{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:21:46Z","timestamp":1760242906877,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2016,11,16]],"date-time":"2016-11-16T00:00:00Z","timestamp":1479254400000},"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, we propose a novel approach to obtain accurate 3D reconstructions of large-scale environments by means of a mobile acquisition platform. The system incorporates a Velodyne LiDAR scanner, as well as a Point Grey Ladybug panoramic camera system. It was designed with genericity in mind, and hence, it does not make any assumption about the scene or about the sensor set-up. The main novelty of this work is that the proposed LiDAR mapping approach deals explicitly with the inhomogeneous density of point clouds produced by LiDAR scanners. To this end, we keep track of a global 3D map of the environment, which is continuously improved and refined by means of a surface reconstruction technique. Moreover, we perform surface analysis on consecutive generated point clouds in order to assure a perfect alignment with the global 3D map. In order to cope with drift, the system incorporates loop closure by determining the pose error and propagating it back in the pose graph. Our algorithm was exhaustively tested on data captured at a conference building, a university campus and an industrial site of a chemical company. Experiments demonstrate that it is capable of generating highly accurate 3D maps in very challenging environments. We can state that the average distance of corresponding point pairs between the ground truth and estimated point cloud approximates one centimeter for an area covering approximately 4000 m     2    . To prove the genericity of the system, it was tested on the well-known Kitti vision benchmark. The results show that our approach competes with state of the art methods without making any additional assumptions.<\/jats:p>","DOI":"10.3390\/s16111923","type":"journal-article","created":{"date-parts":[[2016,11,16]],"date-time":"2016-11-16T16:23:04Z","timestamp":1479313384000},"page":"1923","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["3D Scene Reconstruction Using Omnidirectional Vision and LiDAR: A Hybrid Approach"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3986-823X","authenticated-orcid":false,"given":"Michiel","family":"Vlaminck","sequence":"first","affiliation":[{"name":"Department of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, iMinds, Ghent 9000, Belgium"}]},{"given":"Hiep","family":"Luong","sequence":"additional","affiliation":[{"name":"Department of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, iMinds, Ghent 9000, Belgium"}]},{"given":"Werner","family":"Goeman","sequence":"additional","affiliation":[{"name":"Sweco\/Grontmij, Ghent 9000, Belgium"}]},{"given":"Wilfried","family":"Philips","sequence":"additional","affiliation":[{"name":"Department of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, iMinds, Ghent 9000, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TRO.2015.2463671","article-title":"ORB-SLAM: A Versatile and Accurate Monocular SLAM System","volume":"31","author":"Montiel","year":"2015","journal-title":"IEEE Trans. Robot."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Caruso, D., Engel, J., and Cremers, D. (October, January 28). Large-Scale direct SLAM for omnidirectional cameras. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7353366"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Billings, S.D., Boctor, E.M., and Taylor, R.H. (2015). Iterative Most-Likely Point Registration (IMLP): A Robust Algorithm for Computing Optimal Shape Alignment. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0117688"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s10514-013-9327-2","article-title":"Comparing ICP Variants on Real-world Data Sets","volume":"34","author":"Pomerleau","year":"2013","journal-title":"Auton. Robot."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"228","DOI":"10.3390\/s16020228","article-title":"Enhanced ICP for the Registration of Large-Scale 3D Environment Models: An Experimental Study","volume":"16","author":"Han","year":"2016","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1007\/978-3-642-40686-7_32","article-title":"Efficient Large-Scale 3D Mobile Mapping and Surface Reconstruction of an Underground Mine","volume":"92","author":"Zlot","year":"2012","journal-title":"Field Serv. Robot."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1002\/rob.20209","article-title":"6D SLAM\u20143D Mapping Outdoor Environments: Research Articles","volume":"24","author":"Lingemann","year":"2007","journal-title":"J. Field Robot."},{"key":"ref_9","unstructured":"Hong, S., Ko, H., and Kim, J. (2010, January 3\u20138). VICP: Velocity updating iterative closest point algorithm. Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), Anchorage, Alaska."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.isprsjprs.2013.02.019","article-title":"Towards 3D LiDAR point cloud registration improvement using optimal neighborhood knowledge","volume":"79","author":"Gressin","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Moosmann, F., and Stiller, C. (2011, January 5\u20139). Velodyne SLAM. Proceedings of the IEEE Intelligent Vehicles Symposium, Baden-Baden, Germany.","DOI":"10.1109\/IVS.2011.5940396"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sarvrood, Y.B., Hosseinyalamdary, S., and Gao, Y. (2016). Visual-LiDAR Odometry Aided by Reduced IMU. ISPRS Int. J. Geo Inf., 5.","DOI":"10.3390\/ijgi5010003"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Trinkle, J., Matsuoka, Y., and Castellanos, J.A. (2009). Robotics: Science and Systems, The MIT Press.","DOI":"10.7551\/mitpress\/8727.001.0001"},{"key":"ref_14","unstructured":"Biber, P., and Stra\u00dfer, W. (November, January 27). The normal distributions transform: A new approach to laser scan matching. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA."},{"key":"ref_15","unstructured":"Sun, Y.X., and Li, J.L. (2013, January 22\u201323). Mapping of Rescue Environment Based on NDT Scan Matching. Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013), Hangzhou, China."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.robot.2014.08.008","article-title":"Generic NDT Mapping in Dynamic Environments and Its Application for Lifelong SLAM","volume":"69","author":"Einhorn","year":"2015","journal-title":"Robot. Auton. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Singh, S. (2014, January 13\u201315). LOAM: LiDAR Odometry and Mapping in Real-time. Proceedings of the Robotics: Science and Systems Conference (RSS), Berkeley, CA, USA.","DOI":"10.15607\/RSS.2014.X.007"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Singh, S. (2015, January 26\u201330). Visual-LiDAR Odometry and Mapping: Low-drift, Robust, and Fast. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, Washington, DC, USA.","DOI":"10.1109\/ICRA.2015.7139486"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1109\/TRO.2010.2042989","article-title":"Fast Registration Based on Noisy Planes with Unknown Correspondences for 3D Mapping","volume":"26","author":"Pathak","year":"2010","journal-title":"IEEE Trans. Robot."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Grant, W., Voorhies, R., 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 (IROS), Tokyo, Japan.","DOI":"10.1109\/IROS.2013.6696980"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1002\/rob.21457","article-title":"Planar Segment Based Three-dimensional Point Cloud Registration in Outdoor Environments","volume":"30","author":"Xiao","year":"2013","journal-title":"J. Field Robot."},{"key":"ref_23","unstructured":"Low, K.L. (2004). Linear Least-Squares Optimization for Point-to Plane ICP Surface Registration, University of North Carolina. Technical Report 4."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1090\/S0025-5718-98-00974-0","article-title":"The Approximation Power of Moving Least-squares","volume":"67","author":"Levin","year":"1998","journal-title":"Math. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1109\/TRO.2012.2197158","article-title":"Bags of Binary Words for Fast Place Recognition in Image Sequences","volume":"28","author":"Tardos","year":"2012","journal-title":"IEEE Trans. Robot."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1145\/325165.325242","article-title":"Animating Rotation with Quaternion Curves","volume":"19","author":"Shoemake","year":"1985","journal-title":"SIGGRAPH Comput. Graph."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wu, C. (2013, January 1\u20138). Towards Linear-Time Incremental Structure from Motion. Proceedings of the 2013 International Conference on 3D Vision, Sydney, Australia.","DOI":"10.1109\/3DV.2013.25"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/11\/1923\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:35:38Z","timestamp":1760211338000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/11\/1923"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,16]]},"references-count":28,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2016,11]]}},"alternative-id":["s16111923"],"URL":"https:\/\/doi.org\/10.3390\/s16111923","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2016,11,16]]}}}