{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:48:06Z","timestamp":1778086086135,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Joint Fund of Ministry of Education","award":["6141A02011907"],"award-info":[{"award-number":["6141A02011907"]}]},{"name":"The National Key Research and Development Program of China","award":["2018YFB1305001"],"award-info":[{"award-number":["2018YFB1305001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we proposed a multi-sensor integrated navigation system composed of GNSS (global navigation satellite system), IMU (inertial measurement unit), odometer (ODO), and LiDAR (light detection and ranging)-SLAM (simultaneous localization and mapping). The dead reckoning results were obtained using IMU\/ODO in the front-end. The graph optimization was used to fuse the GNSS position, IMU\/ODO pre-integration results, and the relative position and relative attitude from LiDAR-SLAM to obtain the final navigation results in the back-end. The odometer information is introduced in the pre-integration algorithm to mitigate the large drift rate of the IMU. The sliding window method was also adopted to avoid the increasing parameter numbers of the graph optimization. Land vehicle tests were conducted in both open-sky areas and tunnel cases. The tests showed that the proposed navigation system can effectually improve accuracy and robustness of navigation. During the navigation drift evaluation of the mimic two-minute GNSS outages, compared to the conventional GNSS\/INS (inertial navigation system)\/ODO integration, the root mean square (RMS) of the maximum position drift errors during outages in the proposed navigation system were reduced by 62.8%, 72.3%, and 52.1%, along the north, east, and height, respectively. Moreover, the yaw error was reduced by 62.1%. Furthermore, compared to the GNSS\/IMU\/LiDAR-SLAM integration navigation system, the assistance of the odometer and non-holonomic constraint reduced vertical error by 72.3%. The test in the real tunnel case shows that in weak environmental feature areas where the LiDAR-SLAM can barely work, the assistance of the odometer in the pre-integration is critical and can effectually reduce the positioning drift along the forward direction and maintain the SLAM in the short-term. Therefore, the proposed GNSS\/IMU\/ODO\/LiDAR-SLAM integrated navigation system can effectually fuse the information from multiple sources to maintain the SLAM process and significantly mitigate navigation error, especially in harsh areas where the GNSS signal is severely degraded and environmental features are insufficient for LiDAR-SLAM.<\/jats:p>","DOI":"10.3390\/s20174702","type":"journal-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T09:35:31Z","timestamp":1597916131000},"page":"4702","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":82,"title":["GNSS\/IMU\/ODO\/LiDAR-SLAM Integrated Navigation System Using IMU\/ODO Pre-Integration"],"prefix":"10.3390","volume":"20","author":[{"given":"Le","family":"Chang","sequence":"first","affiliation":[{"name":"GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5591-0859","authenticated-orcid":false,"given":"Xiaoji","family":"Niu","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyi","family":"Liu","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"ref_1","unstructured":"Shin, E.-H. (2001). Accuarcy Improvement of Low Cost INS\/GPS for Land Applications. [Master\u2019s Thesis, University of Calgary]."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1002\/rob.20104","article-title":"Mobile robot motion estimation by 2D scan matching with genetic and iterative closest point algorithms","volume":"23","author":"Morales","year":"2006","journal-title":"J. Field Robot."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Singh, S. (2014, January 12\u201316). LOAM: Lidar Odometry and Mapping in Real-time. Proceedings of the 2014 Robotics: Science and Systems, Berkeley, CA, USA.","DOI":"10.15607\/RSS.2014.X.007"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shan, T., and Englot, B. (2018, January 1\u20135). Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594299"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhang, L., Qin, S., Tian, D., Ouyang, S., and Chen, C. (2019). Optimized LOAM Using Ground Plane Constraints and SegMatch-Based Loop Detection. Sensors, 19.","DOI":"10.3390\/s19245419"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Censi, A. (2008, January 27). An ICP variant using a point-to-line metric. Proceedings of the 2008 IEEE International Conference on Robotics and Automation, Nice, France.","DOI":"10.1109\/ROBOT.2008.4543181"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Segal, A., Haehnel, D., and Thrun, S. (July, January 28). Generalized-icp. Proceedings of the 2009 Robotics: Science and Systems, Seattle, WA, USA.","DOI":"10.15607\/RSS.2009.V.021"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s10514-009-9108-0","article-title":"On the use of likelihood fields to perform sonar scan matching localization","volume":"26","author":"Burguera","year":"2009","journal-title":"Auton. Robot."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hess, W., Kohler, D., Rapp, H., and Andor, D. (2016, January 16\u201320). Real-time loop closure in 2D LIDAR SLAM. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487258"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Qian, C., Liu, H., Tang, J., Chen, Y., Kaartinen, H., Kukko, A., Zhu, L., Liang, X., Chen, L., and Hyypp\u00e4, J. (2017). An integrated GNSS\/INS\/LiDAR-SLAM positioning method for highly accurate forest stem mapping. Remote Sens., 9.","DOI":"10.3390\/rs9010003"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"23286","DOI":"10.3390\/s150923286","article-title":"INS\/GPS\/LiDAR integrated navigation system for urban and indoor environments using hybrid scan matching algorithm","volume":"15","author":"Gao","year":"2015","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chiang, K.-W., Tsai, G.-J., Li, Y.-H., Li, Y., and El-Sheimy, N. (2020). Navigation Engine Design for Automated Driving Using INS\/GNSS\/3D LiDAR-SLAM and Integrity Assessment. Remote Sens., 12.","DOI":"10.3390\/rs12101564"},{"key":"ref_14","first-page":"1","article-title":"Consistent map building in petrochemical complexes for firefighter robots using SLAM based on GPS and LIDAR","volume":"5","author":"Shamsudin","year":"2018","journal-title":"Robomech J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.isprsjprs.2017.09.006","article-title":"Graph SLAM correction for single scanner MLS forest data under boreal forest canopy","volume":"132","author":"Kukko","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","unstructured":"(2018, July 09). Cartographer. Available online: https:\/\/github.com\/cartographer-project\/cartographer."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2463","DOI":"10.1109\/TVT.2020.2966765","article-title":"Performance Enhancement of INS\/GNSS\/Refreshed-SLAM Integration for Acceptable Lane-Level Navigation Accuracy","volume":"69","author":"Chiang","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/MRA.2006.1678144","article-title":"Simultaneous localization and mapping: Part I","volume":"13","author":"Bailey","year":"2006","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chang, L., Niu, X., Liu, T., Tang, J., and Qian, C. (2019). GNSS\/INS\/LiDAR-SLAM Integrated Navigation System Based on Graph Optimization. Remote Sens., 11.","DOI":"10.3390\/rs11091009"},{"key":"ref_20","unstructured":"Shin, E.-H., and Estimation Techniques for Low-Cost Inertial Navigation (2015, September 01). UCGE Report. Available online: https:\/\/www.ucalgary.ca\/engo_webdocs\/NES\/05.20219.EHShin.pdf."},{"key":"ref_21","unstructured":"Sukkarieh, S. (2000). Low Cost, High Integrity, Aided Inertial Navigation Systems for Autonomous Land Vehicles. [Ph.D. Thesis, University of Sydney]."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1109\/70.964672","article-title":"The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications","volume":"17","author":"Dissanayake","year":"2001","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wu, Y., Wu, M., Hu, X., and Hu, D. (2009, January 10\u201313). Self-calibration for land navigation using inertial sensors and odometer: Observability analysis. Proceedings of the AIAA Guidance Navigation, and Control Conference, Chicago, IL, USA.","DOI":"10.2514\/6.2009-5970"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, S., Guo, Y., Zhu, Q., and Liu, Z. (2019, January 3\u20135). Lidar-IMU and Wheel Odometer Based Autonomous Vehicle Localization System. Proceedings of the Chinese Control and Decision Conference, Nanchang, China.","DOI":"10.1109\/CCDC.2019.8832695"},{"key":"ref_25","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_26","unstructured":"Chen, Q., Zhang, Q., and Niu, X. (2020). Estimate the Pitch and Heading Mounting Angles of the IMU for Land Vehicular GNSS\/INS Integrated System. IEEE Trans. Intell. Transp. Syst., 1\u201313."},{"key":"ref_27","unstructured":"Agarwal, S., and Mierle, K. (2018, March 23). Ceres-Solver. Available online: http:\/\/ceres-solver.org."},{"key":"ref_28","unstructured":"Xiangyuan, K., Jiming, G., and Zongquan, L. (2010). Foundation of Geodesy, Wuhan University Press. [2nd ed.]."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Le Gentil, C., Vidal-Calleja, T., and Huang, S. (2018, January 21\u201326). 3d lidar-imu calibration based on upsampled preintegrated measurements for motion distortion correction. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8460179"},{"key":"ref_30","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_31","unstructured":"Jun, W., and Gongmin, Y. (2016). Strapdown Inertial Navigation Algorithm and Integrated Navigation Principles, Northwestern Polytechnical University Press, Co. Ltd."},{"key":"ref_32","unstructured":"Yongyuan, Q. (2000). Kalman Filter and Integrated Navigation Principle, Northwestern Polytechnical University Press, Co. Ltd."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1002\/rob.20360","article-title":"Sliding window filter with application to planetary landing","volume":"27","author":"Sibley","year":"2010","journal-title":"J. Field Robot."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Eckenhoff, K., Paull, L., and Huang, G. (2016, January 9\u201314). Decoupled, consistent node removal and edge sparsification for graph-based SLAM. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759505"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4702\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:03:54Z","timestamp":1760177034000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4702"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":34,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20174702"],"URL":"https:\/\/doi.org\/10.3390\/s20174702","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,20]]}}}