{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T18:04:09Z","timestamp":1771265049983,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,9,2]],"date-time":"2019-09-02T00:00:00Z","timestamp":1567382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91748101"],"award-info":[{"award-number":["91748101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Simultaneous localization and mapping (SLAM) is a fundamental problem for various applications. For indoor environments, planes are predominant features that are less affected by measurement noise. In this paper, we propose a novel point-plane SLAM system using RGB-D cameras. First, we extract feature points from RGB images and planes from depth images. Then plane correspondences in the global map can be found using their contours. Considering the limited size of real planes, we exploit constraints of plane edges. In general, a plane edge is an intersecting line of two perpendicular planes. Therefore, instead of line-based constraints, we calculate and generate supposed perpendicular planes from edge lines, resulting in more plane observations and constraints to reduce estimation errors. To exploit the orthogonal structure in indoor environments, we also add structural (parallel or perpendicular) constraints of planes. Finally, we construct a factor graph using all of these features. The cost functions are minimized to estimate camera poses and global map. We test our proposed system on public RGB-D benchmarks, demonstrating its robust and accurate pose estimation results, compared with other state-of-the-art SLAM systems.<\/jats:p>","DOI":"10.3390\/s19173795","type":"journal-article","created":{"date-parts":[[2019,9,3]],"date-time":"2019-09-03T03:06:14Z","timestamp":1567479974000},"page":"3795","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["Point-Plane SLAM Using Supposed Planes for Indoor Environments"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8674-3790","authenticated-orcid":false,"given":"Xiaoyu","family":"Zhang","sequence":"first","affiliation":[{"name":"Robotics Institute, Beihang University, Beijing 100191, China"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Robotics Institute, Beihang University, Beijing 100191, China"}]},{"given":"Xianyu","family":"Qi","sequence":"additional","affiliation":[{"name":"Robotics Institute, Beihang University, Beijing 100191, China"}]},{"given":"Ziwei","family":"Liao","sequence":"additional","affiliation":[{"name":"Robotics Institute, Beihang University, Beijing 100191, China"}]},{"given":"Ran","family":"Wei","sequence":"additional","affiliation":[{"name":"Beijing Evolver Robotics Technology Co., Ltd., Beijing 100192, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,2]]},"reference":[{"key":"ref_1","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 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487258"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, J., Zhong, R., Hu, Q., and Ai, M. (2016). Feature-Based Laser Scan Matching and Its Application for Indoor Mapping. Sensors, 16.","DOI":"10.3390\/s16081265"},{"key":"ref_3","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 IEEE and ACM International Symposium on Mixed and Augmented Reality, Nara, Japan.","DOI":"10.1109\/ISMAR.2007.4538852"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, R., Wan, W., Wang, Y., and Di, K. (2019). A New RGB-D SLAM Method with Moving Object Detection for Dynamic Indoor Scenes. Remote Sens., 11.","DOI":"10.3390\/rs11101143"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Meng, X., Gao, W., and Hu, Z. (2018). Dense RGB-D SLAM with Multiple Cameras. Sensors, 18.","DOI":"10.3390\/s18072118"},{"key":"ref_6","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":"2011","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"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","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_8","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 (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10605-2_54"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TPAMI.2017.2658577","article-title":"Direct Sparse Odometry","volume":"40","author":"Engel","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","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_11","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 211 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Basel, Switzerland.","DOI":"10.1109\/ISMAR.2011.6092378"},{"key":"ref_12","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 (ICCV), Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126513"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Weingarten, J., and Siegwart, R. (2005, January 2\u20136). EKF-based 3D SLAM for structured environment reconstruction. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Edmonton, AB, Canada.","DOI":"10.1109\/IROS.2005.1545285"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"14651","DOI":"10.3182\/20080706-5-KR-1001.02481","article-title":"SLAM and data fusion from visual landmarks and 3D planes","volume":"41","author":"Zureiki","year":"2008","journal-title":"IFAC Proc. Vol."},{"key":"ref_15","unstructured":"Thrun, S., Burgard, W., and Fox, D. (2006). Probabilistic Robotics, The MIT Press."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gostar, A.K., Fu, C., Chuah, W., Hossain, M.I., Tennakoon, R., Bab-Hadiashar, A., and Hoseinnezhad, R. (2019). State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds. Sensors, 19.","DOI":"10.3390\/s19071614"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Taguchi, Y., Jian, Y.D., Ramalingam, S., and Feng, C. (2013, January 6\u201310). Point-plane SLAM for hand-held 3D sensors. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6631318"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ma, L., Kerl, C., St\u00fcckler, J., and Cremers, D. (2016, January 26\u201321). CPA-SLAM: Consistent plane-model alignment for direct RGB-D SLAM. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487260"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kaess, M. (2015, January 25\u201330). Simultaneous localization and mapping with infinite planes. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139837"},{"key":"ref_20","unstructured":"Ming, H., Westman, E., Zhang, G., and Kaess, M. (June, January 29). Keyframe-based dense planar SLAM. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ferrer, G. (2019, August 16). Eigen-Factors: Plane Estimation for Multi-Frame and Time-Continuous Point Cloud Alignment. Available online: http:\/\/sites.skoltech.ru\/app\/data\/uploads\/sites\/50\/2019\/07\/ferrer2019planes.pdf.","DOI":"10.1109\/IROS40897.2019.8967573"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, S., Song, Y., Kaess, M., and Scherer, S. (2016, January 9\u201314). Pop-up slam: Semantic monocular plane slam for low-texture environments. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759204"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Coughlan, J.M., and Yuille, A.L. (1999, January 20\u201325). Manhattan World: Compass Direction from a Single Image by Bayesian Inference. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790349"},{"key":"ref_24","unstructured":"Yi, Z., Kneip, L., Rodriguez, C., and Li, H. (2016, January 21\u201323). Divide and Conquer: Efficient Density-Based Tracking of 3D Sensors in Manhattan Worlds. Proceedings of the Asian Conference on Computer Vision (ACCV), Taipei, Taiwan."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kim, P., Coltin, B., and Jin Kim, H. (2018, January 8\u201314). Linear RGB-D SLAM for planar environments. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01225-0_21"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, L., and Wu, Z. (2019). RGB-D SLAM with Manhattan Frame Estimation Using Orientation Relevance. Sensors, 19.","DOI":"10.3390\/s19051050"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Guo, R., Peng, K., Fan, W., Zhai, Y., and Liu, Y. (2019). RGB-D SLAM Using Point\u2013Plane Constraints for Indoor Environments. Sensors, 19.","DOI":"10.3390\/s19122721"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G.R. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hartley, R., and Zisserman, A. (2003). Multiple View Geometry in Computer Vision, Cambridge University Press. [2nd ed.].","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_30","unstructured":"Trevor, A.J.B., Gedikli, S., Rusu, R.B., and Christensen, H.I. (2013, January 5). Efficient organized point cloud segmentation with connected components. Proceedings of the 3rd Workshop on Semantic Perception Mapping and Exploration (SPME), Karlsruhe, Germany."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_32","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 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal.","DOI":"10.1109\/IROS.2012.6385773"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2300000043","article-title":"Factor Graphs for Robot Perception","volume":"6","author":"Frank","year":"2017","journal-title":"Found. Trends Robot."},{"key":"ref_34","unstructured":"Grisetti, G., K\u00fcmmerle, R., Strasdat, H., and Konolige, K. (2011, January 9\u201313). g2o: A general Framework for (Hyper) Graph Optimization. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China."},{"key":"ref_35","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_36","unstructured":"Handa, A., Whelan, T., Mcdonald, J., and Davison, A.J. (June, January 31). A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Barfoot, T. (2017). State Estimation for Robotics, Cambridge University Press.","DOI":"10.1017\/9781316671528"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/17\/3795\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:16:05Z","timestamp":1760188565000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/17\/3795"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,2]]},"references-count":37,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["s19173795"],"URL":"https:\/\/doi.org\/10.3390\/s19173795","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,2]]}}}