{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T08:17:02Z","timestamp":1769242622960,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2015,7,3]],"date-time":"2015-07-03T00:00:00Z","timestamp":1435881600000},"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>Localization is an essential issue for robot navigation, allowing the robot to perform tasks autonomously. However, in environments with laser scan ambiguity, such as long corridors, the conventional SLAM (simultaneous localization and mapping) algorithms exploiting a laser scanner may not estimate the robot pose robustly. To resolve this problem, we propose a novel localization approach based on a hybrid method incorporating a 2D laser scanner and a monocular camera in the framework of a graph structure-based SLAM. 3D coordinates of image feature points are acquired through the hybrid method, with the assumption that the wall is normal to the ground and vertically flat. However, this assumption can be relieved, because the subsequent feature matching process rejects the outliers on an inclined or non-flat wall. Through graph optimization with constraints generated by the hybrid method, the final robot pose is estimated. To verify the effectiveness of the proposed method, real experiments were conducted in an indoor environment with a long corridor. The experimental results were compared with those of the conventional GMappingapproach. The results demonstrate that it is possible to localize the robot in environments with laser scan ambiguity in real time, and the performance of the proposed method is superior to that of the conventional approach.<\/jats:p>","DOI":"10.3390\/s150715830","type":"journal-article","created":{"date-parts":[[2015,7,3]],"date-time":"2015-07-03T16:19:37Z","timestamp":1435940377000},"page":"15830-15852","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Graph Structure-Based Simultaneous Localization and Mapping Using a Hybrid Method of 2D Laser Scan and Monocular Camera Image in Environments with Laser Scan Ambiguity"],"prefix":"10.3390","volume":"15","author":[{"given":"Taekjun","family":"Oh","sequence":"first","affiliation":[{"name":"Urban Robotics Laboratory (URL), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro (373-1 Guseong-dong), Yuseong-gu, Daejeon 305-701, Korea"}]},{"given":"Donghwa","family":"Lee","sequence":"additional","affiliation":[{"name":"Urban Robotics Laboratory (URL), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro (373-1 Guseong-dong), Yuseong-gu, Daejeon 305-701, Korea"}]},{"given":"Hyungjin","family":"Kim","sequence":"additional","affiliation":[{"name":"Urban Robotics Laboratory (URL), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro (373-1 Guseong-dong), Yuseong-gu, Daejeon 305-701, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5799-2026","authenticated-orcid":false,"given":"Hyun","family":"Myung","sequence":"additional","affiliation":[{"name":"Urban Robotics Laboratory (URL), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro (373-1 Guseong-dong), Yuseong-gu, Daejeon 305-701, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2015,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1177\/0278364906072768","article-title":"Square root SAM: Simultaneous localization and mapping via square root information smoothing","volume":"25","author":"Dellaert","year":"2006","journal-title":"Int. J. Robot. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1109\/TRO.2008.2006706","article-title":"iSAM: Incremental smoothing and mapping","volume":"24","author":"Kaess","year":"2008","journal-title":"IEEE Trans. Robot."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1177\/0278364911430419","article-title":"iSAM2: Incremental smoothing and mapping using the bayes tree","volume":"31","author":"Kaess","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Grisetti, G., Stachniss, C., and Burgard, W. (2005, January 18\u201322). Improving grid-based SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling. Barcelona, Spain.","DOI":"10.15607\/RSS.2005.I.009"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gouveia, B.D., Portugal, D., and Marques, L. (2014, January 14\u201318). Speeding up Rao-Blackwellized particle filter SLAM with a multithreaded architecture. Chicago, IL, USA.","DOI":"10.1109\/IROS.2014.6942766"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11805","DOI":"10.3390\/s140711805","article-title":"NAVIS\u2014An UGV indoor positioning system using laser scan matching for large-area real-time applications","volume":"14","author":"Tang","year":"2014","journal-title":"Sensors"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1109\/TPAMI.2007.1049","article-title":"MonoSLAM: Real-time single camera SLAM","volume":"29","author":"Davison","year":"2007","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Strasdat, H., Montiel, J., and Davison, A.J. (2010, January 27\u201330). Scale drift-aware large scale monocular SLAM. Zaragoza, Spain.","DOI":"10.15607\/RSS.2010.VI.010"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ramos, F.T., Fox, D., and Durrant-Whyte, H.F. (2007, January 27\u201330). CRF-matching: Conditional random fields for feature-based scan matching. Atlanta, GA, USA.","DOI":"10.15607\/RSS.2007.III.026"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1002\/rob.20321","article-title":"Three-dimensional mapping with time-of-flight cameras","volume":"26","author":"May","year":"2009","journal-title":"J. Field Robot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3390\/s120100429","article-title":"Sensor fusion of monocular cameras and laser rangefinders for line-based simultaneous localization and mapping (SLAM) tasks in autonomous mobile robots","volume":"12","author":"Zhang","year":"2012","journal-title":"Sensors"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Lee, D., Kim, H., and Myung, H. (2012, January 26\u201329). GPU-based real-time RGB-D 3D SLAM. Daejeon, Korea.","DOI":"10.1007\/978-3-642-37374-9_47"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lee, D., Kim, H., and Myung, H. (2012, January 6\u201318). 2D image feature-based real-time RGB-D 3D SLAM. Gwangju, Korea.","DOI":"10.1007\/978-3-642-37374-9_47"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"12467","DOI":"10.3390\/s140712467","article-title":"Solution to the SLAM problem in low dynamic environments using a pose graph and an RGB-D sensor","volume":"14","author":"Lee","year":"2014","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.3390\/s110202035","article-title":"Ultra wide-band localization and SLAM: A comparative study for mobile robot navigation","volume":"11","author":"Segura","year":"2011","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10197","DOI":"10.3390\/s111110197","article-title":"A novel combined SLAM based on RBPF-SLAM and EIF-SLAM for mobile system sensing in a large scale environment","volume":"11","author":"He","year":"2011","journal-title":"Sensors"},{"key":"ref_18","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":"12","author":"Shi","year":"2012","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8501","DOI":"10.3390\/s130708501","article-title":"A robust approach for a filter-based monocular simultaneous localization and mapping (SLAM) system","volume":"13","author":"Grau","year":"2013","journal-title":"Sensors"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6317","DOI":"10.3390\/s140406317","article-title":"Monocular SLAM for autonomous robots with enhanced features initialization","volume":"14","author":"Guerra","year":"2014","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1109\/TIE.2014.2341595","article-title":"DV-SLAM (dual-sensor-based vector-field SLAM) and observability analysis","volume":"62","author":"Lee","year":"2014","journal-title":"IEEE Trans. Ind. Electron"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.robot.2015.03.003","article-title":"Magnetic field constraints and sequence-based matching for indoor pose graph SLAM","volume":"70","author":"Jung","year":"2015","journal-title":"Robot Auton. Syst."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Rosten, E., and Drummond, T. (2006, January 7\u201313). Machine learning for high-speed corner detection. Graz, Austria.","DOI":"10.1007\/11744023_34"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Calonder, M., Lepetit, V., and Fua, P. (2008, January 12\u201318). Keypoint signatures for fast learning and recognition. Marseille, France.","DOI":"10.1007\/978-3-540-88682-2_6"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Grisetti, G., Stachniss, C., Grzonka, S., and Burgard, W. (2007, January 27\u201330). A tree parameterization for efficiently computing maximum likelihood maps using gradient descent. Atlanta, GA, USA.","DOI":"10.15607\/RSS.2007.III.009"},{"key":"ref_28","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_29","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_30","unstructured":"Pioneer P3-DX. Available online: http:\/\/www.mobilerobots.com\/researchrobots\/pioneerp3dx.aspx\/."},{"key":"ref_31","unstructured":"Hokuyo URG-04LX. Available online: https:\/\/www.hokuyo-aut.jp\/02sensor\/07scanner\/urg_04lx.html\/."},{"key":"ref_32","unstructured":"Point Grey Flea3 FL3-U3-13E4C-C. Available online: http:\/\/www.ptgrey.com\/flea3-13-mp-color-usb3-vision-e2v-ev76c560-camera\/."},{"key":"ref_33","unstructured":"MSI GE60-2OE. Available online: http:\/\/www.msi.com\/product\/nb\/GE60-2OE.html#hero-overview\/."},{"key":"ref_34","unstructured":"Bradski, G., and Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library, O'Reilly Media, Inc."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., and Ng, A.Y. (2009, January 12\u201317). ROS: An open-source robot operating system. Kobe, Japan.","DOI":"10.1109\/MRA.2010.936956"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/34.159901","article-title":"Camera calibration with distortion models and accuracy evaluation","volume":"14","author":"Weng","year":"1992","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_37","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."},{"key":"ref_38","unstructured":"Camera calibration toolbox. Available online: http:\/\/www.vision.caltech.edu\/bouguetj\/calib_doc\/."},{"key":"ref_39","unstructured":"Zhang, Q., and Pless, R. (October, January 28). Extrinsic calibration of a camera and laser range finder. Sendai, Japan."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lee, D., Jeon, H., and Myung, H. (2012, January 11\u201316). Vision-based 6-DOF displacement measurement of structures with a planar marker. San Diego, CA, USA.","DOI":"10.1117\/12.915068"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/7\/15830\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:48:41Z","timestamp":1760215721000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/7\/15830"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,7,3]]},"references-count":40,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2015,7]]}},"alternative-id":["s150715830"],"URL":"https:\/\/doi.org\/10.3390\/s150715830","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,7,3]]}}}