{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T16:19:18Z","timestamp":1773505158044,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,4]],"date-time":"2019-12-04T00:00:00Z","timestamp":1575417600000},"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>Automatic and accurate mapping and modeling of underground infrastructure has become indispensable for several important tasks ranging from urban planning and construction to safety and hazard mitigation. However, this offers several technical and operational challenges. The aim of this work is to develop a portable automated mapping solution for the 3D mapping and modeling of underground pipe networks during renovation and installation work when the infrastructure is being laid down in open trenches. The system is used to scan the trench and then the 3D scans obtained from the system are registered together to form a 3D point cloud of the trench containing the pipe network using a modified global ICP (iterative closest point) method. In the 3D point cloud, pipe-like structures are segmented using fuzzy C-means clustering and then modeled using a nested MSAC (M-estimator SAmpling Consensus) algorithm. The proposed method is evaluated on real data pertaining to three different sites, containing several different types of pipes. We report an overall registration error of less than     7 %    , an overall segmentation accuracy of     85 %     and an overall modeling error of less than     5 %    . The evaluated results not only demonstrate the efficacy but also the suitability of the proposed solution.<\/jats:p>","DOI":"10.3390\/s19245345","type":"journal-article","created":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T03:16:36Z","timestamp":1575515796000},"page":"5345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System"],"prefix":"10.3390","volume":"19","author":[{"given":"Ahmad K.","family":"Aijazi","sequence":"first","affiliation":[{"name":"Institut Pascal, UMR 6602, Universit\u00e9 Clermont Auvergne, CNRS, SIGMA Clermont, F-63000 Clermont-Ferrand, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laurent","family":"Malaterre","sequence":"additional","affiliation":[{"name":"Institut Pascal, UMR 6602, Universit\u00e9 Clermont Auvergne, CNRS, SIGMA Clermont, F-63000 Clermont-Ferrand, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3486-3918","authenticated-orcid":false,"given":"Laurent","family":"Trassoudaine","sequence":"additional","affiliation":[{"name":"Institut Pascal, UMR 6602, Universit\u00e9 Clermont Auvergne, CNRS, SIGMA Clermont, F-63000 Clermont-Ferrand, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thierry","family":"Chateau","sequence":"additional","affiliation":[{"name":"Institut Pascal, UMR 6602, Universit\u00e9 Clermont Auvergne, CNRS, SIGMA Clermont, F-63000 Clermont-Ferrand, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2930-3393","authenticated-orcid":false,"given":"Paul","family":"Checchin","sequence":"additional","affiliation":[{"name":"Institut Pascal, UMR 6602, Universit\u00e9 Clermont Auvergne, CNRS, SIGMA Clermont, F-63000 Clermont-Ferrand, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, J., Guo, T., Leung, H., Xu, H., Liu, L., Wang, B., and Liu, Y. (2019). Locating Underground Pipe Using Wideband Chaotic Ground Penetrating Radar. Sensors, 19.","DOI":"10.3390\/s19132913"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ndteint.2017.04.002","article-title":"A review of Ground Penetrating Radar application in civil engineering: A 30-year journey from Locating and Testing to Imaging and Diagnosis","volume":"96","author":"Lai","year":"2018","journal-title":"NDT E Int."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bai, X., An, W., Wang, B., Jiang, J., Zhang, Y., and Zhang, J. (2019, January 12\u201313). Automatic Identification of Underground Pipeline Based on Ground Penetrating Radar. Proceedings of the International Conference on Wireless and Satellite Systems, WiSATS 2019: Wireless and Satellite Systems, Harbin, China.","DOI":"10.1007\/978-3-030-19156-6_7"},{"key":"ref_4","unstructured":"FITESIC (2019, August 16). RAPH. Available online: http:\/\/90.85.0.186\/index.php\/fr\/innovations-technologies\/fit-esic-innove\/60-innovations-technologies-fr-fr\/fit-esic-innove-fr-fr\/158-raph."},{"key":"ref_5","unstructured":"Reso3d (2019, August 16). RESO 3D. Available online: http:\/\/reso3d.com\/."},{"key":"ref_6","unstructured":"Rabbani, T., van den Heuvel, F., and Vosselman, G. (2006, January 25\u201327). Segmentation of point clouds using smoothness constraints. Proceedings of the ISPRS Commission V Symposium, Part 6: Image Engineering and Vision Metrology, International Society for Photogrammetry and Remote Sensing (ISPRS), Dresden, Germany."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1700","DOI":"10.1109\/TVCG.2013.74","article-title":"Cylinder Detection in Large-Scale Point Cloud of Pipeline Plant","volume":"19","author":"Liu","year":"2013","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.isprsjprs.2013.12.001","article-title":"An adaptive approach for the segmentation and extraction of planar and linear\/cylindrical features from laser scanning data","volume":"93","author":"Lari","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","first-page":"899","article-title":"Extraction of Surface Primitives using Geometric Constraints of Objects","volume":"2009A","author":"Masuda","year":"2009","journal-title":"Proc. JSPE Semest. Meet."},{"key":"ref_10","first-page":"63","article-title":"Reconstruction of Cylinder and Rectangle Faces by Detecting Edges in Large-Scale Point-Cloud","volume":"2010A","author":"Matsunuma","year":"2010","journal-title":"Proc. JSPE Semest. Meet."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1007\/s00371-010-0520-4","article-title":"SkelTre","volume":"26","author":"Bucksch","year":"2010","journal-title":"Vis. Comput."},{"key":"ref_12","unstructured":"Hyojoo, S., Changmin, K., and Changwan, K. (2014, January 19\u201321). Automatic 3D Reconstruction of As-built Pipeline Based on Curvature Computations from Laser-Scanned Data. Proceedings of the Construction Research Congress 2014, Atlanta, GA, USA."},{"key":"ref_13","unstructured":"Su, Y.T., and Bethel, J. (2010, January 26\u201330). Detection and robust estimation of cylinder features in point clouds. Proceedings of the ASPRS Annual Conference on Opportunities for Emerging Geospatial Technologies, San Diego, CA, USA."},{"key":"ref_14","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. Constr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"04014009","DOI":"10.1061\/(ASCE)CP.1943-5487.0000329","article-title":"Automatic Detection of Cylindrical Objects in Built Facilities","volume":"28","author":"Ahmed","year":"2014","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Huang, J., and You, S. (July, January 29). Detecting Objects in Scene Point Cloud: A Combinational Approach. Proceedings of the 2013 International Conference on 3D Vision-3DV 2013, Seattle, WA, USA.","DOI":"10.1109\/3DV.2013.31"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pang, G., Qiu, R., Huang, J., You, S., and Neumann, U. (2015, January 18\u201322). Automatic 3D industrial point cloud modeling and recognition. Proceedings of the 2015 14th IAPR International Conference on Machine Vision Applications (MVA), Tokyo, Japan.","DOI":"10.1109\/MVA.2015.7153124"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Qiu, R., Zhou, Q.Y., and Neumann, U. (2014, January 6\u201312). Pipe-Run Extraction and Reconstruction from Point Clouds. Proceedings of the Computer Vision (ECCV 2014), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10578-9_2"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jin, Y.H., and Lee, W.H. (2019). Fast Cylinder Shape Matching Using Random Sample Consensus in Large Scale Point Cloud. Appl. Sci., 9.","DOI":"10.3390\/app9050974"},{"key":"ref_20","first-page":"4","article-title":"Catmull-rom splines","volume":"41","author":"Twigg","year":"2003","journal-title":"Computer"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.autcon.2018.12.015","article-title":"Pipe radius estimation using Kinect range cameras","volume":"99","author":"Nahangi","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Proen\u00e7a, P.F., and Gao, Y. (2018, January 1\u20135). Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometry. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593516"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Figueiredo, R., Moreno, P., and Bernardino, A. (2017, January 26\u201328). Robust cylinder detection and pose estimation using 3D point cloud information. Proceedings of the 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Coimbra, Portugal.","DOI":"10.1109\/ICARSC.2017.7964081"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fuhrmann, S., and Goesele, M. (2011, January 12\u201315). Fusion of Depth Maps with Multiple Scales. Proceedings of the 2011 SIGGRAPH Asia Conference, New York, NY, USA.","DOI":"10.1145\/2024156.2024182"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1111\/j.1467-8659.2011.02030.x","article-title":"VASE: Volume-Aware Surface Evolution for Surface Reconstruction from Incomplete Point Clouds","volume":"30","author":"Tagliasacchi","year":"2011","journal-title":"Comput. Graph. Forum"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s10851-015-0615-7","article-title":"A Novel Fractional Implicit Polynomial Approach for Stable Representation of Complex Shapes","volume":"55","author":"Wu","year":"2016","journal-title":"J. Math. Imaging Vis."},{"key":"ref_27","unstructured":"Kim, M.S., and Shimada, K. (2006, January 26\u201328). Segmentation of Scanned Mesh into Analytic Surfaces Based on Robust Curvature Estimation and Region Growing. Proceedings of the Geometric Modeling and Processing (GMP 2006), Pittsburgh, PA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.cagd.2017.02.005","article-title":"Implicit surface reconstruction with total variation regularization","volume":"52","author":"Liu","year":"2017","journal-title":"Comput. Aided Geom. Des."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/TIP.2014.2366374","article-title":"Implicit B-Spline Surface Reconstruction","volume":"24","author":"Rouhani","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","unstructured":"Velodyne (2019, August 16). Velodyne LiDAR PUCK. Available online: https:\/\/velodynelidar.com\/vlp-16.html."},{"key":"ref_31","unstructured":"WithRobot (2019, August 16). myAHRS+. Available online: http:\/\/withrobot.com\/en\/sensor\/myahrsplus\/."},{"key":"ref_32","unstructured":"ublox (2019, August 16). C94-M8P\u2014u-blox RTK Application Board Package. Available online: https:\/\/www.u-blox.com\/en\/product\/c94-m8p."},{"key":"ref_33","unstructured":"Fioraio, N., and Konolige, K. (2011, January 27\u201330). Realtime visual and point cloud slam. Proceedings of the RGB-D Workshop on Advanced Reasoning with Depth Cameras at Robotics: Science and Systems (RSS), Los Angeles, CA, USA."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1127\/pfg\/2015\/0270","article-title":"A Correspondence Framework for ALS Strip Adjustments based on Variants of the ICP Algorithm Korrespondenzen f\u00fcr die ALS-Streifenausgleichung auf Basis von ICP","volume":"2015","author":"Glira","year":"2015","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1080\/01621459.1974.10482962","article-title":"The Influence Curve and its Role in Robust Estimation","volume":"69","author":"Hampel","year":"1974","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7726","DOI":"10.1080\/01431161.2014.975420","article-title":"Automatic detection and feature estimation of windows in 3D urban point clouds exploiting fa\u00e7ade symmetry and temporal correspondences","volume":"35","author":"Aijazi","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ghorpade, V.K., Checchin, P., and Trassoudaine, L. (2015, January 2\u20134). Line-of-sight-based ToF camera\u2019s range image filtering for precise 3D scene reconstruction. Proceedings of the 2015 European Conference on Mobile Robots (ECMR), Lincoln, UK.","DOI":"10.1109\/ECMR.2015.7324208"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Aijazi, A.K., Checchin, P., and Trassoudaine, L. (2012, January 13\u201315). Handling Occlusions for Accurate 3D Urban Cartography: A New Approach Based on Characterization and Multiple Passages. Proceedings of the 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization Transmission, Zurich, Switzerland.","DOI":"10.1109\/3DIMPVT.2012.26"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3701","DOI":"10.3390\/rs5083701","article-title":"Automatic Removal of Imperfections and Change Detection for Accurate 3D Urban Cartography by Classification and Incremental Updating","volume":"5","author":"Aijazi","year":"2013","journal-title":"Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","article-title":"FCM: The fuzzy c-means clustering algorithm","volume":"10","author":"Bezdek","year":"1984","journal-title":"Comput. Geosci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1007\/978-981-13-1498-8_73","article-title":"A Dynamic Spatial Fuzzy C-Means Clustering-Based Medical Image Segmentation","volume":"Volume 813","author":"Abraham","year":"2019","journal-title":"Emerging Technologies in Data Mining and Information Security"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"20967","DOI":"10.1109\/ACCESS.2019.2895636","article-title":"Fuzzy C-Means Clustering Interval Type-2 Cerebellar Model Articulation Neural Network for Medical Data Classification","volume":"7","author":"Le","year":"2019","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Cai, Z., Ma, H., and Zhang, L. (2019). A Building Detection Method Based on Semi-Suppressed Fuzzy C-Means and Restricted Region Growing Using Airborne LiDAR. Remote Sens., 11.","DOI":"10.3390\/rs11070848"},{"key":"ref_45","first-page":"9","article-title":"3D model reconstruction from aerial ortho-imagery and LiDAR data","volume":"11","author":"Loutfia","year":"2017","journal-title":"J. Geomat."},{"key":"ref_46","unstructured":"Bezdek, J.C. (2013). Pattern Recognition with Fuzzy Objective Function Algorithms, Springer Science & Business Media."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1016\/S0167-8655(02)00401-4","article-title":"Suppressed fuzzy c-means clustering algorithm","volume":"24","author":"Fan","year":"2003","journal-title":"Pattern Recognit. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1006\/cviu.1999.0832","article-title":"MLESAC: A New Robust Estimator with Application to Estimating Image Geometry","volume":"78","author":"Torr","year":"2000","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/TIM.1976.6312298","article-title":"A circle fitting procedure and its error analysis","volume":"IM-25","year":"1976","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_50","unstructured":"Leica-Geosystems (2019, August 03). Leica ScanStation P20. Available online: https:\/\/w3.leica-geosystems.com\/downloads123\/hds\/hds\/ScanStation_P20\/brochures-datasheet\/Leica_ScanStation_P20_DAT_us.pdf."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"153","DOI":"10.5194\/isprs-annals-III-3-153-2016","article-title":"Detecting and Analyzing Corrosion Spots on the Hull of Large Marine Vessels using Colored 3D LiDAR Point Clouds","volume":"3","author":"Aijazi","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Vihinen, M. (2012). How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis. BMC Genom., 13.","DOI":"10.1186\/1471-2164-13-S4-S2"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.isprsjprs.2019.10.015","article-title":"A Frustum-based probabilistic framework for 3D object detection by fusion of LiDAR and camera data","volume":"159","author":"Gong","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/24\/5345\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:40:10Z","timestamp":1760190010000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/24\/5345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,4]]},"references-count":53,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19245345"],"URL":"https:\/\/doi.org\/10.3390\/s19245345","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,4]]}}}