{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T15:19:03Z","timestamp":1780413543545,"version":"3.54.1"},"reference-count":48,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, an optimized three-dimensional (3D) pairwise point cloud registration algorithm is proposed, which is used for flatness measurement based on a laser profilometer. The objective is to achieve a fast and accurate six-degrees-of-freedom (6-DoF) pose estimation of a large-scale planar point cloud to ensure that the flatness measurement is precise. To that end, the proposed algorithm extracts the boundary of the point cloud to obtain more effective feature descriptors of the keypoints. Then, it eliminates the invalid keypoints by neighborhood evaluation to obtain the initial matching point pairs. Thereafter, clustering combined with the geometric consistency constraints of correspondences is conducted to realize coarse registration. Finally, the iterative closest point (ICP) algorithm is used to complete fine registration based on the boundary point cloud. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of boundary extraction and registration performance.<\/jats:p>","DOI":"10.3390\/s21144860","type":"journal-article","created":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T21:18:52Z","timestamp":1626643132000},"page":"4860","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Pairwise Registration Algorithm for Large-Scale Planar Point Cloud Used in Flatness Measurement"],"prefix":"10.3390","volume":"21","author":[{"given":"Zichao","family":"Shu","sequence":"first","affiliation":[{"name":"College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5122-3472","authenticated-orcid":false,"given":"Songxiao","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhipeng","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianbin","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiaojun","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1007\/s11665-018-3842-4","article-title":"Defect characterization through automated laser track trace identification in SLM processes using laser profilometer data","volume":"28","author":"Baucher","year":"2019","journal-title":"J. Mater. Eng. Perform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s00107-020-01529-6","article-title":"Study on the in-process measurements of the surface roughness of Douglas fir green veneers with the use of laser profilometer","volume":"78","author":"Stefanowski","year":"2020","journal-title":"Eur. J. Wood Prod."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.isprsjprs.2020.01.021","article-title":"Inlier extraction for point cloud registration via supervoxel guidance and game theory optimization","volume":"163","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MRA.2012.2206675","article-title":"Tutorial: Point cloud library: Three-dimensional object recognition and 6 dof pose estimation","volume":"19","author":"Aldoma","year":"2012","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A method for registration of 3-D shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.imavis.2003.09.004","article-title":"Robust registration of 2D and 3D point set","volume":"21","author":"Fitzgibbon","year":"2003","journal-title":"Image Vis. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yang, J., Li, H., and Jia, Y. (2013, January 1\u20138). Go-icp: Solving 3d Registration Efficiently and Globally Optimally. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.184"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0262-8856(92)90066-C","article-title":"Object modeling by registration of multiple range images","volume":"10","author":"Chen","year":"1992","journal-title":"Image Vis. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1006\/cviu.2000.0889","article-title":"A survey of free-form object representation and recognition techniques","volume":"81","author":"Campbell","year":"2001","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_10","first-page":"435","article-title":"Generalized-icp","volume":"Volume 2","author":"Segal","year":"2009","journal-title":"Robotics: Science and Systems"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1080\/01431161.2019.1701211","article-title":"Improved Iterative Closest Point (ICP) 3D point cloud registration algorithm based on point cloud filtering and adaptive fireworks for coarse registration","volume":"41","author":"Shi","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4328","DOI":"10.1109\/TGRS.2013.2281391","article-title":"A novel coarse-to-fine scheme for automatic image registration based on SIFT and mutual information","volume":"52","author":"Gong","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Huang, J., Wang, Z., Gao, J., Huang, Y., and Towers, D.P. (2017). High-Precision registration of point clouds based on sphere feature constraints. Sensors, 17.","DOI":"10.3390\/s17010072"},{"key":"ref_14","unstructured":"Wang, Y., Ewert, D., Schilberg, D., and Jeschke, S. (2013, January 21\u201322). Edge Extraction by Merging 3D Point Cloud and 2D Image Data. Proceedings of the International Conference and Expo on Emerging Technologies for a Smarter World, Melville, NY, USA."},{"key":"ref_15","first-page":"177","article-title":"Improved algorithm for extraction of boundary characteristic point from scattered point cloud","volume":"48","author":"Chen","year":"2012","journal-title":"Comput. Eng. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.cad.2008.12.004","article-title":"Extracting lines of curvature from noisy point clouds","volume":"41","author":"Kalogerakis","year":"2009","journal-title":"Comput. Aided Des."},{"key":"ref_17","first-page":"82","article-title":"Automatic extraction of boundary characteristic from scatter data","volume":"36","author":"Sun","year":"2008","journal-title":"J. Huazhong Univ. Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.autcon.2016.12.002","article-title":"An adaptive approach for the reconstruction and modeling of as-built 3D pipelines from point clouds","volume":"75","author":"Patil","year":"2017","journal-title":"Autom. Construct."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2994","DOI":"10.1109\/TGRS.2019.2946326","article-title":"A Point Cloud Feature Regularization Method by Fusing Judge Criterion of Field Force","volume":"58","author":"Chen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.mechatronics.2015.10.014","article-title":"3D reconstruction and multiple point cloud registration using a low precision RGB-D sensor","volume":"35","author":"Takimoto","year":"2016","journal-title":"Mechatronics"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1007\/s11831-019-09320-4","article-title":"Computational methods of acquisition and processing of 3D point cloud data for construction applications","volume":"27","author":"Wang","year":"2020","journal-title":"Arch. Comput. Method Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/TRO.2018.2882730","article-title":"Real-time global registration for globally consistent rgb-d slam","volume":"35","author":"Han","year":"2019","journal-title":"IEEE Trans. Robot."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, H., Li, X., Zhao, L., and Chen, S. (2019). A novel coarse-to-fine scheme for remote sensing image registration based on SIFT and phase correlation. Remote Sens., 11.","DOI":"10.3390\/rs11151833"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.isprsjprs.2020.03.013","article-title":"Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark","volume":"163","author":"Dong","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., and Beetz, M. (2009, January 12\u201317). Fast Point Feature Histograms (FPFH) for 3D Registration. Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Flint, A., Dick, A., and Van Den Hengel, A. (2007, January 3\u20135). Thrift: Local 3d Structure Recognition. Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications, Glenelg, Australia.","DOI":"10.1109\/DICTA.2007.4426794"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tombari, F., Salti, S., and Di Stefano, L. (2010, January 5\u201311). Unique Signatures of Histograms for Local Surface Description. Proceedings of the European Conference on Computer Vision, Crete, Greece.","DOI":"10.1007\/978-3-642-15558-1_26"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1109\/TPAMI.2014.2316828","article-title":"3D object recognition in cluttered scenes with local surface features: A survey","volume":"36","author":"Guo","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.ins.2016.01.095","article-title":"A fast and robust local descriptor for 3D point cloud registration","volume":"346","author":"Yang","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.isprsjprs.2017.06.012","article-title":"A novel binary shape context for 3D local surface description","volume":"130","author":"Dong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2530","DOI":"10.1109\/TGRS.2019.2952086","article-title":"PLADE: A plane-based descriptor for point cloud registration with small overlap","volume":"58","author":"Chen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Knopp, J., Prasad, M., Willems, G., Timofte, R., and Gool, L.V. (2010, January 5\u201311). Hough Transform and 3D SURF for Robust Three-Dimensional Classification. Proceedings of the European Conference on Computer Vision, Crete, Greece.","DOI":"10.1007\/978-3-642-15567-3_43"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Glent Buch, A., Yang, Y., Kruger, N., and Petersen, H.G. (2014, January 20\u201323). In Search of Inliers: 3D Correspondence by Local and Global Voting. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.266"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.isprsjprs.2017.10.001","article-title":"Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game","volume":"134","author":"Zai","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Aiger, D., Mitra, N.J., and Cohen-Or, D. (2008). 4-points congruent sets for robust pairwise surface registration. ACM SIGGRAPH 2008 Papers, ACM.","DOI":"10.1145\/1399504.1360684"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cheng, L., Chen, S., Liu, X., Xu, H., Wu, Y., Li, M., and Chen, Y. (2018). Registration of laser scanning point clouds: A review. Sensors, 18.","DOI":"10.3390\/s18051641"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1111\/cgf.12446","article-title":"Super 4pcs fast global pointcloud registration via smart indexing","volume":"Volume 33","author":"Mellado","year":"2014","journal-title":"Computer Graphics Forum"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.isprsjprs.2017.06.011","article-title":"Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets","volume":"130","author":"Ge","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","first-page":"283","article-title":"Markerless point cloud registration with keypoint-based 4-points congruent sets","volume":"1","author":"Theiler","year":"2013","journal-title":"Remote Sens."},{"key":"ref_40","first-page":"1009","article-title":"Non-rigid point set registration: Coherent point drift","volume":"19","author":"Myronenko","year":"2007","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gao, W., and Tedrake, R. (2019, January 16\u201320). Filterreg: Robust and Efficient Probabilistic Point-Set Registration Using Gaussian Filter and Twist Parameterization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01135"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zeng, A., Song, S., Niessner, M., Fisher, M., Xiao, J., and Funkhouser, T. (2017, January 22\u201325). 3dmatch: Learning Local gGeometric Descriptors from rgb-d Reconstructions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.29"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Deng, H., Birdal, T., and Ilic, S. (2018, January 8\u201314). Ppf-foldnet: Unsupervised Learning of Rotation Invariant 3d Local Descriptors. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01228-1_37"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Aoki, Y., Goforth, H., Srivatsan, R.A., and Lucey, S. (2019, January 16\u201320). Pointnetlk: Robust & Efficient Point Cloud Registration Using Pointnet. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00733"},{"key":"ref_45","unstructured":"Wang, Y., and Solomon, J.M. (November, January 27). Deep closest point: Learning Representations for Point Cloud Registration. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_46","unstructured":"Magnusson, M., and Duckett, T. (2005, January 7\u201310). A Comparison of 3D Registration Algorithms for Autonomous Underground Mining Vehicles. Proceedings of the European Conference on Mobile Robotics, Ancona, Italy."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1177\/0278364914539404","article-title":"Scan registration using segmented region growing NDT","volume":"33","author":"Das","year":"2014","journal-title":"Int. J. Robot. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/j.robot.2008.08.005","article-title":"Towards 3D point cloud based object maps for household environments","volume":"56","author":"Rusu","year":"2008","journal-title":"Rob. Autom. 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