Author Affiliations
1Hubei Key Laboratory of Advanced Automotive Components Technology, Wuhan University of Technology, Wuhan, Hubei 430070, China2Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, Hubei 430070, Chinashow less
Fig. 1. Flowchart of registration of point clouds P and Q
Fig. 2. Keypoint extraction. (a)Voxel grid filtering; (b) extracting keypoints using normal distance
Fig. 3. Distribution of keypoints under different parameters. (a) a0=0.3 mm, r=1.0 mm, thr=10%, m=5, the number of keypoints is 658; (b) a0=0.4 mm, r=2.0 mm, thr=10%, m=5, the number of keypoints is 597; (c) a0=0.4 mm, r=2.0 mm, thr=10%, m=10, the number of keypoints is 364
Fig. 4. Different nearest point models. (a) “Point to point” model; (b) “point to triangle plane” model
Fig. 5. Coarse registration of model point clouds. (a) Feature matching; (b) correct correspondences after improved RANSAC; (c) results of coarse registration
Fig. 6. Coarse registration of building point clouds. (a) Feature matching; (b) correct correspondences after improved RANSAC; (c) results of coarse registration
Fig. 7. Fine registration of model point clouds. (a) Results of fine registration by proposed method; (b) chromatographic comparison of point clouds distance deviation under fine registration; (c) registration error comparison of fine registration among different methods
Fig. 8. Fine registration of building point clouds. (a) Results of fine registration by proposed method; (b) chromatographic comparison of point clouds distance deviation under fine registration; (c) registration error comparison of fine registration among different methods
Fig. 9. Registration experiment comparison of Gaussian noise point clouds under different methods. (a) Bunny; (b) happy; (c) armadillo
Fig. 10. Registration results of different point clouds with Gaussian noise σ=0.02 in proposed method. (a) Bunny; (b) happy; (c) armadillo
Dataset | Size ofpoint cloud | Numberof keypoints | Number ofcorrespondences | Number of correctcorrespondenses | RMS/mm |
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Happy024Happy048 | 7558269158 | 433381 | 102 | 75 | 0.44 | Dragon120Dragon144 | 2183323530 | 382411 | 80 | 53 | 1.04 | Armadillo15Armadillo45 | 3220824813 | 405371 | 97 | 64 | 0.96 |
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Table 1. Coarse registration results of model point clouds
Method | Happy | Armadillo | Dragon |
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Time /s | RMS /mm | Time /s | RMS /mm | Time /s | RMS /mm |
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Uniform+FPFH+SAC-IA | 42.90 | 2.06 | 6.40 | 1.64 | 4.48 | 1.14 | NARF+FPFH+SAC-IA | 28.67 | 2.18 | 16.40 | 1.76 | 8.43 | 1.91 | ISS+FPFH+SAC-IA | 17.80 | 2.53 | 9.09 | 1.15 | 10.17 | 1.02 | KFPCS | 6.70 | 1.29 | 3.28 | 1.03 | 2.71 | 1.09 | Proposed method | 1.23 | 0.44 | 1.80 | 0.96 | 0.61 | 1.04 |
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Table 2. Comparison of coarse registration results of model point clouds by different methods
Dataset | Size ofpoint cloud | Number ofkeypoints | Number ofcorrespondences | Number of correctcorrespondences | RMS / (10-2 m) |
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Dagstuhl000Dagstuhl001 | 8135981360 | 453404 | 113 | 46 | 2.07 | Hokuyo_0Hokuyo_1 | 370261370277 | 26953283 | 565 | 123 | 1.82 |
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Table 3. Coarse registration results of building point clouds
Method | Dagstuhl | Hokuyo |
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Time /s | RMS /m | Time /s | RMS /m |
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Uniform+FPFH+SAC-IA | 27.20 | 0.0415 | 93.80 | 0.0267 | NARF+FPFH+SAC-IA | 4.33 | 0.0360 | 63.70 | 0.0439 | ISS+FPFH+SAC-IA | 12.04 | 0.0239 | 77.40 | 0.0206 | KFPCS | 5.23 | 0.0249 | 29.70 | 0.0279 | Proposed method | 0.72 | 0.0207 | 12.09 | 0.0182 |
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Table 4. Comparison of coarse registration results of building point clouds by different methods
Method | Happy | Armadillo | Dragon |
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Time /s | RMS /mm | Time /s | RMS /mm | Time /s | RMS /mm |
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Standard ICP | 18.90 | 0.082 | 7.60 | 0.170 | 5.80 | 0.230 | GICP | 32.23 | 0.055 | 15.10 | 0.091 | 11.50 | 0.167 | LM-ICP | 20.24 | 0.079 | 6.97 | 0.150 | 10.17 | 0.183 | NDT | 5.30 | 0.087 | 2.35 | 0.150 | 1.69 | 0.180 | Proposed method | 11.2 | 0.053 | 6.47 | 0.084 | 4.70 | 0.173 |
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Table 5. Comparison of fine registration results of model point clouds under different methods
Method | Dagstuhl | Hokuyo |
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Time /s | RMS /(10-3 m) | Time /s | RMS /(10-3 m) |
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Standard ICP | 15.70 | 4.77 | 105.6 | 2.82 | GICP | 23.97 | 4.31 | 121.7 | 2.43 | LM-ICP | 60.60 | 4.52 | 203.6 | 4.56 | NDT | 10.67 | 5.73 | 49.6 | 2.57 | Proposed method | 13.60 | 3.58 | 70.9 | 1.61 |
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Table 6. Comparison of fine registration results of building point clouds by different methods