{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T11:09:26Z","timestamp":1772104166453,"version":"3.50.1"},"reference-count":46,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T00:00:00Z","timestamp":1715817600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>At present, industrial scenes with sparse features and weak textures are widely encountered, and the three-dimensional reconstruction of such scenes is a recognized problem. Pressure pipelines have a wide range of applications in fields such as petroleum engineering, chemical engineering, and hydropower station engineering. However, there is no mature solution for the three-dimensional reconstruction of pressure pipes. The main reason is that the typical scenes in which pressure pipes are found also have relatively few features and textures. Traditional three-dimensional reconstruction algorithms based on feature extraction are largely ineffective for such scenes that are lacking in features. In view of the above problems, this paper proposes an improved interframe registration algorithm based on point cloud fitting with cylinder axis vector constraints. By incorporating geometric feature parameters of a cylindrical pressure pipeline, specifically the axis vector of the cylinder, to constrain the traditional iterative closest point algorithm, the accuracy of point cloud registration can be improved in scenarios lacking features and textures, and some environmental uncertainties can be overcome. Finally, using actual laser point cloud data collected from pressure pipelines, the proposed fitting-based point cloud registration algorithm with cylinder axis vector constraints is tested. The experimental results show that under the same conditions, compared with other open-source point cloud registration algorithms, the proposed method can achieve higher registration accuracy. Moreover, integrating this algorithm into an open-source three-dimensional reconstruction algorithm framework can lead to better reconstruction results.<\/jats:p>","DOI":"10.1017\/s0263574724000845","type":"journal-article","created":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T08:19:45Z","timestamp":1715847585000},"page":"29-46","source":"Crossref","is-referenced-by-count":3,"title":["An effective point cloud registration method for\u00a0three-dimensional reconstruction of pressure piping"],"prefix":"10.1017","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0360-542X","authenticated-orcid":false,"given":"Yulong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enguang","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1379-5677","authenticated-orcid":false,"given":"Baoyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanzheng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2024,5,16]]},"reference":[{"key":"S0263574724000845_ref14","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1017\/S0263574721000850","article-title":"A physics perspective on lidar data assimilation for mobile robots","volume":"40","author":"Berquin","year":"2022","journal-title":"Robotica"},{"key":"S0263574724000845_ref7","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-79672-3","volume-title":"Advanced Tunneling Techniques and Information Modeling of Underground Infrastructure","author":"Yang","year":"2021"},{"key":"S0263574724000845_ref28","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1111\/cgf.12178","article-title":"Sparse iterative closest point","volume":"32","author":"Bouaziz","year":"2013","journal-title":"Comput Graph Forum"},{"key":"S0263574724000845_ref34","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1177\/0278364912460895","article-title":"Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations","volume":"31","author":"Stoyanov","year":"2012","journal-title":"Int J Robot Res"},{"key":"S0263574724000845_ref24","doi-asserted-by":"crossref","unstructured":"[24] Censi, A. , \u201cAn ICP Variant using a Point-to-Line Metric,\u201d In:\u00a02008 IEEE International Conference on Robotics and Automation (ICRA), (2008) pp. 19\u201325.","DOI":"10.1109\/ROBOT.2008.4543181"},{"key":"S0263574724000845_ref32","unstructured":"[32] Magnusson, M. , The Three-Dimensional Normal-Distributions Transform: An Efficient Representation for Registration, Surface Analysis, and Loop Detection (\u00d6rebro universitet, 2009). Ph.D. dissertation"},{"key":"S0263574724000845_ref9","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1017\/S0263574719000675","article-title":"Three-dimensional reconstruction based on visual slam of mobile robot in search and rescue disaster scenarios","volume":"38","author":"Wang","year":"2020","journal-title":"Robotica"},{"key":"S0263574724000845_ref42","doi-asserted-by":"crossref","unstructured":"[42] Yew, Z. J. and Lee, G. H. , \u201cREGTR: End-to-End Point Cloud Correspondences with Transformers,\u201d In:\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (2022) pp. 6677\u20136686.","DOI":"10.1109\/CVPR52688.2022.00656"},{"key":"S0263574724000845_ref20","doi-asserted-by":"crossref","unstructured":"[20] Shan, T. , Englot, B. , Meyers, D. , Wang, W. , Ratti, C. and Rus, D. , \u201cLio-sam: Tightly-Coupled Lidar Inertial Odometry via Smoothing and Mapping,\u201d In:\u00a02020 IEEE\/RSJ international conference on intelligent robots and systems (IROS), (2020) pp. 5135\u20135142.","DOI":"10.1109\/IROS45743.2020.9341176"},{"key":"S0263574724000845_ref21","doi-asserted-by":"crossref","first-page":"2053","DOI":"10.1109\/TRO.2022.3141876","article-title":"FAST-LIO2: Fast direct LiDAR-inertial odometry","volume":"38","author":"Xu","year":"2022","journal-title":"IEEE Trans Robot"},{"key":"S0263574724000845_ref22","doi-asserted-by":"crossref","first-page":"4861","DOI":"10.1109\/LRA.2022.3152830","article-title":"Faster-LIO: Lightweight tightly coupled lidar-inertial odometry using parallel sparse incremental voxels","volume":"7","author":"Bai","year":"2022","journal-title":"IEEE Robot Autom Lett"},{"key":"S0263574724000845_ref25","doi-asserted-by":"crossref","unstructured":"[25] Serafin, J. and Grisetti, G. , \u201cNICP: Dense Normal Based Point Cloud Registration,\u201d In:\u00a02015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), (2015) pp. 742\u2013749.","DOI":"10.1109\/IROS.2015.7353455"},{"key":"S0263574724000845_ref10","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1017\/S0263574719000584","article-title":"Robust semantic mapping in challenging environments","volume":"38","author":"Cheng","year":"2020","journal-title":"Robotica"},{"key":"S0263574724000845_ref26","doi-asserted-by":"crossref","unstructured":"[26] Koide, K. , Yokozuka, M. , Oishi, S. and Banno, A. , \u201cVoxelized GICP for Fast and Accurate 3D Point Cloud Registration,\u201d In:\u00a02021 IEEE International Conference on Robotics and Automation (ICRA), (2021) pp. 11054\u201311059.","DOI":"10.1109\/ICRA48506.2021.9560835"},{"key":"S0263574724000845_ref38","doi-asserted-by":"crossref","unstructured":"[38] Qin, Z. , Yu, H. , Wang, C. , Guo, Y. , Peng, Y. and Xu, K. , \u201cGeometric Transformer for Fast and Robust Point Cloud Registration,\u201d In:\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (2022) pp. 11143\u201311152.","DOI":"10.1109\/CVPR52688.2022.01086"},{"key":"S0263574724000845_ref39","first-page":"23872","article-title":"Cofinet: Reliable coarse-to-fine correspondences for robust pointcloud registration","volume":"34","author":"Yu","year":"2021","journal-title":"Adv Neur Inform Process Syst"},{"key":"S0263574724000845_ref31","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1109\/TPAMI.2015.2513405","article-title":"Go-ICP: A globally optimal solution to 3D ICP point-set registration","volume":"38","author":"Yang","year":"2015","journal-title":"IEEE Trans Patt Anal"},{"key":"S0263574724000845_ref16","doi-asserted-by":"crossref","first-page":"3184","DOI":"10.1109\/LRA.2021.3062815","article-title":"Balm: Bundle adjustment for lidar mapping","volume":"6","author":"Liu","year":"2021","journal-title":"IEEE Robot Autom Lett"},{"key":"S0263574724000845_ref18","doi-asserted-by":"crossref","unstructured":"[18] Shan, T. and Englot, B. , \u201cLego-loam: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain,\u201d In:\u00a02018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), (2018) pp. 4758\u20134765.","DOI":"10.1109\/IROS.2018.8594299"},{"key":"S0263574724000845_ref43","doi-asserted-by":"crossref","unstructured":"[43] Fu, K. , Liu, S. , Luo, X. and Wang, M. , \u201cRobust Point Cloud Registration Framework Based on Deep Graph Matching,\u201d In:\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (2021) pp. 8893\u20138902.","DOI":"10.1109\/CVPR46437.2021.00878"},{"key":"S0263574724000845_ref37","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1109\/TRO.2020.3033695","article-title":"Teaser: Fast and certifiable point cloud registration","volume":"37","author":"Yang","year":"2020","journal-title":"IEEE Trans Robot"},{"key":"S0263574724000845_ref4","doi-asserted-by":"crossref","unstructured":"[4] Sarkar, M. , Prabhakar, M. and Ghose, D. , \u201cAvoiding Obstacles with Geometric Constraints on Lidar Data for Autonomous Robots,\u201d In:\u00a0Third Congress on Intelligent Systems: Proceedings of CIS 2022, 1, (2023) pp. 749\u2013761.","DOI":"10.1007\/978-981-19-9225-4_54"},{"key":"S0263574724000845_ref17","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1017\/S0263574723001698","article-title":"BA-LIOM: Tightly coupled laser-inertial odometry and mapping with bundle adjustment","volume":"42","author":"Li","year":"2024","journal-title":"Robotica"},{"key":"S0263574724000845_ref35","doi-asserted-by":"crossref","unstructured":"[35] Magnusson, M. , Vaskevicius, N. , Stoyanov, T. , Pathak, K. and Birk, A. , \u201cBeyond Points: Evaluating Recent 3D Scan-Matching Algorithms,\u201d In:\u00a02015 IEEE International Conference on Robotics and Automation (ICRA), (2015) pp. 3631\u20133637.","DOI":"10.1109\/ICRA.2015.7139703"},{"key":"S0263574724000845_ref1","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1017\/S0263574721000862","article-title":"Autonomous vehicle self-localization in urban environments based on 3d curvature feature points\u2013monte carlo localization","volume":"40","author":"Liu","year":"2022","journal-title":"Robotica"},{"key":"S0263574724000845_ref5","doi-asserted-by":"crossref","first-page":"7868","DOI":"10.3390\/s22207868","article-title":"Deep learning for LiDAR point cloud classification in remote sensing","volume":"22","author":"Diab","year":"2022","journal-title":"Sensors"},{"key":"S0263574724000845_ref41","first-page":"5749","article-title":"Efficient 3D Deep LiDAR Odometry","volume":"45","author":"Wang","year":"2022","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"key":"S0263574724000845_ref8","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1017\/S0263574722000339","article-title":"Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: A survey","volume":"41","author":"Nguyen","year":"2023","journal-title":"Robotica"},{"key":"S0263574724000845_ref30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3306346.3323037","article-title":"A symmetric objective function for ICP","volume":"38","author":"Rusinkiewicz","year":"2019","journal-title":"ACM Trans Graph (TOG)"},{"key":"S0263574724000845_ref15","doi-asserted-by":"crossref","first-page":"2648","DOI":"10.1017\/S0263574723000589","article-title":"A low-cost indoor positioning system based on data-driven modeling for robotics research and education","volume":"41","author":"Ou","year":"2023","journal-title":"Robotica"},{"key":"S0263574724000845_ref36","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1111\/cgf.12446","article-title":"Super 4PCS fast global pointcloud registration via smart indexing","volume":"33","author":"Mellado","year":"2014","journal-title":"Comput Graph Forum"},{"key":"S0263574724000845_ref44","doi-asserted-by":"crossref","unstructured":"[44] Gao, W. and Tedrake, R. , \u201cFilterreg: Robust and Efficient Probabilistic Point-Set Registration Using Gaussian Filter and Twist Parameterization,\u201d In:\u00a02019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2019) pp. 11087\u201311096.","DOI":"10.1109\/CVPR.2019.01135"},{"key":"S0263574724000845_ref13","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1017\/S0263574724000079","article-title":"SLAMBandMAI: a comprehensive methodology for SLAM benchmark and map accuracy improvement","volume":"42","author":"Liu","year":"2024","journal-title":"Robotica"},{"key":"S0263574724000845_ref12","unstructured":"[12] Huang, X. , Mei, G. , Zhang, J. and Abbas, R. , \u201cA comprehensive survey on point cloud registration,\u201d (2021). arXiv preprint arXiv: 2103.02690, 2021."},{"key":"S0263574724000845_ref46","doi-asserted-by":"crossref","unstructured":"[46] Campbell, D. and Petersson, L. , \u201cAn Adaptive Data Representation for Robust Point-Set Registration and Merging,\u201d In:\u00a02015 IEEE International Conference on Computer Vision (ICCV), (2015) pp. 4292\u20134300.","DOI":"10.1109\/ICCV.2015.488"},{"key":"S0263574724000845_ref2","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1017\/S0263574721000369","article-title":"An efficient LiDAR-based localization method for self-driving cars in dynamic environments","volume":"40","author":"Zhang","year":"2022","journal-title":"Robotica"},{"key":"S0263574724000845_ref19","doi-asserted-by":"crossref","first-page":"2588","DOI":"10.1017\/S026357472300053X","article-title":"Comparing lidar and IMU-based SLAM approaches for 3D robotic mapping","volume":"41","author":"Fasiolo","year":"2023","journal-title":"Robotica"},{"key":"S0263574724000845_ref3","doi-asserted-by":"crossref","first-page":"141","DOI":"10.5194\/isprs-archives-XLVI-3-W1-2022-141-2022","article-title":"A 3d Lidar reconstruction approach for vegetation detection in power transmission networks","volume":"46","author":"Ma","year":"2022","journal-title":"Int Arch Photogram Remote Sens Spat Inform Sci"},{"key":"S0263574724000845_ref11","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1017\/S0263574721000497","article-title":"A novel navigation system for an autonomous mobile robot in an uncertain environment","volume":"40","author":"Chen","year":"2022","journal-title":"Robotica"},{"key":"S0263574724000845_ref29","doi-asserted-by":"crossref","unstructured":"[29] Pavlov, A. L. , Ovchinnikov, G. W. , Derbyshev, D. Y. , Tsetserukou, D. and Oseledets, I. V. , \u201cAA-ICP: Iterative Closest Point with Anderson Acceleration,\u201d In:\u00a02018 IEEE International Conference on Robotics and Automation (ICRA), (2018) pp. 3407\u20133412.","DOI":"10.1109\/ICRA.2018.8461063"},{"key":"S0263574724000845_ref40","doi-asserted-by":"crossref","unstructured":"[40] Huang, S. , Gojcic, Z. , Usvyatsov, M. , Wieser, A. and Schindler, K. , \u201cPredator: Registration of 3D Point Clouds with Low Overlap,\u201d In:\u00a0Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, (2021) pp. 4267\u20134276.","DOI":"10.1109\/CVPR46437.2021.00425"},{"key":"S0263574724000845_ref45","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/TPAMI.2010.223","article-title":"Robust point set registration using gaussian mixture models","volume":"33","author":"Jian","year":"2011","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"key":"S0263574724000845_ref6","doi-asserted-by":"crossref","first-page":"107737","DOI":"10.1016\/j.compag.2023.107737","article-title":"LiDAR applications in precision agriculture for cultivating crops: A review of recent advances","volume":"207","author":"Rivera","year":"2023","journal-title":"Comput Electron Agr"},{"key":"S0263574724000845_ref23","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0262-8856(92)90066-C","article-title":"Object modelling by registration of multiple range images","volume":"10","author":"Chen","year":"1992","journal-title":"Image Vision Comput"},{"key":"S0263574724000845_ref33","doi-asserted-by":"crossref","first-page":"2262","DOI":"10.1109\/TPAMI.2010.46","article-title":"Point set registration: Coherent point drift","volume":"32","author":"Myronenko","year":"2010","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"key":"S0263574724000845_ref27","first-page":"3450","article-title":"Fast and robust iterative closest point","volume":"44","author":"Zhang","year":"2021","journal-title":"IEEE Trans Patt Anal"}],"container-title":["Robotica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0263574724000845","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T03:28:37Z","timestamp":1745897317000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0263574724000845\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,16]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["S0263574724000845"],"URL":"https:\/\/doi.org\/10.1017\/s0263574724000845","relation":{},"ISSN":["0263-5747","1469-8668"],"issn-type":[{"value":"0263-5747","type":"print"},{"value":"1469-8668","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,16]]}}}