{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T13:11:37Z","timestamp":1781356297795,"version":"3.54.1"},"reference-count":19,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672244, 91748106"],"award-info":[{"award-number":["61672244, 91748106"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hubei Province Natural Science Foundation of China","award":["2019CFB526"],"award-info":[{"award-number":["2019CFB526"]}]},{"name":"Shandong Province Key Research and Development Project of China","award":["2019JZZY010443"],"award-info":[{"award-number":["2019JZZY010443"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Currently, simultaneous localization and mapping (SLAM) is one of the main research topics in the robotics field. Visual-inertia SLAM, which consists of a camera and an inertial measurement unit (IMU), can significantly improve robustness and enable scale weak-visibility, whereas monocular visual SLAM is scale-invisible. For ground mobile robots, the introduction of a wheel speed sensor can solve the scale weak-visibility problem and improve robustness under abnormal conditions. In this paper, a multi-sensor fusion SLAM algorithm using monocular vision, inertia, and wheel speed measurements is proposed. The sensor measurements are combined in a tightly coupled manner, and a nonlinear optimization method is used to maximize the posterior probability to solve the optimal state estimation. Loop detection and back-end optimization are added to help reduce or even eliminate the cumulative error of the estimated poses, thus ensuring global consistency of the trajectory and map. The outstanding contribution of this paper is that the wheel odometer pre-integration algorithm, which combines the chassis speed and IMU angular speed, can avoid the repeated integration caused by linearization point changes during iterative optimization; state initialization based on the wheel odometer and IMU enables a quick and reliable calculation of the initial state values required by the state estimator in both stationary and moving states. Comparative experiments were conducted in room-scale scenes, building scale scenes, and visual loss scenarios. The results showed that the proposed algorithm is highly accurate\u20142.2 m of cumulative error after moving 812 m (0.28%, loopback optimization disabled)\u2014robust, and has an effective localization capability even in the event of sensor loss, including visual loss. The accuracy and robustness of the proposed method are superior to those of monocular visual inertia SLAM and traditional wheel odometers.<\/jats:p>","DOI":"10.3390\/s21165522","type":"journal-article","created":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T21:17:06Z","timestamp":1629235026000},"page":"5522","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Robust Tightly Coupled Pose Measurement Based on Multi-Sensor Fusion in Mobile Robot System"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8801-0972","authenticated-orcid":false,"given":"Gang","family":"Peng","sequence":"first","affiliation":[{"name":"Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Wuhan 430070, China"},{"name":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zezao","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Wuhan 430070, China"},{"name":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7669-000X","authenticated-orcid":false,"given":"Jiaxi","family":"Peng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Wuhan 430070, China"},{"name":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dingxin","family":"He","sequence":"additional","affiliation":[{"name":"Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Wuhan 430070, China"},{"name":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinde","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Wuhan 430070, China"},{"name":"School of Automation, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Hu","sequence":"additional","affiliation":[{"name":"Shantui Construction Machinery Co., Ltd., Jining 272000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,17]]},"reference":[{"key":"ref_1","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_2","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). LSD-SLAM: Large-scale direct monocular SLAM. European Conference on Computer Vision, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TPAMI.2017.2658577","article-title":"Direct sparse odometry","volume":"40","author":"Engel","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach Intell."},{"key":"ref_4","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, Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6906584"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1142\/S2301385019410012","article-title":"Evidential SLAM fusing 2D laser scanner and stereo camera","volume":"7","author":"Valente","year":"2019","journal-title":"Unmanned Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Unsal, D., and Demirbas, K. (2012, January 23\u201326). Estimation of deterministic and stochastic IMU error parameters. Proceedings of the 2012 IEEE\/ION Position, Location and Navigation Symposium, Myrtle Beach, SC, USA.","DOI":"10.1109\/PLANS.2012.6236828"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wu, K.J., Guo, C.X., Georgiou, G., and Roumeliotis, S.I. (June, January 29). VINS on wheels. Proceedings of the 2017 IEEE International Conference on Robotics and Automation, Marina Bay Sands, Singapore.","DOI":"10.1109\/ICRA.2017.7989603"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TIM.2017.2754678","article-title":"Novel EKF-Based Vision\/Inertial System Integration for Improved Navigation","volume":"67","author":"Karamat","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Qian, J., Zi, B., Wang, D., Ma, Y., and Zhang, D. (2017). The design and development of an omni-directional mobile robot oriented to an intelligent manufacturing system. Sensors, 17.","DOI":"10.3390\/s17092073"},{"key":"ref_10","first-page":"8562","article-title":"ST-VIO: Visual Inertial Odometry Combined with Image Segmentation and Tracking","volume":"69","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_11","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_12","unstructured":"Qin, T., Cao, S., Pan, J., and Shen, S. (2019). A general optimization-based framework for global pose estimation with multiple sensors. arXiv."},{"key":"ref_13","unstructured":"Qin, T., Pan, J., Cao, S., and Shen, S. (2019). A general optimization-based framework for local odometry estimation with multiple sensors. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, J., Shi, Z., and Zhong, Y. (2017, January 26\u201328). Visual SLAM incorporating wheel odometer for indoor robots. Proceedings of the 2017 36th Chinese Control Conference (CCC), Dalian, China.","DOI":"10.23919\/ChiCC.2017.8028171"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2652","DOI":"10.1109\/TCYB.2018.2831900","article-title":"Odometry-Vision-Based Ground Vehicle Motion Estimation with SE(2)-Constrained SE(3) Poses","volume":"49","author":"Zheng","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_16","first-page":"1","article-title":"Multisensor-Based Navigation and Control of a Mobile Service Robot","volume":"49","author":"Yuan","year":"2019","journal-title":"IEEE Trans. Syst. Man. Cybern. Syst."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Huber, P.J. (1992). Robust Estimation of a Location Parameter, Springer.","DOI":"10.1007\/978-1-4612-4380-9_35"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Forster, C., Carlone, L., Dellaert, F., and Scaramuzza, D. (2015). IMU preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation. 2015 Robotics Science and Systems, Georgia Institute of Technology.","DOI":"10.15607\/RSS.2015.XI.006"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5522\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:45:31Z","timestamp":1760165131000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5522"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,17]]},"references-count":19,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["s21165522"],"URL":"https:\/\/doi.org\/10.3390\/s21165522","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,17]]}}}