{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T10:53:43Z","timestamp":1779101623231,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:00:00Z","timestamp":1688342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science and Engineering Research Council of Canada\u2013NSERC","award":["CRDPJ 537080-18"],"award-info":[{"award-number":["CRDPJ 537080-18"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Mobile Mapping System (MMS) plays a crucial role in generating accurate 3D maps for a wide range of applications. However, traditional MMS that utilizes tilted LiDAR (light detection and ranging) faces limitations in capturing comprehensive environmental data. We propose the \u201cPVL-Cartographer\u201d SLAM (Simultaneous Localization And Mapping) approach for MMS to address these limitations. This proposed system incorporates multiple sensors to yield dependable and precise mapping and localization. It consists of two subsystems: early fusion and intermediate fusion. In early fusion, range maps are created from LiDAR points within a panoramic image space, simplifying the integration of visual features. The SLAM system accommodates both visual features with and without augmented ranges. In intermediate fusion, camera and LiDAR nodes are merged using a pose graph, with constraints between nodes derived from IMU (Inertial Measurement Unit) data. Comprehensive testing in challenging outdoor settings demonstrates that the proposed SLAM system can generate trustworthy outcomes even in feature-scarce environments. Ultimately, our suggested PVL-Cartographer system effectively and accurately addresses the MMS localization and mapping challenge.<\/jats:p>","DOI":"10.3390\/rs15133383","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T01:38:32Z","timestamp":1688434712000},"page":"3383","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["PVL-Cartographer: Panoramic Vision-Aided LiDAR Cartographer-Based SLAM for Maverick Mobile Mapping System"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5831-6882","authenticated-orcid":false,"given":"Yujia","family":"Zhang","sequence":"first","affiliation":[{"name":"The Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6595-0723","authenticated-orcid":false,"given":"Jungwon","family":"Kang","sequence":"additional","affiliation":[{"name":"The Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5127-8358","authenticated-orcid":false,"given":"Gunho","family":"Sohn","sequence":"additional","affiliation":[{"name":"The Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Huai, J., Zhang, Y., and Yilmaz, A. (October, January 28). Real-time large scale 3D reconstruction by fusing kinect and imu data. Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-3\/W5, 2015 ISPRS Geospatial Week 2015, La Grande Motte, France.","DOI":"10.5194\/isprsannals-II-3-W5-491-2015"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alsadik, B. (2020). Ideal angular orientation of selected 64-channel multi beam lidars for mobile mapping systems. Remote Sens., 12.","DOI":"10.3390\/rs12030510"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lin, M., Cao, Q., and Zhang, H. (2018, January 18\u201320). PVO: Panoramic visual odometry. Proceedings of the 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM), Singapore.","DOI":"10.1109\/ICARM.2018.8610700"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tardif, J.P., Pavlidis, Y., and Daniilidis, K. (2008, January 22\u201326). Monocular visual odometry in urban environments using an omnidirectional camera. Proceedings of the 2008 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4651205"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"119","DOI":"10.3390\/s130100119","article-title":"GPS-supported visual SLAM with a rigorous sensor model for a panoramic camera in outdoor environments","volume":"13","author":"Shi","year":"2012","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.isprsjprs.2019.11.014","article-title":"Panoramic SLAM from a multiple fisheye camera rig","volume":"159","author":"Ji","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sumikura, S., Shibuya, M., and Sakurada, K. (2019, January 21\u201325). OpenVSLAM: A versatile visual SLAM framework. Proceedings of the 27th ACM International Conference on Multimedia, Nice, FL, USA.","DOI":"10.1145\/3343031.3350539"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, J., Kaess, M., and Singh, S. (2014, January 14\u201318). Real-time depth enhanced monocular odometry. Proceedings of the 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA.","DOI":"10.1109\/IROS.2014.6943269"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Graeter, J., Wilczynski, A., and Lauer, M. (2018, January 1\u20135). Limo: Lidar-monocular visual odometry. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594394"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/MITS.2010.939925","article-title":"A tutorial on graph-based SLAM","volume":"2","author":"Grisetti","year":"2010","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-up robust features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1109\/TRO.2017.2705103","article-title":"Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras","volume":"33","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.1109\/TRO.2021.3075644","article-title":"Orb-slam3: An accurate open-source library for visual, visual\u2013inertial, and multimap slam","volume":"37","author":"Campos","year":"2021","journal-title":"IEEE Trans. Robot."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Engel, J., Sch\u00f6ps, T., and Cremers, D. (2014, January 6\u201312). LSD-SLAM: Large-scale direct monocular SLAM. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Proceedings, Part II 13, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10605-2_54"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TPAMI.2017.2658577","article-title":"Direct sparse odometry","volume":"40","author":"Engel","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kerl, C., Sturm, J., and Cremers, D. (2013, January 6\u201310). Robust odometry estimation for RGB-D cameras. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6631104"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1109\/TRO.2013.2279412","article-title":"3-D mapping with an RGB-D camera","volume":"30","author":"Endres","year":"2013","journal-title":"IEEE Trans. Robot."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1177\/0278364911434148","article-title":"RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments","volume":"31","author":"Henry","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohi, P., Shotton, J., Hodges, S., and Fitzgibbon, A. (2011, January 26\u201329). Kinectfusion: Real-time dense surface mapping and tracking. Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality, Basel, Switzerland.","DOI":"10.1109\/ISMAR.2011.6092378"},{"key":"ref_22","unstructured":"Nie\u00dfner, M., Dai, A., and Fisher, M. (2014, January 7\u201311). Combining Inertial Navigation and ICP for Real-time 3D Surface Reconstruction. Proceedings of the Eurographics (Short Papers), Strasbourg, France."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kang, J., Zhang, Y., Liu, Z., Sit, A., and Sohn, G. (2021, January 6\u201310). RPV-SLAM: Range-augmented panoramic visual SLAM for mobile mapping system with panoramic camera and tilted LiDAR. Proceedings of the 2021 20th International Conference on Advanced Robotics (ICAR), Ljubljana, Slovenia.","DOI":"10.1109\/ICAR53236.2021.9659458"},{"key":"ref_24","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, Rome, Italy.","DOI":"10.15607\/RSS.2014.X.007"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The kitti dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_26","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 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139486"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lin, J., and Zhang, F. (August, January 31). Loam livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197440"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, L., Kong, X., Zhao, X., Li, W., Wen, F., Zhang, H., and Liu, Y. (June, January 30). SA-LOAM: Semantic-aided LiDAR SLAM with loop closure. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9560884"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mendes, E., Koch, P., and Lacroix, S. (2016, January 23\u201327). ICP-based pose-graph SLAM. Proceedings of the 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Lausanne, Switzerland.","DOI":"10.1109\/SSRR.2016.7784298"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Behley, J., and Stachniss, C. (2018, January 26\u201330). Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments. Proceedings of the Robotics: Science and Systems, Pittsburgh, PA, USA.","DOI":"10.15607\/RSS.2018.XIV.016"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, X., Milioto, A., Palazzolo, E., Giguere, P., Behley, J., and Stachniss, C. (2019, January 3\u20138). Suma++: Efficient lidar-based semantic slam. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967704"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shan, T., and Englot, B. (2018, January 1\u20135). Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594299"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shan, T., Englot, B., Meyers, D., Wang, W., Ratti, C., and Rus, D. (January, January 24). Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341176"},{"key":"ref_34","unstructured":"Shan, T., Englot, B., Ratti, C., and Daniela, R. (June, January 30). LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hess, W., Kohler, D., Rapp, H., and Andor, D. (2016, January 16\u201321). Real-time loop closure in 2D LIDAR SLAM. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487258"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Dwijotomo, A., Abdul Rahman, M.A., Mohammed Ariff, M.H., Zamzuri, H., and Wan Azree, W.M.H. (2020). Cartographer slam method for optimization with an adaptive multi-distance scan scheduler. Appl. Sci., 10.","DOI":"10.3390\/app10010347"},{"key":"ref_37","unstructured":"Remondino, F., Georgopoulos, A., Gonz\u00e1lez-Aguilera, D., and Agrafiotis, P. (2018). Latest Developments in Reality-Based 3D Surveying and Modelling, MDPI."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Elhashash, M., Albanwan, H., and Qin, R. (2022). A Review of Mobile Mapping Systems: From Sensors to Applications. Sensors, 22.","DOI":"10.3390\/s22114262"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A flexible new technique for camera calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TRO.2018.2853729","article-title":"Vins-mono: A robust and versatile monocular visual-inertial state estimator","volume":"34","author":"Qin","year":"2018","journal-title":"IEEE Trans. Robot."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, Z., and Scaramuzza, D. (2018, January 1\u20135). A Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593941"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"8085","DOI":"10.1109\/JSTARS.2022.3206399","article-title":"YOLOv5-Tassel: Detecting Tassels in RGB UAV Imagery With Improved YOLOv5 Based on Transfer Learning","volume":"15","author":"Liu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. 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