{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:17:03Z","timestamp":1780053423263,"version":"3.54.0"},"reference-count":36,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,10]],"date-time":"2021-07-10T00:00:00Z","timestamp":1625875200000},"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>LiDAR (light detection and ranging), as an active sensor, is investigated in the simultaneous localization and mapping (SLAM) system. Typically, a LiDAR SLAM system consists of front-end odometry and back-end optimization modules. Loop closure detection and pose graph optimization are the key factors determining the performance of the LiDAR SLAM system. However, the LiDAR works at a single wavelength (905 nm), and few textures or visual features are extracted, which restricts the performance of point clouds matching based loop closure detection and graph optimization. With the aim of improving LiDAR SLAM performance, in this paper, we proposed a LiDAR and visual SLAM backend, which utilizes LiDAR geometry features and visual features to accomplish loop closure detection. Firstly, the bag of word (BoW) model, describing the visual similarities, was constructed to assist in the loop closure detection and, secondly, point clouds re-matching was conducted to verify the loop closure detection and accomplish graph optimization. Experiments with different datasets were carried out for assessing the proposed method, and the results demonstrated that the inclusion of the visual features effectively helped with the loop closure detection and improved LiDAR SLAM performance. In addition, the source code, which is open source, is available for download once you contact the corresponding author.<\/jats:p>","DOI":"10.3390\/rs13142720","type":"journal-article","created":{"date-parts":[[2021,7,11]],"date-time":"2021-07-11T22:16:48Z","timestamp":1626041808000},"page":"2720","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["A LiDAR\/Visual SLAM Backend with Loop Closure Detection and Graph Optimization"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9071-0051","authenticated-orcid":false,"given":"Shoubin","family":"Chen","sequence":"first","affiliation":[{"name":"Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"},{"name":"Orbbec Research, Shenzhen 518052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1607-2626","authenticated-orcid":false,"given":"Baoding","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Urban Smart Transportation & Safety Maintenance, Shenzhen University, Shenzhen 518060, China"},{"name":"Key Laboratory for Resilient Infrastructures of Coastal Cities (Shenzhen University), Ministry of Education, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changhui","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, Finnish Geospatial Research Institute (FGI), FI-02430 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weixing","family":"Xue","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingquan","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/MRA.2006.1678144","article-title":"Simultaneous localization and mapping (SLAM): Part II","volume":"13","author":"Bailey","year":"2006","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","article-title":"Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age","volume":"32","author":"Cadena","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.isprsjprs.2021.05.006","article-title":"Crowdsourcing-based indoor mapping using smartphones: A survey","volume":"177","author":"Zhou","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gao, X., Wang, R., Demmel, N., and Cremers, D. (2018, January 1\u20135). LDSO: Direct sparse odometry with loop closure. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593376"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Klein, G., and Murray, D. (2007, January 13\u201316). Parallel tracking and mapping for small AR workspaces. Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, Nara, Japan.","DOI":"10.1109\/ISMAR.2007.4538852"},{"key":"ref_7","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","author":"Tardos","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hess, W., Kohler, D., Rapp, H., and Andor, D. (2016, January 27\u201329). Real-time loop closure in 2D LIDAR SLAM. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRAE), Jeju-Do, Korea.","DOI":"10.1109\/ICRA.2016.7487258"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/TRO.2006.889486","article-title":"Improved techniques for grid mapping with rao-blackwellized particle filters","volume":"23","author":"Grisetti","year":"2007","journal-title":"IEEE Trans. Robot."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sturm, J., Engelhard, N., Endres, F., Burgard, W., and Cremers, D. (2012, January 7\u201312). A benchmark for the evaluation of RGB-D SLAM systems. Proceedings of the 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, Portugal.","DOI":"10.1109\/IROS.2012.6385773"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Shen, S., Michael, N., and Kumar, V. (2011, January 9\u201313). Autonomous multi-floor indoor navigation with a computationally constrained MAV. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980357"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shen, S., Michael, N., and Kumar, V. (2015, January 26\u201330). Tightly coupled monocular visual-inertial fusion for autonomous flight of rotorcraft MAVs. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139939"},{"key":"ref_15","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 13th European Conference of Computer Vision, Z\u00fcrich, Switzerland.","DOI":"10.1007\/978-3-319-10605-2_54"},{"key":"ref_16","unstructured":"Forster, C., Pizzoli, M., and Scaramuzza, D. (June, January 31). SVO: Fast semi-direct monocular visual odometry. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, R., Schworer, M., and Cremers, D. (2017, January 22\u201329). Stereo DSO: Large-scale direct sparse visual odometry with stereo cameras. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.421"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1145\/504729.504754","article-title":"Probabilistic robotics","volume":"45","author":"Thrun","year":"2002","journal-title":"Commun. ACM"},{"key":"ref_19","unstructured":"Harmon, R.S., Holloway, J.H., and Broach, J.T. (2010). Comparison of indoor robot localization techniques in the absence of GPS. Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, International Society for Optics and Photonics."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1177\/0278364906065387","article-title":"The graph SLAM algorithm with applications to large-scale mapping of urban structures","volume":"25","author":"Thrun","year":"2006","journal-title":"Int. J. Robot. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Olson, E.B. (2009, January 12\u201317). Real-time correlative scan matching. Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152375"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s10514-015-9525-1","article-title":"A real-time method for depth enhanced visual odometry","volume":"41","author":"Zhang","year":"2017","journal-title":"Auton. Robot."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Shin, Y.S., Park, Y.S., and Kim, A. (2018, January 21\u201325). Direct visual slam using sparse depth for camera-LiDAR system. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8461102"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shao, W., Vijayarangan, S., Li, C., and Kantor, G. (2019, January 3\u20138). Stereo visual inertial LiDAR simultaneous localization and mapping. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8968012"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1002\/rob.21809","article-title":"Laser-visual-inertial odometry and mapping with high robustness and low drift","volume":"35","author":"Zhang","year":"2018","journal-title":"J. Field Robot."},{"key":"ref_27","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 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139486"},{"key":"ref_28","unstructured":"Hahnel, D., Burgard, W., Fox, D., and Thrun, S. (2003, January 27\u201331). An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements. Proceedings of the 2003 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1177\/0278364909100586","article-title":"Factoring the mapping problem: Mobile robot map-building in the hybrid spatial semantic hierarchy","volume":"29","author":"Beeson","year":"2009","journal-title":"Int. J. Robot. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1177\/0278364913498910","article-title":"Robust loop closing over time for pose graph SLAM","volume":"32","author":"Latif","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_31","unstructured":"Ulrich, I., and Nourbakhsh, I. (2000, January 24\u201328). Appearance-based place recognition for topological localization. Proceedings of the 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation, San Francisco, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Galvez-Lopez, D., and Tardos, J.D. (2011, January 25\u201330). Real-time loop detection with bags of binary words. Proceedings of the 2011 IEEE\/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA.","DOI":"10.1109\/IROS.2011.6094885"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Sivic, J., and Zisserman, A. (2003, January 13\u201316). Video Google: A text retrieval approach to object matching in videos. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Nice, France.","DOI":"10.1109\/ICCV.2003.1238663"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Robertson, S. (2004). Understanding inverse document frequency: On theoretical arguments for IDF. J. Doc., 503\u2013520.","DOI":"10.1108\/00220410410560582"},{"key":"ref_36","unstructured":"Nister, D., and Stewenius, H. (2006, January 17\u201322). Scalable recognition with a vocabulary tree. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), New York, NY, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2720\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:28:51Z","timestamp":1760164131000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2720"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,10]]},"references-count":36,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13142720"],"URL":"https:\/\/doi.org\/10.3390\/rs13142720","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,10]]}}}