{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T08:29:32Z","timestamp":1769761772589,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:00:00Z","timestamp":1670544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"National Science Council (NSTC)","doi-asserted-by":"publisher","award":["MOST 111-2221-E-006-110-"],"award-info":[{"award-number":["MOST 111-2221-E-006-110-"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"National Science Council (NSTC)","doi-asserted-by":"publisher","award":["MOST 111-2622-E-006-012-"],"award-info":[{"award-number":["MOST 111-2622-E-006-012-"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the rapid development of technology, unmanned aerial vehicles (UAVs) have become more popular and are applied in many areas. However, there are some environments where the Global Positioning System (GPS) is unavailable or has the problem of GPS signal outages, such as indoor and bridge inspections. Visual inertial odometry (VIO) is a popular research solution for non-GPS navigation. However, VIO has problems of scale errors and long-term drift. This study proposes a method to correct the position errors of VIO without the help of GPS information for vertical takeoff and landing (VTOL) UAVs. In the initial process, artificial landmarks are utilized to improve the positioning results of VIO by the known landmark information. The position of the UAV is estimated by VIO. Then, the accurate position is estimated by the extended Kalman filter (EKF) with the known landmark, which is used to obtain the scale correction using the least squares method. The Inertial Measurement Unit (IMU) data are used for integration in the time-update process. The EKF can be updated with two measurements. One is the visual odometry (VO) estimated directly by a landmark. The other is the VIO with scale correction. When the landmark is detected during takeoff phase, or the UAV is returning to the takeoff location during landing phase, the trajectory estimated by the landmark is used to update the scale correction. At the beginning of the experiments, preliminary verification was conducted on the ground. A self-developed UAV equipped with a visual\u2013inertial sensor to collect data and a high-precision real time kinematic (RTK) to verify trajectory are applied to flight tests. The experimental results show that the method proposed in this research effectively solves the problems of scale and the long-term drift of VIO.<\/jats:p>","DOI":"10.3390\/s22249654","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T05:10:19Z","timestamp":1670821819000},"page":"9654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Landmark-Based Scale Estimation and Correction of Visual Inertial Odometry for VTOL UAVs in a GPS-Denied Environment"],"prefix":"10.3390","volume":"22","author":[{"given":"Jyun-Cheng","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Aeronautics and Astronautics, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chih-Chun","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Aeronautics and Astronautics, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang-Te","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Aeronautics and Astronautics, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3471-3290","authenticated-orcid":false,"given":"Ying-Chih","family":"Lai","sequence":"additional","affiliation":[{"name":"Department of Aeronautics and Astronautics, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan"},{"name":"Institute of Civil Aviation, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104069","DOI":"10.1016\/j.robot.2022.104069","article-title":"A review of gnss-independent uav navigation techniques","volume":"152","author":"Gyagenda","year":"2022","journal-title":"Robot. Auton. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1002\/rob.21454","article-title":"Gps-denied indoor and outdoor monocular vision aided navigation and control of unmanned aircraft","volume":"30","author":"Chowdhary","year":"2013","journal-title":"J. Field Robot."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jeong, N., Hwang, H., and Matson, E.T. (2018, January 12\u201314). Evaluation of Low-Cost Lidar Sensor for Application in Indoor Uav Navigation. Proceedings of the 2018 IEEE Sensors Applications Symposium (SAS), Seoul, Republic of Korea.","DOI":"10.1109\/SAS.2018.8336719"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3607","DOI":"10.1109\/COMST.2018.2855063","article-title":"A survey of enabling technologies for network localization, tracking, and navigation","volume":"20","author":"Laoudias","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1080\/10095020.2017.1420509","article-title":"A survey on vision-based uav navigation","volume":"21","author":"Lu","year":"2018","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_6","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_7","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_8","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_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\u2013inertial, and multimap slam","volume":"37","author":"Campos","year":"2021","journal-title":"IEEE Trans. Robot."},{"key":"ref_10","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_11","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_12","doi-asserted-by":"crossref","unstructured":"Bustos, A.P., Chin, T.-J., Eriksson, A., and Reid, I. (2019, January 20\u201324). Visual Slam: Why Bundle Adjust?. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793749"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1177\/0278364917728574","article-title":"Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback","volume":"36","author":"Bloesch","year":"2017","journal-title":"Int. J. Robot. Res."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Delmerico, J., and Scaramuzza, D. (2018, January 21\u201325). A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QL, Australia.","DOI":"10.1109\/ICRA.2018.8460664"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Kumar, G.A., Patil, A.K., Patil, R., Park, S.S., and Chai, Y.H. (2017). A lidar and imu integrated indoor navigation system for uavs and its application in real-time pipeline classification. Sensors, 17.","DOI":"10.3390\/s17061268"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3317","DOI":"10.1109\/LRA.2021.3064227","article-title":"Fast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated Kalman filter","volume":"6","author":"Xu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_18","unstructured":"Scaramuzza, D., and Zhang, Z. (2019). Visual-inertial odometry of aerial robots. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Huang, G. (2019, January 20\u201324). Visual-Inertial Navigation: A Concise Review. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793604"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/LRA.2018.2793349","article-title":"Robust stereo visual inertial odometry for fast autonomous flight","volume":"3","author":"Sun","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bloesch, M., Omari, S., Hutter, M., and Siegwart, R. (October, January 28). Robust Visual Inertial Odometry Using a Direct Ekf-Based Approach. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7353389"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cort\u00e9s, S., Solin, A., Rahtu, E., and Kannala, J. (2018, January 8\u201314). Advio: An Authentic Dataset for Visual-Inertial Odometry. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_26"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Schubert, D., Goll, T., Demmel, N., Usenko, V., St\u00fcckler, J., and Cremers, D. (2018, January 1\u20135). The Tum Vi Benchmark for Evaluating Visual-Inertial Odometry. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593419"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhao, R., Liu, E., Yan, K., and Ma, Y. (2018). Scale estimation and correction of the monocular simultaneous localization and mapping (slam) based on fusion of 1d laser range finder and vision data. Sensors, 18.","DOI":"10.3390\/s18061948"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lv, Q., Ma, J., Wang, G., and Lin, H. (2016, January 27\u201329). Absolute Scale Estimation of Orb-Slam Algorithm Based on Laser Ranging. Proceedings of the 2016 35th Chinese Control Conference (CCC), Chengdu, China.","DOI":"10.1109\/ChiCC.2016.7554983"},{"key":"ref_27","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 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Republic of Korea.","DOI":"10.1109\/IROS.2016.7759304"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s10846-018-0775-y","article-title":"Monocular slam system for mavs aided with altitude and range measurements: A gps-free approach","volume":"94","author":"Urzua","year":"2019","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Engel, J., St\u00fcckler, J., and Cremers, D. (2015\u20132, January 28). Large-Scale Direct Slam with Stereo Cameras. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7353631"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, P., Geppert, M., Heng, L., Sattler, T., Geiger, A., and Pollefeys, M. (2018, January 1\u20135). Towards Robust Visual Odometry with a Multi-Camera System. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593561"},{"key":"ref_31","unstructured":"Okuyama, K., Kawasaki, T., and Kroumov, V. (2011, January 11\u201313). Localization and Position Correction for Mobile Robot Using Artificial Visual Landmarks. Proceedings of the 2011 International Conference on Advanced Mechatronic Systems, Zhengzhou, China."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lebedev, I., Erashov, A., and Shabanova, A. (2020, January 7\u20139). Accurate Autonomous Uav Landing Using Vision-Based Detection of Aruco-Marker. Proceedings of the International Conference on Interactive Collaborative Robotics, St. Petersburg, Russia.","DOI":"10.1007\/978-3-030-60337-3_18"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"27136","DOI":"10.1109\/JSEN.2021.3120663","article-title":"A multi-sensor fusion self-localization system of a miniature underwater robot in structured and gps-denied environments","volume":"21","author":"Xing","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_34","unstructured":"Qin, T. (2022, July 15). Available online: https:\/\/github.Com\/ethz-asl\/kalibr."},{"key":"ref_35","unstructured":"(2022, July 15). Gaowenliang. Available online: https:\/\/github.com\/gaowenliang\/imu_utils."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9654\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:37:19Z","timestamp":1760146639000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9654"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,9]]},"references-count":35,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22249654"],"URL":"https:\/\/doi.org\/10.3390\/s22249654","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,9]]}}}