{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T21:07:43Z","timestamp":1776114463806,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,15]],"date-time":"2018-11-15T00:00:00Z","timestamp":1542240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>State-of-the-art indoor mobile laser scanners are now lightweight and portable enough to be carried by humans. They allow the user to map challenging environments such as multi-story buildings and staircases while continuously walking through the building. The trajectory of the laser scanner is usually discarded in the analysis, although it gives insight about indoor spaces and the topological relations between them. In this research, the trajectory is used in conjunction with the point cloud to subdivide the indoor space into stories, staircases, doorways, and rooms. Analyzing the scanner trajectory as a standalone dataset is used to identify the staircases and to separate the stories. Also, the doors that are traversed by the operator during the scanning are identified by processing only the interesting spots of the point cloud with the help of the trajectory. Semantic information like different space labels is assigned to the trajectory based on the detected doors. Finally, the point cloud is semantically enriched by transferring the labels from the annotated trajectory to the full point cloud. Four real-world datasets with a total of seven stories are used to evaluate the proposed methods. The evaluation items are the total number of correctly detected rooms, doors, and staircases.<\/jats:p>","DOI":"10.3390\/rs10111815","type":"journal-article","created":{"date-parts":[[2018,11,15]],"date-time":"2018-11-15T11:32:47Z","timestamp":1542281567000},"page":"1815","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8721-0905","authenticated-orcid":false,"given":"Ahmed","family":"Elseicy","sequence":"first","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation, University of Twente, P.O. Box 217, 7514 AE Enschede, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7102-4446","authenticated-orcid":false,"given":"Shayan","family":"Nikoohemat","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation, University of Twente, P.O. Box 217, 7514 AE Enschede, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9477-9905","authenticated-orcid":false,"given":"Michael","family":"Peter","sequence":"additional","affiliation":[{"name":"Independent Researcher, 46397 Bocholt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4511-2095","authenticated-orcid":false,"given":"Sander Oude","family":"Elberink","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation, University of Twente, P.O. Box 217, 7514 AE Enschede, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1016\/j.autcon.2010.06.007","article-title":"Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques","volume":"19","author":"Tang","year":"2010","journal-title":"Autom. Constr."},{"key":"ref_2","unstructured":"Okorn, B., Xiong, X., Akinci, B., and Huber, D. (2010, January 17\u201320). Toward Automated Modeling of Floor Plans. Proceedings of the Symposium on 3D Data Processing, Visualization and Transmission, Paris, France."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.autcon.2012.10.006","article-title":"Automatic creation of semantically rich 3D building models from laser scanner data","volume":"31","author":"Xiong","year":"2013","journal-title":"Autom. Constr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.cag.2014.07.005","article-title":"Automatic Room Detection and Reconstruction in Cluttered Indoor Environments with Complex Room Layouts","volume":"44","author":"Mura","year":"2014","journal-title":"Comput. Graph."},{"key":"ref_5","unstructured":"Mozos, O.M., Stachniss, C., and Burgard, W. (2005, January 18\u201322). Supervised Learning of Places from Range Data using AdaBoost. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1177\/0278364909356483","article-title":"Multi-modal semantic place classification","volume":"29","author":"Pronobis","year":"2010","journal-title":"Int. J. Rob. Res."},{"key":"ref_7","first-page":"275","article-title":"Indoor navigation from point clouds: 3D modelling and Obstacle Detection","volume":"41","author":"Boguslawski","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"143","DOI":"10.5194\/isprs-annals-III-1-143-2016","article-title":"Comparison of Zeb1 and Leica C10 Indoor Laser Scanning Point Clouds","volume":"III-1","author":"Sirmacek","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Contreras, L., and Mayol-Cuevas, W. (October, January 28). Trajectory-driven point cloud compression techniques for visual SLAM. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7353365"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"417","DOI":"10.5194\/isprs-archives-XLII-2-W7-417-2017","article-title":"Automatic Indoor Building Reconstruction from Mobile Laser Scanning Data","volume":"XLII-2\/W7","author":"Xie","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_12","first-page":"345","article-title":"Indoor Modelling from Slam-Based Laser Scanner: Door Detection to Envelope Reconstruction","volume":"XLII-2\/W7","author":"Verbree","year":"2017","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Turner, E., and Zakhor, A. (2015). Multistory Floor Plan Generation and Room Labeling of Building Interiors from Laser Range Data, Springer.","DOI":"10.1007\/978-3-319-25117-2_3"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.cag.2015.07.008","article-title":"Automatic Reconstruction of Parametric Building Models from Indoor Point Clouds","volume":"54","author":"Ochmann","year":"2016","journal-title":"Comput. Graph."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"355","DOI":"10.5194\/isprs-annals-IV-2-W4-355-2017","article-title":"Exploiting Indoor Mobile Laser Scanner Trajectories for Semantic Interpretation of Point Clouds","volume":"IV-2\/W4","author":"Nikoohemat","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/cgf.13015","article-title":"Piecewise-planar Reconstruction of Multi-room Interiors with Arbitrary Wall Arrangements","volume":"35","author":"Mura","year":"2016","journal-title":"Comput. Graph. Forum"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mozos, \u00d3.M. (2010). Semantic Labeling of Places with Mobile Robots, Springer Tracts in Advanced Robotics; Springer.","DOI":"10.1007\/978-3-642-11210-2"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Peter, M., Zhong, R., Oude Elberink, S., and Zhou, Q. (2018). Space Subdivision in Indoor Mobile Laser Scanning Point Clouds Based on Scanline Analysis. Sensors, 18.","DOI":"10.3390\/s18061838"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jung, J., Stachniss, C., and Kim, C. (2017). Automatic Room Segmentation of 3D Laser Data Using Morphological Processing. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6070206"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LRA.2017.2651939","article-title":"Automatic Room Segmentation from Unstructured 3-D Data of Indoor Environments","volume":"2","author":"Ambrus","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bormann, R., Jordan, F., Li, W., Hampp, J., and H\u00e4gele, M. (2016, January 16\u201321). Room segmentation: Survey, implementation, and analysis. Proceedings of the IEEE International Conference on Robotics and Automation, Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487234"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shi, L., Kodagoda, S., and Dissanayake, G. (2010, January 18\u201322). Laser range data based semantic labeling of places. Proceedings of the 2010 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5650387"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1109\/TRO.2011.2165372","article-title":"Activity-based Estimation of Human Trajectories","volume":"28","author":"Grzonka","year":"2012","journal-title":"IEEE Trans. Robot."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Alzantot, M., and Youssef, M. (2012, January 6\u20139). CrowdInside: Automatic Construction of Indoor Floorplans. Proceedings of the 20th International Conference on Advances in Geographic Information Systems\u2014SIGSPATIAL\u201912, Redondo Beach, CA, USA.","DOI":"10.1145\/2424321.2424335"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Grzonka, S., Dijoux, F., Karwath, A., and Burgard, W. (2010, January 3\u20137). Mapping indoor environments based on human activity. Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA.","DOI":"10.1109\/ROBOT.2010.5509976"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1111\/tgis.12308","article-title":"Semantic enrichment of octree structured point clouds for multi-story 3D pathfinding","volume":"22","author":"Fichtner","year":"2018","journal-title":"Trans. GIS"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"393","DOI":"10.5194\/isprs-annals-IV-2-W4-393-2017","article-title":"Automatic Generation of Indoor Navigable Space Using a Point Cloud and Its Scanner Trajectory","volume":"IV-2\/W4","author":"Staats","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_28","unstructured":"Friedman, S., Pasula, H., and Fox, D. (2007, January 13\u201317). Voronoi Random Fields: Extracting the Topological Structure of Indoor Environments via Place Labeling. Proceedings of the 20th International Joint Conference on Artifical Intelligence (IJCAI\u201907), Kingston, ON, Canada."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bobkov, D., Kiechle, M., Hilsenbeck, S., and Steinbach, E. (2017, January 10\u201314). Room Segmentation in 3D Point Clouds Using Anisotropic Potential Fields. Proceedings of the 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China.","DOI":"10.1109\/ICME.2017.8019484"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"McCormac, J., Handa, A., Davison, A., and Leutenegger, S. (June, January 29). SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989538"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1109\/TRO.2012.2200990","article-title":"Zebedee: Design of a Spring-Mounted 3-D Range Sensor with Application to Mobile Mapping","volume":"28","author":"Bosse","year":"2012","journal-title":"IEEE Trans. Robot."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.autcon.2017.06.026","article-title":"Automatic building accessibility diagnosis from point clouds","volume":"82","author":"Balado","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.isprsjprs.2014.02.004","article-title":"Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut","volume":"90","author":"Oesau","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","first-page":"321","article-title":"3D Modelling of Interior Spaces: Learning the Language of Indoor Architecture","volume":"XL-5","author":"Khoshelham","year":"2014","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Turner, E., and Zakhor, A. (2012, January 13\u201315). Watertight As-Built Architectural Floor Plans Generated from Laser Range Data. Proceedings of the 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission, Zurich, Switzerland.","DOI":"10.1109\/3DIMPVT.2012.80"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Rocha, J.A.M.R., Times, V.C., Oliveira, G., Alvares, L.O., and Bogorny, V. (2010, January 7\u20139). DB-SMoT: A Direction-Based Spatio-Temporal Clustering Method. Proceedings of the 2010 5th IEEE International Conference Intelligent Systems, London, UK.","DOI":"10.1109\/IS.2010.5548396"},{"key":"ref_37","unstructured":"Zhao, X.-L., and Xu, W.-X. (2009, January 10\u201311). A Clustering-Based Approach for Discovering Interesting Places in a Single Trajectory. Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation, Changsha, China."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1179\/000870406X93490","article-title":"Performance Evaluation of Line Simplification Algorithms for Vector Generalization","volume":"43","author":"Shi","year":"2006","journal-title":"Cartogr. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.compenvurbsys.2009.07.008","article-title":"Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects","volume":"33","author":"Dodge","year":"2009","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jin, P., Cui, T., Wang, Q., and Jensen, C.S. (2016). Effective Similarity Search on Indoor Moving-Object Trajectories, Springer.","DOI":"10.1007\/978-3-319-32049-6_12"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Buchin, M., Driemel, A., Van Kreveld, M., and Sacristan, V. (2011). Segmenting Trajectories: A Framework and Algorithms Using Spatiotemporal Criteria. J. Spat. Inf. Sci.","DOI":"10.5311\/JOSIS.2011.3.66"},{"key":"ref_42","first-page":"112","article-title":"Algorithms for The Reduction of the Number of Points Required to Represent A Digitized Line or Its Caricature","volume":"10","author":"Douglas","year":"1973","journal-title":"Cartogr. Int. J. Geogr. Inf. Geovis."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Meratnia, N., and de By, R.A. (2004). Spatiotemporal Compression Techniques for Moving Point Objects, Springer.","DOI":"10.1007\/978-3-540-24741-8_44"},{"key":"ref_44","first-page":"1","article-title":"Recognising Structure in Laser Scanner Point Clouds","volume":"Volume 46","author":"Thies","year":"2004","journal-title":"Information Sciences"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"367","DOI":"10.5194\/isprs-archives-XLII-2-W7-367-2017","article-title":"The ISPRS Benchmark on Indoor Modelling","volume":"42","author":"Khoshelham","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"173","DOI":"10.5194\/isprsannals-II-3-173-2014","article-title":"Design of an indoor mapping system using three 2D laser scanners and 6 DOF SLAM","volume":"II-3","author":"Vosselman","year":"2014","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1815\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:30:01Z","timestamp":1760196601000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1815"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,15]]},"references-count":46,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["rs10111815"],"URL":"https:\/\/doi.org\/10.3390\/rs10111815","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,15]]}}}