{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:31:27Z","timestamp":1772724687065,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2015,6,1]],"date-time":"2015-06-01T00:00:00Z","timestamp":1433116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a novel approach for estimating the ego-motion of a vehicle in dynamic and unknown environments using tightly-coupled inertial and visual sensors. To improve the accuracy and robustness, we exploit the combination of point and line features to aid navigation. The mathematical framework is based on trifocal geometry among image triplets, which is simple and unified for point and line features. For the fusion algorithm design, we employ the Extended Kalman Filter (EKF) for error state prediction and covariance propagation, and the Sigma Point Kalman Filter (SPKF) for robust measurement updating in the presence of high nonlinearities. The outdoor and indoor experiments show that the combination of point and line features improves the estimation accuracy and robustness compared to the algorithm using point features alone.<\/jats:p>","DOI":"10.3390\/s150612816","type":"journal-article","created":{"date-parts":[[2015,6,1]],"date-time":"2015-06-01T10:55:28Z","timestamp":1433156128000},"page":"12816-12833","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Tightly-Coupled Stereo Visual-Inertial Navigation Using Point and Line Features"],"prefix":"10.3390","volume":"15","author":[{"given":"Xianglong","family":"Kong","sequence":"first","affiliation":[{"name":"College of Mechatronics and Automation, National University of Defense Technology,  Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenqi","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mechatronics and Automation, National University of Defense Technology,  Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lilian","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechatronics and Automation, National University of Defense Technology,  Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujie","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechatronics and Automation, National University of Defense Technology,  Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Titterton, D.H., and Weston, J.L. (2004). Srapdown Inertial Navigation Technology, The Institution of Electrical Engineers. [2nd ed.].","DOI":"10.1049\/PBRA017E"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1177\/0278364910382802","article-title":"Visual-inertial sensor fusion: Localization, mapping and sensor-to-sensor self-calibration","volume":"30","author":"Kelly","year":"2011","journal-title":"Int. J. Robot. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8877","DOI":"10.3390\/s120708877","article-title":"Observability analysis of a matrix kalman filter-based navigation system using visual\/inertial\/magnetic sensors","volume":"12","author":"Feng","year":"2012","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2239","DOI":"10.1109\/TAES.2012.6237590","article-title":"Real-time vision-aided localization and navigation based on three-view geometry","volume":"48","author":"Indelman","year":"2012","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Weiss, S., Achtelik, M.W., Lynen, S., Chli, M., and Siegwart, R. (2012, January 14\u201318). Real-time onboard visual-inertial state estimation and self-calibration of mavs in unknown environments. Proceeding of the IEEE International Conference on Robotics and Automation, St. Paul MN, USA.","DOI":"10.1109\/ICRA.2012.6225147"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kottas, D.G., and Roumeliotis, S.I. (2013, January 6\u201310). Efficient and consistent vision-aided inertial navigation using line observations. Proceeding of the IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630775"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1177\/0278364913481251","article-title":"High-precision, consistent EKF-based visual-inertial odometry","volume":"32","author":"Li","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1177\/0278364913509675","article-title":"Camera-imu-based localization: Observability analysis and consistency improvement","volume":"33","author":"Hesch","year":"2014","journal-title":"Int. J. Robot. Res."},{"key":"ref_9","unstructured":"Hu, J.-S., and Chen, M.-Y. (June, January 31). A sliding-window visual-imu odometer based on tri-focal tensor geometry. Proceeding of the IEEE International Conference on Robotics and Automation, Hong Kong, China."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1177\/0278364907079279","article-title":"An introduction to inertial and visual sensing","volume":"26","author":"Corke","year":"2007","journal-title":"Int. J. Robot. Res."},{"key":"ref_11","unstructured":"Roumeliotis, S.I., Johnson, A.E., and Montgomery, J.F. (2002, January 12\u201318). Augmenting inertial navigation with image-based motion estimation. Proceeding of the IEEE International Conference on Robotics and Automation, Washington, DC, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Diel, D.D., DeBitetto, P., and Teller, S. (2005, January 5\u20137). Epipolar constraints for vision-aided inertial navigation. Proceeding of the Seventh IEEE Workshops on Application of Computer Vision, Breckenridge, CO, USA.","DOI":"10.1109\/ACVMOT.2005.48"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tardif, J.-P., George, M., and Laverne, M. (2010, January 18\u201322). A new approach to vision-aided inertial navigation. Proceeding of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5651059"},{"key":"ref_14","unstructured":"Sirtkaya, S., Seymen, B., and Alatan, A.A. (2013, January 9\u201312). Loosely coupled kalman filtering for fusion of visual odometry and inertial navigation. Proceeding of the 16th International Conference on Information Fusion (FUSION), Istanbul, Turkey."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mourikis, A., and Roumeliotis, S.I. (2007, January 10\u201314). A multi-state constraint kalman filter for vision-aided inertial navigation. Proceeding of the IEEE Inernational Conference in Robotics and Automation, Roma, Italy.","DOI":"10.1109\/ROBOT.2007.364024"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Leutenegger, S., Furgale, P.T., Rabaud, V., Chli, M., Konolige, K., and Siegwart, R. (2013, January 24\u201328). Keyframe-based visual-inertial slam using nonlinear optimization. Proceeding of the Robotics: Science and Systems, Berlin, Germany.","DOI":"10.15607\/RSS.2013.IX.037"},{"key":"ref_17","unstructured":"Zhang, L. (2013). Line Primitives and Their Applications in Geometric Computer Vision. [Ph.D. Thesis, Kiel University]."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hartley, R., and Zisserman, A. (2004). Multiple View Geometry in Computer Vision, Cambridge University Press. [2nd ed.].","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s11263-011-0492-5","article-title":"Impact of landmark parametrization on monocular ekf-slam with points and lines","volume":"97","author":"Sola","year":"2012","journal-title":"Int. J. Comput. Vision."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Weiss, S., and Siegwart, R. (2011, January 9\u201313). Real-time metric state estimation for modular vision-inertial systems. Proceeding of the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China.","DOI":"10.1109\/ICRA.2011.5979982"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1002\/j.2161-4296.2003.tb00319.x","article-title":"A new positioning filter: Phase smoothing in the position domain","volume":"50","author":"Ford","year":"2003","journal-title":"Navigation"},{"key":"ref_22","unstructured":"Van Der Merwe, R. (2004). Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models. [Ph.D. Thesis, Oregon Health & Science University]."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Julier, S.J. (2002, January 8\u201310). The scaled unscented transformation. Proceeding of the American Control Conference, Anchorage, AK, USA.","DOI":"10.1109\/ACC.2002.1025369"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for autonomous driving? The kitti vision benchmark suite. Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, Rhode Island, Greece.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1007\/11744023_34","article-title":"Machine learning for high-speed corner detection","volume":"Volume 3951","author":"Leonardis","year":"2006","journal-title":"Computer Vision\u2013Eccv 2006"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/0004-3702(95)00022-4","article-title":"A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry","volume":"78","author":"Zhang","year":"1995","journal-title":"Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1016\/j.patrec.2011.06.001","article-title":"Edlines: A real-time line segment detector with a false detection control","volume":"32","author":"Akinlar","year":"2011","journal-title":"Pattern Recognit. Lett."},{"key":"ref_28","first-page":"236","article-title":"Line matching using appearance similarities and geometric constraints","volume":"Volume 7476","author":"Pinz","year":"2012","journal-title":"Pattern Recognition"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_30","unstructured":"Bar-Shalom, Y., Li, X.R., and Kirubarajan, T. (2004). Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software, John Wiley & Sons."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Geiger, A., Ziegler, J., and Stiller, C. (2011, January 5\u20139). Stereoscan: Dense 3D reconstruction in real-time. Proceeding of the IEEE Intelligent Vehicles Symposium, Baden-Baden, Germany.","DOI":"10.1109\/IVS.2011.5940405"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Furgale, P., Rehder, J., and Siegwart, R. (2013, January 3\u20138). Unified temporal and spatial calibration for multi-sensor systems. Proceeding of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan.","DOI":"10.1109\/IROS.2013.6696514"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Coughlan, J.M., and Yuille, A.L. (1999, January 20\u201327). Manhattan world: Compass direction from a single image by bayesian inference. Proceeding of the IEEE International Conference on Computer Vision, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790349"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/6\/12816\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:47:16Z","timestamp":1760215636000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/6\/12816"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,6,1]]},"references-count":33,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2015,6]]}},"alternative-id":["s150612816"],"URL":"https:\/\/doi.org\/10.3390\/s150612816","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,6,1]]}}}