{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:44:48Z","timestamp":1767339888448,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,2,2]],"date-time":"2019-02-02T00:00:00Z","timestamp":1549065600000},"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>This paper presents a global monocular indoor positioning system for a robotic vehicle starting from a known pose. The proposed system does not depend on a dense 3D map, require prior environment exploration or installation, or rely on the scene remaining the same, photometrically or geometrically. The approach presents a new way of providing global positioning relying on the sparse knowledge of the building floorplan by utilizing special algorithms to resolve the unknown scale through wall\u2013plane association. This Wall Plane Fusion algorithm presented finds correspondences between walls of the floorplan and planar structures present in the 3D point cloud. In order to extract planes from point clouds that contain scale ambiguity, the Scale Invariant Planar RANSAC (SIPR) algorithm was developed. The best wall\u2013plane correspondence is used as an external constraint to a custom Bundle Adjustment optimization which refines the motion estimation solution and enforces a global scale solution. A necessary condition is that only one wall needs to be in view. The feasibility of using the algorithms is tested with synthetic and real-world data; extensive testing is performed in an indoor simulation environment using the Unreal Engine and Microsoft Airsim. The system performs consistently across all three types of data. The tests presented in this paper show that the standard deviation of the error did not exceed 6 cm.<\/jats:p>","DOI":"10.3390\/s19030634","type":"journal-article","created":{"date-parts":[[2019,2,5]],"date-time":"2019-02-05T11:31:07Z","timestamp":1549366267000},"page":"634","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Global Monocular Indoor Positioning of a Robotic Vehicle with a Floorplan"],"prefix":"10.3390","volume":"19","author":[{"given":"John","family":"Noonan","sequence":"first","affiliation":[{"name":"Department of Computer Science, Technion\u2014Israel Institute of Technology, Haifa 3200003, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hector","family":"Rotstein","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Technion\u2014Israel Institute of Technology, Haifa 3200003, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir","family":"Geva","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Technion\u2014Israel Institute of Technology, Haifa 3200003, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ehud","family":"Rivlin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Technion\u2014Israel Institute of Technology, Haifa 3200003, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dabove, P., Di Pietra, V., Piras, M., Jabbar, A.A., and Kazim, S.A. (2018, January 23\u201326). Indoor positioning using Ultra-wide band (UWB) technologies: Positioning accuracies and sensors\u2019 performances. Proceedings of the Position, Location and Navigation Symposium (PLANS), 2018 IEEE\/ION, Monterey, CA, USA.","DOI":"10.1109\/PLANS.2018.8373379"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Noonan, J., Rotstein, H., Geva, A., and Rivlin, E. (2018, January 24\u201327). Vision-Based Indoor Positioning of a Robotic Vehicle with a Floorplan. Proceedings of the 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nantes, France.","DOI":"10.1109\/IPIN.2018.8533855"},{"key":"ref_3","unstructured":"Geva, A. (2018). Sensory Routines for Indoor Autonomous Quad-Copter. [Ph.D. Thesis, Technion, Israel Institute of Technology]."},{"key":"ref_4","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_5","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_6","doi-asserted-by":"crossref","unstructured":"Liu, H., Chen, M., Zhang, G., Bao, H., and Bao, Y. (2018, January 18\u201322). ICE-BA: Incremental, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAM. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00211"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Von Stumberg, L., Usenko, V., and Cremers, D. (2018, January 21\u201325). Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8462905"},{"key":"ref_9","unstructured":"Quan, M., Piao, S., Tan, M., and Huang, S.S. (2018, January 21\u201325). Map-Based Visual-Inertial Monocular SLAM using Inertial assisted Kalman Filter. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1109\/TRO.2018.2820722","article-title":"Recovering Stable Scale in Monocular SLAM Using Object-Supplemented Bundle Adjustment","volume":"34","author":"Frost","year":"2018","journal-title":"IEEE Trans. Robot."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Parkhiya, P., Khawad, R., Murthy, J.K., Bhowmick, B., and Krishna, K.M. (2018, January 21\u201325). Constructing Category-Specific Models for Monocular Object-SLAM. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8460816"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Murthy, J.K., Sharma, S., and Krishna, K.M. (2017, January 24\u201328). Shape priors for real-time monocular object localization in dynamic environments. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8205990"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Song, S., and Chandraker, M. (2014, January 24\u201327). Robust Scale Estimation in Real-Time Monocular SFM for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.203"},{"key":"ref_15","unstructured":"Zhou, D., Dai, Y., and Li, H. (2016, January 19\u201322). Reliable scale estimation and correction for monocular visual odometry. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dragon, R., and Van Gool, L. (2014, January 24\u201327). Ground Plane Estimation using a Hidden Markov Model. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.442"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, H., Yin, X., Du, M., and Chen, Q. (2018, January 21\u201325). Monocular Visual Odometry Scale Recovery Using Geometrical Constraint. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8462902"},{"key":"ref_18","unstructured":"Wu, J., Ma, L., and Hu, X. (June, January 29). Delving deeper into convolutional neural networks for camera relocalization. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Naseer, T., and Burgard, W. (2017, January 24\u201328). Deep regression for monocular camera-based 6-dof global localization in outdoor environments. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8205957"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Valada, A., Radwan, N., and Burgard, W. (2018, January 21\u201325). Deep Auxiliary Learning for Visual Localization and Odometry. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8462979"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yin, X., Wang, X., Du, X., and Chen, Q. (2017, January 22\u201329). Scale Recovery for Monocular Visual Odometry Using Depth Estimated with Deep Convolutional Neural Fields. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.625"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Feng, G., Ma, L., Tan, X., and Qin, D. (2018). Drift-Aware Monocular Localization Based on a Pre-Constructed Dense 3D Map in Indoor Environments. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7080299"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Caselitz, T., Steder, B., Ruhnke, M., and Burgard, W. (2016, January 9\u201314). Monocular camera localization in 3d lidar maps. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759304"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s10514-006-9006-7","article-title":"Simultaneous localization and odometry self calibration for mobile robot","volume":"22","author":"Martinelli","year":"2007","journal-title":"Auton. Robot."},{"key":"ref_25","unstructured":"(2019, January 15). IDS Imaging Development Systems GmbH. Available online: https:\/\/en.ids-imaging.com\/store\/ui-3271le.html."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gonzales, J., Zhang, F., Li, K., and Borrelli, F. (2016, January 13\u201316). Autonomous drifting with onboard sensors. Proceedings of the Advanced Vehicle Control: Proceedings of the 13th International Symposium on Advanced Vehicle Control (AVEC\u201916), Munich, Germany.","DOI":"10.1201\/9781315265285-22"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Geiger, A., Ziegler, J., and Stiller, C. (2011, January 5\u20139). StereoScan: Dense 3D Reconstruction in Real-time. Proceedings of the Intelligent Vehicles Symposium (IV), Baden-Baden, Germany.","DOI":"10.1109\/IVS.2011.5940405"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/3\/634\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:30:39Z","timestamp":1760185839000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/3\/634"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,2]]},"references-count":27,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["s19030634"],"URL":"https:\/\/doi.org\/10.3390\/s19030634","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,2,2]]}}}