{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T02:32:15Z","timestamp":1768789935412,"version":"3.49.0"},"reference-count":92,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,26]],"date-time":"2019-11-26T00:00:00Z","timestamp":1574726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007156","name":"Innovation and Technology Commission - Hong Kong","doi-asserted-by":"publisher","award":["K-ZS0R"],"award-info":[{"award-number":["K-ZS0R"]}],"id":[{"id":"10.13039\/501100007156","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["B-Q61E"],"award-info":[{"award-number":["B-Q61E"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004377","name":"Hong Kong Polytechnic University","doi-asserted-by":"publisher","award":["1-ZVN6"],"award-info":[{"award-number":["1-ZVN6"]}],"id":[{"id":"10.13039\/501100004377","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Over the last decade, increasing demands for building interior mapping have brought the challenge of effectively and efficiently acquiring geometric information. Most mobile mapping methods rely on the integration of Simultaneous Localization And Mapping (SLAM) and costly Inertial Measurement Units (IMUs). Meanwhile, the methods also suffer misalignment errors caused by the low-resolution inhomogeneous point clouds captured using multi-line Mobile Laser Scanners (MLSs). While point-based alignments between such point clouds are affected by the highly dynamic moving patterns of the platform, plane-based methods are limited by the poor quality of the planes extracted, which reduce the methods\u2019 robustness, reliability, and applicability. To alleviate these issues, we proposed and developed a method for plane extraction from low-resolution inhomogeneous point clouds. Based on the definition of virtual scanlines and the Enhanced Line Simplification (ELS) algorithm, the method extracts feature points, generates line segments, forms patches, and merges multi-direction fractions to form planes. The proposed method reduces the over-segmentation fractions caused by measurement noise and scanline curvature. A dedicated plane-to-plane point cloud alignment workflow based on the proposed plane extraction method was created to demonstrate the method\u2019s application. The implementation of the coarse-to-fine procedure and the shortest-path initialization strategy eliminates the necessity of IMUs in mobile mapping. A mobile mapping prototype was designed to test the performance of the proposed methods. The results show that the proposed workflow and hardware system achieves centimeter-level accuracy, which suggests that it can be applied to mobile mapping and sensor fusion.<\/jats:p>","DOI":"10.3390\/rs11232789","type":"journal-article","created":{"date-parts":[[2019,11,26]],"date-time":"2019-11-26T10:57:27Z","timestamp":1574765847000},"page":"2789","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Novel Method for Plane Extraction from Low-Resolution Inhomogeneous Point Clouds and its Application to a Customized Low-Cost Mobile Mapping System"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3798-1538","authenticated-orcid":false,"given":"Wenzheng","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong"}]},{"given":"Wenzhong","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong"}]},{"given":"Haodong","family":"Xiang","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong"}]},{"given":"Ke","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,26]]},"reference":[{"key":"ref_1","unstructured":"Applanix Corp (2015, December 08). Land Solutions: TIMMS Indoor Mapping. Available online: http:\/\/www.applanix.com\/solutions\/land\/timms.html."},{"key":"ref_2","unstructured":"(2015, December 08). ViAmetris 3D Mapping Viametris|Continuous Indoor Mobile Scanner iMS3D. Available online: https:\/\/www.viametris.com\/ims3d."},{"key":"ref_3","unstructured":"NavVis US Inc. (2019, April 12). NavVis|M6. Available online: https:\/\/www.navvis.com\/m6."},{"key":"ref_4","unstructured":"(2015, June 08). Leica Geosystems AG Leica Pegasus: Backpack-Award-Winning Wearable Reality Capture-Indoors, Outdoors, Anywhere. Available online: http:\/\/www.leica-geosystems.com\/en\/Leica-PegasusBackpack_106730.htm."},{"key":"ref_5","unstructured":"(2016, October 05). Google Introducing Cartographer. Available online: https:\/\/opensource.googleblog.com\/2016\/10\/introducing-cartographer.html."},{"key":"ref_6","unstructured":"(2019, April 11). GreenValley International LiBackpack-Mobile Handheld LiDAR-3D Mapping System. Available online: https:\/\/greenvalleyintl.com\/hardware\/libackpack\/."},{"key":"ref_7","unstructured":"(2019, April 11). ViAmetris 3D Mapping Viametris|Backpack Mobile Scanner bMS3D LD5+. Available online: https:\/\/www.viametris.com\/bms3d4cams."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13","DOI":"10.5194\/isprs-annals-IV-1-13-2018","article-title":"Development of A Portable High Performance Mobile Mapping System Using The Robot Operating System","volume":"IV\u20131","author":"Blaser","year":"2018","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_9","first-page":"17","article-title":"A man-portable, IMU-free mobile mapping system","volume":"II","author":"Borrmann","year":"2015","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, T., Carlberg, M., Chen, G., Chen, J., Kua, J., and Zakhor, A. (2010, January 15\u201317). Indoor localization and visualization using a human-operated backpack system. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, Z\u00fcrich, Switzerland.","DOI":"10.1109\/IPIN.2010.5646820"},{"key":"ref_11","unstructured":"Occipital Inc. (2019, July 16). PX-80 Overview. Available online: http:\/\/labs.paracosm.io\/px-80-overview."},{"key":"ref_12","unstructured":"(2019, April 11). GeoSLAM GeoSLAM-The Experts in \u201cGo-Anywhere\u201d 3D Mobile Mapping Technology. Available online: https:\/\/geoslam.com\/."},{"key":"ref_13","unstructured":"(2019, July 15). Kaarta Stencil 2\u2013KAARTA. Available online: https:\/\/www.kaarta.com\/products\/stencil-2\/."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lehtola, V., Kaartinen, H., N\u00fcchter, A., Kaijaluoto, R., Kukko, A., Litkey, P., Honkavaara, E., Rosnell, T., Vaaja, M., and Virtanen, J.-P. (2017). Comparison of the Selected State-Of-The-Art 3D Indoor Scanning and Point Cloud Generation Methods. Remote Sens., 9.","DOI":"10.3390\/rs9080796"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nocerino, E., Menna, F., Remondino, F., Toschi, I., and Rodr\u00edguez-Gonz\u00e1lvez, P. (2017, January 26\u201327). Investigation of indoor and outdoor performance of two portable mobile mapping systems. Proceedings of the Videometrics, Range Imaging, and Applications XIV, Munich, Germany.","DOI":"10.1117\/12.2270761"},{"key":"ref_16","first-page":"637","article-title":"Investigation of Geometric Performance of An Indoor Mobile Mapping System","volume":"XLII\u20132","author":"Maboudi","year":"2018","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/BF01427149","article-title":"Iterative point matching for registration of free-form curves and surfaces","volume":"13","author":"Zhang","year":"1994","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Singh, S. (2014, January 13\u201317). LOAM: Lidar Odometry and Mapping in Real-time. Proceedings of the Robotics: Science and Systems (RSS), Berkeley, CA, USA.","DOI":"10.15607\/RSS.2014.X.007"},{"key":"ref_19","unstructured":"Olsson, C., Kahl, F., and Oskarsson, M. (2006, January 17\u201322). The Registration Problem Revisited: Optimal Solutions From Points, Lines and Planes. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), IEEE, New York, NY, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bogoslavskyi, I., and Stachniss, C. (2016, January 9\u201314). Fast range image-based segmentation of sparse 3D laser scans for online operation. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759050"},{"key":"ref_21","first-page":"55","article-title":"Calibration and Stability Analysis of the VLP-16 Laser Scanner","volume":"XL","author":"Glennie","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Grant, W.S., Voorhies, R.C., and Itti, L. (2013, January 3\u20137). Finding planes in LiDAR point clouds for real-time registration. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan.","DOI":"10.1109\/IROS.2013.6696980"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1007\/s10514-018-9794-6","article-title":"Efficient Velodyne SLAM with point and plane features","volume":"43","author":"Grant","year":"2019","journal-title":"Auton. Robots"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Karam, S., Vosselman, G., Peter, M., Hosseinyalamdary, S., and Lehtola, V. (2019). Design, Calibration, and Evaluation of a Backpack Indoor Mobile Mapping System. Remote Sens., 11.","DOI":"10.3390\/rs11080905"},{"key":"ref_25","first-page":"257","article-title":"Point cloud segmentation for urban scene classification","volume":"XL-7\/W2","author":"Vosselman","year":"2013","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nguyen, A., and Le, B. (2013, January 12\u201315). 3D point cloud segmentation: A survey. Proceedings of the 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM), IEEE, Manila, Philippines.","DOI":"10.1109\/RAM.2013.6758588"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1111\/j.1467-8659.2007.01016.x","article-title":"Efficient RANSAC for Point-Cloud Shape Detection","volume":"26","author":"Schnabel","year":"2007","journal-title":"Comput. Graph. Forum"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.autcon.2014.12.015","article-title":"Segmentation of building point cloud models including detailed architectural\/structural features and MEP systems","volume":"51","author":"Dimitrov","year":"2015","journal-title":"Autom. Constr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.apergo.2015.01.005","article-title":"The validity of the first and second generation Microsoft KinectTM for identifying joint center locations during static postures","volume":"49","author":"Xu","year":"2015","journal-title":"Appl. Ergon."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.isprsjprs.2018.01.013","article-title":"An efficient global energy optimization approach for robust 3D plane segmentation of point clouds","volume":"137","author":"Dong","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xu, B., Jiang, W., Shan, J., Zhang, J., and Li, L. (2015). Investigation on the Weighted RANSAC Approaches for Building Roof Plane Segmentation from LiDAR Point Clouds. Remote Sens., 8.","DOI":"10.3390\/rs8010005"},{"key":"ref_33","first-page":"339","article-title":"A Review of Point Clouds Segmentation and Classification Algorithms","volume":"XLII-2\/W3","author":"Grilli","year":"2017","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1109\/TPAMI.2013.2296310","article-title":"The Random Cluster Model for Robust Geometric Fitting","volume":"36","author":"Pham","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/34.982886","article-title":"ICP registration using invariant features","volume":"24","author":"Sharp","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Adan, A., and Huber, D. (2011, January 16\u201319). 3D Reconstruction of Interior Wall Surfaces under Occlusion and Clutter. Proceedings of the 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, IEEE, Hangzhou, China.","DOI":"10.1109\/3DIMPVT.2011.42"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Shi, W., Ahmed, W., Li, N., Fan, W., Xiang, H., and Wang, M. (2018). Semantic Geometric Modelling of Unstructured Indoor Point Cloud. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8010009"},{"key":"ref_38","unstructured":"Deschaud, J., and Goulette, F. (2010, January 17\u201320). A Fast and Accurate Plane Detection Algorithm for Large Noisy Point Clouds Using Filtered Normals and Voxel Growing. Proceedings of the 3DPVT, Paris, France."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.1016\/j.robot.2013.07.001","article-title":"Three-dimensional point cloud plane segmentation in both structured and unstructured environments","volume":"61","author":"Xiao","year":"2013","journal-title":"Robot. Auton. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.isprsjprs.2015.01.011","article-title":"Octree-based region growing for point cloud segmentation","volume":"104","author":"Vo","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.isprsjprs.2019.05.007","article-title":"Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density","volume":"153","author":"Li","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Teboul, O., Simon, L., Koutsourakis, P., and Paragios, N. (2010, January 13\u201318). Segmentation of building facades using procedural shape priors. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540068"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"657","DOI":"10.20965\/ijat.2017.p0657","article-title":"Line-Based Planar Structure Extraction from a Point Cloud with an Anisotropic Distribution","volume":"11","author":"Miyazaki","year":"2017","journal-title":"Int. J. Autom. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.autcon.2017.12.029","article-title":"6D DBSCAN-based segmentation of building point clouds for planar object classification","volume":"88","author":"Czerniawski","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Georgiev, K., Creed, R.T., and Lakaemper, R. (2011, January 25\u201330). Fast plane extraction in 3D range data based on line segments. Proceedings of the 2011 IEEE\/RSJ International Conference on Intelligent Robots and Systems, IEEE, San Francisco, CA, USA.","DOI":"10.1109\/IROS.2011.6048584"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.autcon.2015.07.005","article-title":"Mobile Laser Scanner data for automatic surface detection based on line arrangement","volume":"58","author":"Cabo","year":"2015","journal-title":"Autom. Constr."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, W., Sakurada, K., and Kawaguchi, N. (2016). Incremental and Enhanced Scanline-Based Segmentation Method for Surface Reconstruction of Sparse LiDAR Data. Remote Sens., 8.","DOI":"10.3390\/rs8110967"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.isprsjprs.2019.03.006","article-title":"Planar surface detection for sparse and heterogeneous mobile laser scanning point clouds","volume":"151","author":"Nguyen","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/3DRes.03(2010)06","article-title":"Automatic Scan Registration Using 3D Linear And Planar Features","volume":"1","author":"Yao","year":"2010","journal-title":"3D Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.aei.2011.08.009","article-title":"Plane-based Registration of Construction Laser Scans with 3D\/4D Building Models","volume":"26","year":"2012","journal-title":"Adv. Eng. Inform."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.14358\/PERS.80.11.1029","article-title":"Association-Matrix-Based Sample Consensus Approach for Automated Registration of Terrestrial Laser Scans Using Linear Features","volume":"80","author":"Habib","year":"2014","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"04016006","DOI":"10.1061\/(ASCE)SU.1943-5428.0000174","article-title":"A Closed-Form Solution for Coarse Registration of Point Clouds Using Linear Features","volume":"142","author":"Fangning","year":"2016","journal-title":"J. Surv. Eng."},{"key":"ref_53","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_54","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 Vis. Comput."},{"key":"ref_55","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_56","unstructured":"Rusinkiewicz, S., and Levoy, M. (June, January 28). Efficient variants of the ICP algorithm. Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling, Quebec City, QC, Canada."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.patrec.2015.03.009","article-title":"A simple and effective relevance-based point sampling for 3D shapes","volume":"59","author":"Albarelli","year":"2015","journal-title":"Pattern Recognit. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1109\/TASE.2018.2802725","article-title":"DNSS: Dual-Normal-Space Sampling for 3-D ICP Registration","volume":"16","author":"Kwok","year":"2019","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"127","DOI":"10.5194\/isprsannals-II-5-W2-127-2013","article-title":"Generation and weighting of 3D point correspondences for improved registration of RGB-D data","volume":"II-5\/W2","author":"Khoshelham","year":"2013","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s10044-013-0324-z","article-title":"A robust and outlier-adaptive method for non-rigid point registration","volume":"17","author":"Gao","year":"2014","journal-title":"Pattern Anal. Appl."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1002\/rob.20204","article-title":"Scan registration for autonomous mining vehicles using 3D-NDT","volume":"24","author":"Magnusson","year":"2007","journal-title":"J. Field Robot."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1023\/A:1007957421070","article-title":"Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans","volume":"18","author":"Lu","year":"1997","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_63","first-page":"53","article-title":"ICL: Iterative closest line a novel point cloud registration algorithm based on linear features","volume":"10","author":"Alshawa","year":"2007","journal-title":"Ekscentar"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1080\/02533839.2008.9671456","article-title":"Registration of ground-based LiDAR point clouds by means of 3D line features","volume":"31","author":"Jaw","year":"2008","journal-title":"J. Chin. Inst. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Lu, Z., Baek, S., and Lee, S. (2008, January 21\u201324). Robust 3D Line Extraction from Stereo Point Clouds. Proceedings of the 2008 IEEE Conference on Robotics, Automation and Mechatronics, IEEE, Chengdu, China.","DOI":"10.1109\/RAMECH.2008.4681439"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"4012","DOI":"10.1016\/j.patcog.2015.06.008","article-title":"Closed form line-segment extraction using the Hough transform","volume":"48","author":"Xu","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Poppinga, J., Vaskevicius, N., Birk, A., and Pathak, K. (2008, January 22\u201326). Fast plane detection and polygonalization in noisy 3D range images. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4650729"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1002\/rob.20322","article-title":"Online three-dimensional SLAM by registration of large planar surface segments and closed-form pose-graph relaxation","volume":"27","author":"Pathak","year":"2010","journal-title":"J. Field Robot."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"173","DOI":"10.5194\/isprsannals-I-3-173-2012","article-title":"Automatic registration of terrestrial laser scanner point clouds using natural planar surfaces","volume":"I\u20133","author":"Theiler","year":"2012","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ulas, C., and Temeltas, H. (2012, January 24\u201327). Plane-feature based 3D outdoor SLAM with Gaussian filters. Proceedings of the IEEE International Conference on Vehicular Electronics and Safety (ICVES 2012), Istanbul, Turkey.","DOI":"10.1109\/ICVES.2012.6294326"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.robot.2017.03.013","article-title":"Fast planar surface 3D SLAM using LIDAR","volume":"92","author":"Lenac","year":"2017","journal-title":"Robot. Auton. Syst."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1508","DOI":"10.1177\/0278364910392405","article-title":"Probabilistic multi-level maps from LIDAR data","volume":"30","author":"Rivadeneyra","year":"2011","journal-title":"Int. J. Robot. Res."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Lee, Y. (2015, January 24\u201328). A reliable range-free indoor localization method for mobile robots. Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, Sweden.","DOI":"10.1109\/CoASE.2015.7294166"},{"key":"ref_74","unstructured":"Chen, H.H. (1990, January 4\u20137). Pose determination from line-to-plane correspondences: Existence condition and closed-form solutions. Proceedings of the Third International Conference on Computer Vision, Osaka, Japan."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s10851-006-0450-y","article-title":"A Minimal Solution to the Generalised 3-Point Pose Problem","volume":"27","year":"2007","journal-title":"J. Math. Imaging Vis."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s11263-012-0576-x","article-title":"A Theory of Minimal 3D Point to 3D Plane Registration and Its Generalization","volume":"102","author":"Ramalingam","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1016\/S0098-3004(02)00009-2","article-title":"A correction to the Douglas\u2013Peucker line generalization algorithm","volume":"28","author":"Ebisch","year":"2002","journal-title":"Comput. Geosci."},{"key":"ref_78","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_79","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1145\/3072959.3054739","article-title":"BundleFusion: Real-time globally consistent 3D reconstruction using on-the-fly surface reintegration","volume":"36","author":"Dai","year":"2017","journal-title":"ACM Trans. Graph."},{"key":"ref_80","unstructured":"Biber, P., and Strasser, W. (2003, January 27\u201331). The normal distributions transform: A new approach to laser scan matching. Proceedings of the 2003 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), Las Vegas, NV, USA."},{"key":"ref_81","unstructured":"Magnusson, M. (2009). The Three-Dimensional Normal-Distributions Transform, University of Massachusetts Amherst."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s10514-016-9548-2","article-title":"Low-drift and real-time lidar odometry and mapping","volume":"41","author":"Zhang","year":"2017","journal-title":"Auton. Robot."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Geneva, P., Eckenhoff, K., Yang, Y., and Huang, G. (2018, January 1\u20135). LIPS: LiDAR-Inertial 3D Plane SLAM. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594463"},{"key":"ref_84","unstructured":"Kummerle, R., Grisetti, G., Strasdat, H., Konolige, K., and Burgard, W. (2011, January 9\u201313). G2o: A general framework for graph optimization. Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Papon, J., Abramov, A., Schoeler, M., and Worgotter, F. (2013, January 23\u201328). Voxel Cloud Connectivity Segmentation-Supervoxels for Point Clouds. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.264"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., and Cousins, S. (2011, January 9\u201313). 3D is here: Point Cloud Library (PCL). Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980567"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"69","DOI":"10.15292\/geodetski-vestnik.2016.01.69-97","article-title":"The Reliability of RANSAC Method When Estimating Geometric Object Parameters","volume":"60","author":"Kregar","year":"2016","journal-title":"Geod. Vestn."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"16710","DOI":"10.3390\/s150716710","article-title":"LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments","volume":"15","author":"Tang","year":"2015","journal-title":"Sensors"},{"key":"ref_89","first-page":"377","article-title":"3D Data Acquisition Based on OpenCV for Close-range Photogrammetry Applications","volume":"XLII-1\/W1","year":"2017","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_90","first-page":"551","article-title":"Comparison of Point Cloud Registration Algorithms for Better Result Assessment\u2013Towards An Open-source Solution","volume":"XLII\u20132","author":"Lachat","year":"2018","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s12518-018-0221-7","article-title":"Point Clouds by SLAM-based Mobile Mapping Systems: Accuracy And Geometric Content Validation in Multisensor Survey And Stand-alone Acquisition","volume":"10","author":"Sammartano","year":"2018","journal-title":"Appl. Geomat."},{"key":"ref_92","unstructured":"Maboudi, M., B\u00e1nhidi, D., and Gerke, M. (2017, January 7\u20138). Evaluation of Indoor Mobile Mapping Systems. Proceedings of the 20th Application-oriented Workshop on Measuring, Modeling, Processing and Analysis of 3D-Data Gesellschaft zur F\u00f6rderung angewandter Informatik, Berlin, Germany."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/23\/2789\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:37:36Z","timestamp":1760189856000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/23\/2789"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,26]]},"references-count":92,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["rs11232789"],"URL":"https:\/\/doi.org\/10.3390\/rs11232789","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,26]]}}}