{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:33:16Z","timestamp":1765546396807,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,4]],"date-time":"2019-01-04T00:00:00Z","timestamp":1546560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["kz170020173571"],"award-info":[{"award-number":["kz170020173571"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFA0603104","2018YFB0105103"],"award-info":[{"award-number":["2017YFA0603104","2018YFB0105103"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1764261","41801335","41871370"],"award-info":[{"award-number":["U1764261","41801335","41871370"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["22120180095"],"award-info":[{"award-number":["22120180095"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving.<\/jats:p>","DOI":"10.3390\/s19010161","type":"journal-article","created":{"date-parts":[[2019,1,4]],"date-time":"2019-01-04T11:34:26Z","timestamp":1546601666000},"page":"161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7864-3255","authenticated-orcid":false,"given":"Junqiao","family":"Zhao","sequence":"first","affiliation":[{"name":"MOE Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2505-6022","authenticated-orcid":false,"given":"Yewei","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xudong","family":"He","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoming","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Ye","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiantian","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Automotive Studies, Tongji University, 4800 Caoan Road, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,4]]},"reference":[{"key":"ref_1","unstructured":"Antoniou, C., Gikas, V., Papathanasopoulou, V., Danezis, C., Panagopoulos, A.D., Markou, I., Efthymiou, D., Yannis, G., and Perakis, H. (2015, January 11\u201315). Localization And Driving Behavior Classification Using Smartphone Sensors in the Direct Absence Of GNSS. Proceedings of the Transportation Research Board 94th Annual Meeting, Washington, DC, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1080\/17489725.2016.1231351","article-title":"A low-cost wireless sensors positioning solution for indoor parking facilities management","volume":"10","author":"Gikas","year":"2016","journal-title":"J. Locat. Based Serv."},{"key":"ref_3","first-page":"57","article-title":"Indoor localization and tracking: Methods, technologies and research challenges","volume":"13","year":"2014","journal-title":"Facta Univ. Ser. Autom. Control Robot."},{"key":"ref_4","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_5","first-page":"1147","article-title":"ORB-SLAM: A Versatile and Accurate Monocular SLAM System","volume":"31","author":"Montiel","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Grimmett, H., Buerki, M., Paz, L., and Pinies, P. (2015, January 26\u201330). Integrating metric and semantic maps for vision-only automated parking. Proceedings of the IEEE International Conference on Robotics and Automation, Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139484"},{"key":"ref_7","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 European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10605-2_54"},{"key":"ref_8","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, Hong Kong, China."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Younes, G., Asmar, D.C., and Shammas, E.A. (arXiv, 2016). A survey on non-filter-based monocular Visual SLAM systems, arXiv.","DOI":"10.15353\/vsnl.v2i1.109"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Houben, S., Neuhausen, M., Michael, M., Kesten, R., Mickler, F., and Schuller, F. (2015). Park marking-based vehicle self-localization with a fisheye topview system. J. Real-Time Image Process.","DOI":"10.1007\/s11554-015-0529-z"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1729881417720781","DOI":"10.1177\/1729881417720781","article-title":"Online semantic mapping of logistic environments using RGB-D cameras","volume":"14","author":"Himstedt","year":"2017","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, L., Zhang, L., Li, X., Liu, X., Shen, Y., and Xiong, L. (2017, January 10\u201314). Vision-based parking-slot detection: A benchmark and a learning-based approach. Proceedings of the 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China.","DOI":"10.1109\/ICME.2017.8019419"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Huang, Y., Zhao, J., He, X., Zhang, S., and Feng, T. (2018, January 26\u201330). Vision-based Semantic Mapping and Localization for Autonomous Indoor Parking. Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500516"},{"key":"ref_14","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_15","unstructured":"Bansal, A., Badino, H., and Huber, D. (2015). Analysis of the CMU Localization Algorithm under Varied Conditions, Robotics Institute. Technical Report CMU-RI-TR-15-05."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Davison, A.J. (2003, January 13\u201316). Real-Time Simultaneous Localisation and Mapping with a Single Camera. Proceedings of the IEEE International Conference on Computer Vision, Nice, France.","DOI":"10.1109\/ICCV.2003.1238654"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Strasdat, H., Montiel, J., and Davison, A.J. (2010, January 3\u20137). Real-time monocular SLAM: Why filter?. Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), Anchorage, AK, USA.","DOI":"10.1109\/ROBOT.2010.5509636"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., Lovegrove, S.J., and Davison, A.J. (2011, January 6\u201313). DTAM: Dense tracking and mapping in real-time. Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126513"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Salas-Moreno, R.F., Newcombe, R.A., Strasdat, H., Kelly, P.H.J., and Davison, A.J. (2013, January 23\u201328). SLAM++: Simultaneous Localisation and Mapping at the Level of Objects. Proceedings of the 2013 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.178"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, S., Fidler, S., and Urtasun, R. (2015, January 7\u201313). Lost Shopping! Monocular Localization in Large Indoor Spaces. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.309"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, X., and Belaroussi, R. (arXiv, 2016). Semi-Dense 3D Semantic Mapping from Monocular SLAM, arXiv.","DOI":"10.1109\/ITSC.2017.8317942"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.robot.2015.08.009","article-title":"Real-time monocular object SLAM","volume":"75","author":"Salas","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_25","unstructured":"Mccormac, J., Handa, A., Davison, A., Leutenegger, S., Mccormac, J., Handa, A., Davison, A., and Leutenegger, S. (June, January 29). SemanticFusion: Dense 3D semantic mapping with convolutional neural networks. Proceedings of the IEEE International Conference on Robotics and Automation, Singapore."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"S\u00fcnderhauf, N., Pham, T.T., Latif, Y., Milford, M., and Reid, I. (2017, January 24\u201328). Meaningful maps with object-oriented semantic mapping. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206392"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, S., and Scherer, S. (arXiv, 2018). CubeSLAM: Monocular 3D Object Detection and SLAM without Prior Models, arXiv.","DOI":"10.1109\/TRO.2019.2909168"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"S\u00fcnderhauf, N., and Protzel, P. (2012, January 7\u201312). Switchable constraints for robust pose graph SLAM. Proceedings of the 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal.","DOI":"10.1109\/IROS.2012.6385590"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1177\/0278364913479413","article-title":"Inference on networks of mixtures for robust robot mapping","volume":"32","author":"Olson","year":"2013","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","doi-asserted-by":"crossref","unstructured":"Xie, L., Wang, S., Markham, A., and Trigoni, N. (2017, January 24\u201328). GraphTinker: Outlier rejection and inlier injection for pose graph SLAM. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206596"},{"key":"ref_32","unstructured":"Pfingsthorn, M., and Birk, A. (June, January 31). Representing and solving local and global ambiguities as multimodal and hyperedge constraints in a generalized graph SLAM framework. Proceedings of the IEEE International Conference on Robotics and Automation, Hong Kong, China."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bowman, S.L., Atanasov, N., Daniilidis, K., and Pappas, G.J. (June, January 29). Probabilistic data association for semantic slam. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989203"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mu, B., Liu, S.Y., Paull, L., Leonard, J., and How, J.P. (2016, January 9\u201314). SLAM with objects using a nonparametric pose graph. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759677"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Olson, E. (2011, January 9\u201313). AprilTag: A robust and flexible visual fiducial system. Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5979561"},{"key":"ref_36","unstructured":"Hong, S., Roh, B., Kim, K., Cheon, Y., and Park, M. (arXiv, 2016). PVANet: Lightweight Deep Neural Networks for Real-time Object Detection, arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hartley, R., and Zisserman, A. (2003). Multiple View Geometry in Computer Vision, Cambridge University Press.","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_38","unstructured":"K\u00fcmmerle, 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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/1\/161\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:23:35Z","timestamp":1760185415000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/1\/161"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,4]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["s19010161"],"URL":"https:\/\/doi.org\/10.3390\/s19010161","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,1,4]]}}}