激光与光电子学进展, 2020, 57 (6): 061002, 网络出版: 2020-03-06 复制并引用该论文  本文已被引 10 次  被引通知

图 & 表

图 1. 点云P和Q配准的流程图

Fig. 1. Flowchart of registration of point clouds P and Q

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图 2. 关键点提取。(a)体素格滤波;(b)法向距离提取关键点

Fig. 2. Keypoint extraction. (a)Voxel grid filtering; (b) extracting keypoints using normal distance

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图 3. 不同参数下的关键点分布。(a) a₀=0.3 mm, r=1.0 mm, t_hr=10%, m=5时,关键点数目为658;(b) a₀=0.4 mm, r=2.0 mm, t_hr=10%, m=5时,关键点数目为597;(c) a₀=0.4 mm, r=2.0 mm, t_hr=10%, m=10时,关键点数目为364

Fig. 3. Distribution of keypoints under different parameters. (a) a₀=0.3 mm, r=1.0 mm, t_hr=10%, m=5, the number of keypoints is 658; (b) a₀=0.4 mm, r=2.0 mm, t_hr=10%, m=5, the number of keypoints is 597; (c) a₀=0.4 mm, r=2.0 mm, t_hr=10%, m=10, the number of keypoints is 364

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图 4. 不同的最近点模型。(a)“点到点”模型;(b)“点到三角面”模型

Fig. 4. Different nearest point models. (a) “Point to point” model; (b) “point to triangle plane” model

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图 5. 模型点云粗配准。(a)特征匹配;(b)优化RANSAC对误匹配剔除;(c)粗配准结果

Fig. 5. Coarse registration of model point clouds. (a) Feature matching; (b) correct correspondences after improved RANSAC; (c) results of coarse registration

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图 6. 建筑物点云粗配准。(a)特征匹配;(b)优化RANSAC对误匹配剔除;(c)粗配准结果

Fig. 6. Coarse registration of building point clouds. (a) Feature matching; (b) correct correspondences after improved RANSAC; (c) results of coarse registration

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图 7. 模型点云精配准。(a)所提算法的精配准结果;(b)精配准下点云距离偏差的色谱对比;(c)不同算法精配准误差的比较

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

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图 8. 建筑物点云精配准。(a)所提算法的精配准结果;(b)精配准下点云距离偏差的色谱对比;(c)不同算法配准精度误差的比较

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

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图 9. 不同算法的高斯噪声点云配准实验比较。(a) Bunny;(b) happy;(c) armadillo

Fig. 9. Registration experiment comparison of Gaussian noise point clouds under different methods. (a) Bunny; (b) happy; (c) armadillo

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图 10. 所提算法在高斯噪声σ=0.02下对不同点云的配准结果。(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

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表 1模型点云粗配准结果

Table1. Coarse registration results of model point clouds

Dataset Size ofpoint cloud Numberof keypoints Number ofcorrespondences Number of correctcorrespondenses RMS/mm 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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表 2不同算法对模型点云粗配准结果的比较

Table2. Comparison of coarse registration results of model point clouds by different methods

Method Happy Armadillo Dragon Time /s RMS /mm Time /s RMS /mm Time /s RMS /mm 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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表 3建筑物点云粗配准结果

Table3. Coarse registration results of building point clouds

Dataset Size ofpoint cloud Number ofkeypoints Number ofcorrespondences Number of correctcorrespondences RMS / (10-2 m) Dagstuhl000Dagstuhl001 8135981360 453404 113 46 2.07 Hokuyo_0Hokuyo_1 370261370277 26953283 565 123 1.82

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表 4不同算法对建筑物点云粗配准结果比较

Table4. Comparison of coarse registration results of building point clouds by different methods

Method Dagstuhl Hokuyo Time /s RMS /m Time /s RMS /m 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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表 5不同算法模型点云精配准结果比较

Table5. Comparison of fine registration results of model point clouds under different methods

Method Happy Armadillo Dragon Time /s RMS /mm Time /s RMS /mm Time /s RMS /mm 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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表 6不同算法建筑物点云精配准结果比较

Table6. Comparison of fine registration results of building point clouds by different methods

Method Dagstuhl Hokuyo Time /s RMS /(10-3 m) Time /s RMS /(10-3 m) 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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彭真, 吕远健, 渠超, 朱大虎. 基于关键点提取与优化迭代最近点的点云配准[J]. 激光与光电子学进展, 2020, 57(6): 061002. Zhen Peng, Yuanjian Lü, Chao Qu, Dahu Zhu. Accurate Registration of 3D Point Clouds Based on Keypoint Extraction and Improved Iterative Closest Point Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061002.