{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:44:09Z","timestamp":1760143449631,"version":"build-2065373602"},"reference-count":18,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T00:00:00Z","timestamp":1707177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Anhui Province University Natural Science Research Project","award":["2023AH051880","GY2022-04"],"award-info":[{"award-number":["2023AH051880","GY2022-04"]}]},{"name":"Fengyang Science and Technology Project","award":["2023AH051880","GY2022-04"],"award-info":[{"award-number":["2023AH051880","GY2022-04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The challenge of precise dynamic positioning for mobile robots is addressed through the development of a multi-inertial navigation system (M-INSs). The inherent cumulative sensor errors prevalent in traditional single inertial navigation systems (INSs) under dynamic conditions are mitigated by a novel algorithm, integrating multiple INS units in a predefined planar configuration, utilizing fixed distances between the units as invariant constraints. An extended Kalman filter (EKF) is employed to significantly enhance the positioning accuracy. Dynamic experimental validation of the proposed 3INS EKF algorithm reveals a marked improvement over individual INS units, with the positioning errors reduced and stability increased, resulting in an average accuracy enhancement rate exceeding 60%. This advancement is particularly critical for mobile robot applications that demand high precision, such as autonomous driving and disaster search and rescue. The findings from this study not only demonstrate the potential of M-INSs to improve dynamic positioning accuracy but also to provide a new research direction for future advancements in robotic navigation systems.<\/jats:p>","DOI":"10.3390\/s24041064","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T12:38:24Z","timestamp":1707223104000},"page":"1064","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Planar Multi-Inertial Navigation Strategy for Autonomous Systems for Signal-Variable Environments"],"prefix":"10.3390","volume":"24","author":[{"given":"Wenbin","family":"Dong","sequence":"first","affiliation":[{"name":"Department of Mechatronics Engineering, Hanyang University, Ansan 15588, Republic of Korea"},{"name":"School of Mechanical Engineering, Anhui Science and Technology University, Chuzhou 233100, China"}]},{"given":"Cheng","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Anhui Science and Technology University, Chuzhou 233100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5284-473X","authenticated-orcid":false,"given":"Le","family":"Bao","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, Hanyang University, Ansan 15588, Republic of Korea"}]},{"given":"Wenqi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, Hanyang University, Ansan 15588, Republic of Korea"}]},{"given":"Kyoosik","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, Hanyang University, Ansan 15588, Republic of Korea"}]},{"given":"Changsoo","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, Hanyang University, Ansan 15588, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.1109\/TRO.2021.3133730","article-title":"GVINS: Tightly Coupled GNSS\u2013Visual\u2013Inertial Fusion for Smooth and Consistent State Estimation","volume":"38","author":"Cao","year":"2022","journal-title":"IEEE Trans. Robot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s10291-023-01586-3","article-title":"Tightly coupled integration of monocular visual-inertial odometry and UC-PPP based on factor graph optimization in difficult urban environments","volume":"28","author":"Pan","year":"2024","journal-title":"GPS Solut."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Farhangian, F., Benzerrouk, H., and Landry, R. (2021). Opportunistic in-flight INS alignment using LEO satellites and a rotatory IMU platform. Aerospace, 8.","DOI":"10.3390\/aerospace8100280"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jiang, C., Zhao, D., Zhang, Q., and Liu, W. (2023). A Multi-GNSS\/IMU Data Fusion Algorithm Based on the Mixed Norms for Land Vehicle Applications. Remote Sens., 15.","DOI":"10.3390\/rs15092439"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fayyad, J., Jaradat, M.A., Gruyer, D., and Najjaran, H. (2020). Deep learning sensor fusion for autonomous vehicle perception and localization: A review. Sensors, 20.","DOI":"10.3390\/s20154220"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Debeunne, C., and Vivet, D. (2020). A review of visual-LiDAR fusion based simultaneous localization and mapping. Sensors, 20.","DOI":"10.3390\/s20072068"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8479","DOI":"10.1109\/JSEN.2021.3050456","article-title":"Kalman filter-based data fusion of Wi-Fi RTT and PDR for indoor localization","volume":"21","author":"Liu","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5288","DOI":"10.1109\/JSEN.2021.3089516","article-title":"A data-driven inertial navigation\/Bluetooth fusion algorithm for indoor localization","volume":"22","author":"Chen","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_9","first-page":"9304","article-title":"UWB system for indoor positioning and tracking with arbitrary target orientation, optimal anchor location, and adaptive NLOS mitigation","volume":"69","author":"Chen","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8247","DOI":"10.1109\/LRA.2023.3326385","article-title":"Robust and Adaptive Calibration of UWB-Aided Vision Navigation System for UAVs","volume":"8","author":"Hu","year":"2023","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Masiero, A., Toth, C., Gabela, J., Retscher, G., Kealy, A., Perakis, H., Gikas, V., and Grejner-Brzezinska, D. (2021). Experimental assessment of UWB and vision-based car cooperative positioning system. Remote Sens., 13.","DOI":"10.3390\/rs13234858"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103448","DOI":"10.1016\/j.autcon.2020.103448","article-title":"Real-time vision-based worker localization & hazard detection for construction","volume":"121","author":"Jeelani","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2729","DOI":"10.1109\/LRA.2021.3062008","article-title":"Autonomous UAV exploration of dynamic environments via incremental sampling and probabilistic roadmap","volume":"6","author":"Xu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, X., Vizzo, I., L\u00e4be, T., Behley, J., and Stachniss, C. (June, January 30). Range image-based LiDAR localization for autonomous vehicles. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA) 2021, Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561335"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Caballero, F., and Merino, L. (October, January 27). DLL: Direct LIDAR Localization. A map-based localization approach for aerial robots. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021, Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636501"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., and Foresti, G.L. (2021, January 20\u201323). VT-ADL: A vision transformer network for image anomaly detection and localization. Proceedings of the 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) 2021, Kyoto, Japan.","DOI":"10.1109\/ISIE45552.2021.9576231"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"221975","DOI":"10.1109\/ACCESS.2020.3043662","article-title":"Ground-level mapping and navigating for agriculture based on IoT and computer vision","volume":"8","author":"Zhao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/BF03546397","article-title":"A numerical implementation of spherical object collision probability","volume":"53","author":"Alfano","year":"2005","journal-title":"J. Astronaut. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1064\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:56:03Z","timestamp":1760104563000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1064"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,6]]},"references-count":18,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24041064"],"URL":"https:\/\/doi.org\/10.3390\/s24041064","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,2,6]]}}}