 2 when the detection distance is 90 m and 60 m respectively
    MethodEvaluation$ \sigma =0.02,\tau =0.5 $$ \sigma =0.02,\tau =0.8 $$ \sigma =0.05,\tau =0.5 $$ \sigma =0.05,\tau =0.8 $
    SAC+ICPRMSE0.1540.1100.1330.143
    LCP25.4%51.9%25.0%22.5%
    PCA+ICPRMSE0.1420.1100.1020.112
    LCP33.7%52.0%41.6%38.7%
    ProposedRMSE0.1180.1230.1050.125
    LCP67.2%69.1%49.5%48.4%
    Table 3. Evaluation of results when the distance is 90 m and 60 m respectively under strong noise disturbance in Case 2
    Changsheng Tan, Genghua Huang, Fengxiang Wang, Wei Kong, Rong Shu. Optimization and validation of coherent point drift for planar-array-based point cloud in space pose measurement[J]. Infrared and Laser Engineering, 2023, 52(2): 20220367
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