{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:20:45Z","timestamp":1774966845913,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T00:00:00Z","timestamp":1694217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science of China","award":["62172315"],"award-info":[{"award-number":["62172315"]}]},{"name":"National Natural Science of China","award":["62073262"],"award-info":[{"award-number":["62073262"]}]},{"name":"National Natural Science of China","award":["61672429"],"award-info":[{"award-number":["61672429"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Continuous, robust, and precise localization is pivotal in enabling the autonomous operation of robots and aircraft in intricate environments, particularly in the absence of GNSS (global navigation satellite system) signals. However, commonly employed approaches, such as visual odometry and inertial navigation systems, encounter hindrances in achieving effective navigation and positioning due to issues of error accumulation. Additionally, the challenge of managing extensive map creation and exploration arises when deploying these systems on unmanned aerial vehicle terminals. This study introduces an innovative system capable of conducting long-range and multi-map visual SLAM (simultaneous localization and mapping) using monocular cameras equipped with pinhole and fisheye lens models. We formulate a graph optimization model integrating GNSS data and graphical information through multi-sensor fusion navigation and positioning technology. We propose partitioning SLAM maps based on map health status to augment accuracy and resilience in large-scale map generation. We introduce a multi-map matching and fusion algorithm leveraging geographical positioning and visual data to address excessive discrete mapping, leading to resource wastage and reduced map-switching efficiency. Furthermore, a multi-map-based visual SLAM online localization algorithm is presented, adeptly managing and coordinating distinct geographical maps in different temporal and spatial domains. We employ a quadcopter to establish a testing system and generate an aerial image dataset spanning several kilometers. Our experiments exhibit the framework\u2019s noteworthy robustness and accuracy in long-distance navigation. For instance, our GNSS-assisted multi-map SLAM achieves an average accuracy of 1.5 m within a 20 km range during unmanned aerial vehicle flights.<\/jats:p>","DOI":"10.3390\/rs15184442","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T09:09:21Z","timestamp":1694423361000},"page":"4442","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["LD-SLAM: A Robust and Accurate GNSS-Aided Multi-Map Method for Long-Distance Visual SLAM"],"prefix":"10.3390","volume":"15","author":[{"given":"Dongdong","family":"Li","sequence":"first","affiliation":[{"name":"National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Shaanxi Key Laboratory of Speech and Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangbing","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Shaanxi Key Laboratory of Speech and Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxiao","family":"Feng","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Shaanxi Key Laboratory of Speech and Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhijun","family":"Wang","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Shaanxi Key Laboratory of Speech and Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinghui","family":"Fan","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Shaanxi Key Laboratory of Speech and Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Shaanxi Key Laboratory of Speech and Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9043-8633","authenticated-orcid":false,"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Telecommunications Engineering, Xidian University, Xi\u2019an 710126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5180-2316","authenticated-orcid":false,"given":"Tao","family":"Yang","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Shaanxi Key Laboratory of Speech and Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Buehler, M., Iagnemma, K., and Singh, S. (2009). The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, Springer.","DOI":"10.1007\/978-3-642-03991-1"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wan, G., Yang, X., Cai, R., Li, H., Zhou, Y., Wang, H., and Song, S. (2018, January 21\u201325). Robust and precise vehicle localization based on multi-sensor fusion in diverse city scenes. Proceedings of the 2018 IEEE International Conference on robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8461224"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Meng, X., Wang, H., and Liu, B. (2017). A robust vehicle localization approach based on gnss\/imu\/dmi\/lidar sensor fusion for autonomous vehicles. Sensors, 17.","DOI":"10.3390\/s17092140"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TRO.2015.2463671","article-title":"ORB-SLAM: A versatile and accurate monocular SLAM system","volume":"31","author":"Montiel","year":"2015","journal-title":"IEEE Trans. Robot."},{"key":"ref_6","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_7","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_8","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_9","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.1109\/TRO.2021.3075644","article-title":"Orb-slam3: An accurate open-source library for visual, visual-inertial, and multimap slam","volume":"37","author":"Campos","year":"2021","journal-title":"IEEE Trans. Robot."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Qin, T., Li, P., and Shen, S. (2018, January 21\u201325). Relocalization, global optimization and map merging for monocular visual-inertial SLAM. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8460780"},{"key":"ref_11","unstructured":"Qin, T., Cao, S., Pan, J., and Shen, S. (2019). A general optimization-based framework for global pose estimation with multiple sensors. arXiv Prepr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.1109\/TRO.2021.3133730","article-title":"GVINS: Tightly coupled GNSS-visual-inertial fusion for smooth and consistent state estimation","volume":"38","author":"Cao","year":"2022","journal-title":"IEEE Trans. Robot."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1109\/LRA.2022.3224367","article-title":"IC-GVINS: A Robust, Real-Time, INS-Centric GNSS-Visual-Inertial Navigation System","volume":"8","author":"Niu","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11845","DOI":"10.1109\/TITS.2021.3107873","article-title":"G-VIDO: A vehicle dynamics and intermittent GNSS-aided visual-inertial state estimator for autonomous driving","volume":"23","author":"Xiong","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Aldibaja, M., Suganuma, N., Yoneda, K., and Yanase, R. (2022). Challenging Environments for Precise Mapping Using GNSS\/INS-RTK Systems: Reasons and Analysis. Remote Sens., 14.","DOI":"10.3390\/rs14164058"},{"key":"ref_16","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. Mach. Intell."},{"key":"ref_17","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_18","first-page":"7","article-title":"Scale drift-aware large scale monocular SLAM","volume":"2","author":"Strasdat","year":"2010","journal-title":"Robot. Sci. Syst. VI"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Strasdat, H., Davison, A.J., Montiel, J.M., and Konolige, K. (2011, January 6\u201313). Double window optimisation for constant time visual SLAM. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126517"},{"key":"ref_20","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_21","unstructured":"Fu, Q., Yu, H., Wang, X., Yang, Z., Zhang, H., and Mian, A. (2020). FastORB-SLAM: A fast ORB-SLAM method with Coarse-to-Fine descriptor independent keypoint matching. arXiv Prepr."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sumikura, S., Shibuya, M., and Sakurada, K. (2019, January 21\u201325). OpenVSLAM: A versatile visual SLAM framework. Proceedings of the 27th ACM International Conference on Multimedia.","DOI":"10.1145\/3343031.3350539"},{"key":"ref_23","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 Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland. Proceedings, Part II 13.","DOI":"10.1007\/978-3-319-10605-2_54"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Forster, C., Pizzoli, M., and Scaramuzza, D. (June, January 31). SVO: Fast semi-direct monocular visual odometry. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6906584"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1109\/TRO.2016.2623335","article-title":"SVO: Semidirect visual odometry for monocular and multicamera systems","volume":"33","author":"Forster","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_26","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 IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.421"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1109\/LRA.2018.2889156","article-title":"Loosely-coupled semi-direct monocular slam","volume":"4","author":"Lee","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_29","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 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8462905"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1109\/TRO.2021.3050417","article-title":"2020 Index IEEE Transactions on Robotics Vol. 36","volume":"36","author":"Adorno","year":"2020","journal-title":"IEEE Trans. Robot."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shahoud, A., Shashev, D., and Shidlovskiy, S. (2022). Visual navigation and path tracking using street geometry information for image alignment and serving. Drones, 6.","DOI":"10.3390\/drones6050107"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mourikis, A.I., and Roumeliotis, S.I. (2007, January 10\u201314). A multi-state constraint Kalman filter for vision-aided inertial navigation. Proceedings of the 2007 IEEE International Conference on Robotics and Automation, Rome, Italy.","DOI":"10.1109\/ROBOT.2007.364024"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Leutenegger, S., Furgale, P., Rabaud, V., Chli, M., Konolige, K., and Siegwart, R. (2013, January 24\u201328). Keyframe-based visual-inertial slam using nonlinear optimization. Proceedings of the Robotis Science and Systems (RSS), Berlin, Germany.","DOI":"10.15607\/RSS.2013.IX.037"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1177\/0278364914554813","article-title":"Keyframe-based visual\u2013inertial odometry using nonlinear optimization","volume":"34","author":"Leutenegger","year":"2015","journal-title":"Int. J. Robot. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1109\/LRA.2017.2653359","article-title":"Visual-inertial monocular SLAM with map reuse","volume":"2","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TRO.2011.2170332","article-title":"Visual-inertial-aided navigation for high-dynamic motion in built environments without initial conditions","volume":"28","author":"Lupton","year":"2011","journal-title":"IEEE Trans. Robot."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TRO.2016.2597321","article-title":"On-manifold preintegration for real-time visual-inertial odometry","volume":"33","author":"Forster","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1109\/LRA.2019.2961227","article-title":"Visual-inertial mapping with non-linear factor recovery","volume":"5","author":"Usenko","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rosinol, A., Abate, M., Chang, Y., and Carlone, L. (August, January 31). Kimera: An open-source library for real-time metric-semantic localization and mapping. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196885"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Huang, F. (2021). Panoramic visual SLAM technology for spherical images. Sensors, 21.","DOI":"10.3390\/s21030705"},{"key":"ref_41","unstructured":"Fu, Q., Wang, J., Yu, H., Ali, I., Guo, F., He, Y., and Zhang, H. (2020). Pl-vins: Real-time monocular visual-inertial slam with point and line features. arXiv Prepr."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gu, N., Xing, F., and You, Z. (2022). Visual\/Inertial\/GNSS Integrated Navigation System under GNSS Spoofing Attack. Remote Sens., 14.","DOI":"10.3390\/rs14235975"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/LRA.2018.2793357","article-title":"Ultimate SLAM? Combining events, images, and IMU for robust visual SLAM in HDR and high-speed scenarios","volume":"3","author":"Vidal","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_44","unstructured":"Groves, P.D. (2008). Principes of GNSS, Inertial and Multisensor Integrated, Artech."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"26074","DOI":"10.1109\/JSEN.2021.3119982","article-title":"Fast and accurate initialization for monocular vision\/INS\/GNSS integrated system on land vehicle","volume":"21","author":"Jin","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5527","DOI":"10.1109\/JSEN.2020.2970277","article-title":"Velocity-based optimization-based alignment (VBOBA) of low-end MEMS IMU\/GNSS for low dynamic applications","volume":"20","author":"Zhang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Eade, E., and Drummond, T. (2008, January 18). Unified loop closing and recovery for real time monocular SLAM. Proceedings of the British Machine Vision Conference, Leeds, UK.","DOI":"10.5244\/C.22.6"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Castle, R., Klein, G., and Murray, D.W. (October, January 28). Video-rate localization in multiple maps for wearable augmented reality. Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers, Pittsburgh, PA, USA.","DOI":"10.1109\/ISWC.2008.4911577"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Forster, C., Lynen, S., Kneip, L., and Scaramuzza, D. (2013, January 3\u20137). Collaborative monocular slam with multiple micro aerial vehicles. Proceedings of the 2013 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan.","DOI":"10.1109\/IROS.2013.6696923"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.robot.2013.11.007","article-title":"C2tam: A cloud framework for cooperative tracking and mapping","volume":"62","author":"Riazuelo","year":"2014","journal-title":"Robot. Auton. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Morrison, J.G., G\u00e1lvez-L\u00f3pez, D., and Sibley, G. (2016, January 6\u20139). MOARSLAM: Multiple operator augmented RSLAM. Proceedings of the Distributed Autonomous Robotic Systems: The 12th International Symposium, London, UK.","DOI":"10.1007\/978-4-431-55879-8_9"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Schmuck, P., and Chli, M. (June, January 29). Multi-uav collaborative monocular slam. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989445"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1002\/rob.21854","article-title":"CCM-SLAM: Robust and efficient centralized collaborative monocular simultaneous localization and mapping for robotic teams","volume":"36","author":"Schmuck","year":"2019","journal-title":"J. Field Robot."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Daoud, H.A., Md. Sabri, A.Q., Loo, C.K., and Mansoor, A.M. (2018). SLAMM: Visual monocular SLAM with continuous mapping using multiple maps. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0195878"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"23772","DOI":"10.1109\/ACCESS.2021.3050617","article-title":"RDS-SLAM: Real-time dynamic SLAM using semantic segmentation methods","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ming, D., and Wu, X. (2022, January 23\u201325). Research on Monocular Vision SLAM Algorithm for Multi-map Fusion and Loop Detection. Proceedings of the 2022 6th International Conference on Automation, Control and Robots (ICACR), Shanghai, China.","DOI":"10.1109\/ICACR55854.2022.9935516"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Liu, B., Zhang, Z., Hao, D., Liu, G., Lu, H., Meng, Y., and Lu, X. (2022, January 27\u201331). Collaborative Visual Inertial SLAM with KNN Map Matching. Proceedings of the 2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Baishan, China.","DOI":"10.1109\/CYBER55403.2022.9907296"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.compeleceng.2016.06.006","article-title":"Feature matching based positioning algorithm for swarm robotics","volume":"67","author":"Karpuz","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_59","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4442\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:48:00Z","timestamp":1760129280000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4442"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,9]]},"references-count":59,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15184442"],"URL":"https:\/\/doi.org\/10.3390\/rs15184442","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,9]]}}}