{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:47:09Z","timestamp":1760240829087,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T00:00:00Z","timestamp":1568332800000},"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":["51605054"],"award-info":[{"award-number":["51605054"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key 419 Technical Innovation Projects of Chongqing Artificial Intelligent Technology","award":["cstc2017rgzn-zdyfX0039"],"award-info":[{"award-number":["cstc2017rgzn-zdyfX0039"]}]},{"name":"The Science and Technology Research Program of Chongqing Education Commission of China","award":["KJQN201800517"],"award-info":[{"award-number":["KJQN201800517"]}]},{"name":"Chongqing Social Science Planning Project","award":["2018QNJJ16"],"award-info":[{"award-number":["2018QNJJ16"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We present a novel low-cost visual odometry method of estimating the ego-motion (self-motion) for ground vehicles by detecting the changes that motion induces on the images. Different from traditional localization methods that use differential global positioning system (GPS), precise inertial measurement unit (IMU) or 3D Lidar, the proposed method only leverage data from inexpensive visual sensors of forward and backward onboard cameras. Starting with the spatial-temporal synchronization, the scale factor of backward monocular visual odometry was estimated based on the MSE optimization method in a sliding window. Then, in trajectory estimation, an improved two-layers Kalman filter was proposed including orientation fusion and position fusion. Where, in the orientation fusion step, we utilized the trajectory error space represented by unit quaternion as the state of the filter. The resulting system enables high-accuracy, low-cost ego-pose estimation, along with providing robustness capability of handing camera module degradation by automatic reduce the confidence of failed sensor in the fusion pipeline. Therefore, it can operate in the presence of complex and highly dynamic motion such as enter-in-and-out tunnel entrance, texture-less, illumination change environments, bumpy road and even one of the cameras fails. The experiments carried out in this paper have proved that our algorithm can achieve the best performance on evaluation indexes of average in distance (AED), average in X direction (AEX), average in Y direction (AEY), and root mean square error (RMSE) compared to other state-of-the-art algorithms, which indicates that the output results of our approach is superior to other methods.<\/jats:p>","DOI":"10.3390\/rs11182139","type":"journal-article","created":{"date-parts":[[2019,9,16]],"date-time":"2019-09-16T03:17:57Z","timestamp":1568603877000},"page":"2139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Forward and Backward Visual Fusion Approach to Motion Estimation with High Robustness and Low Cost"],"prefix":"10.3390","volume":"11","author":[{"given":"Ke","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Mechanical Transmission, School of Automobile Engineering, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical Transmission, School of Automobile Engineering, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JunLan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Economics &amp; Management, Chongqing Normal University, Chongqing 401331, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuan","family":"Cao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical Transmission, School of Automobile Engineering, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhoubing","family":"Xiong","sequence":"additional","affiliation":[{"name":"Intelligent Vehicle R&amp;D Institute, Changan Auto Company, Chongqing 401120, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Company, Ltd., Chongqing 401122, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,13]]},"reference":[{"key":"ref_1","unstructured":"Gluckman, J., and Nayar, S.K. (1998, January 7). Ego-Motion and Omnidirectional Cameras. Proceedings of the International Conference on Computer Vision, Bombay, India."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.3390\/s130201919","article-title":"Extended Kalman Filter-Based Methods for Pose Estimation Using Visual, Inertial and Magnetic Sensors: Comparative Analysis and Performance Evaluation","volume":"13","author":"Gabriele","year":"2013","journal-title":"Sensors"},{"key":"ref_3","first-page":"2021","article-title":"Visual Enhancement Method for Intelligent Vehicle\u2019s Safety Based on Brightness Guide Filtering Algorithm Thinking of The High Tribological and Attenuation Effects","volume":"22","author":"Wang","year":"2016","journal-title":"J. Balk. Tribol. Assoc."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, J.L., Wang, K., Bao, H.H., and Chen, T. (2019). A Design of Cooperative Overtaking Based on Complex Lane Detection and Collision Risk Estimation. IEEE Access., 87951\u201387959.","DOI":"10.1109\/ACCESS.2019.2922113"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.3901\/CJME.2014.0707.118","article-title":"Simultaneous Multi-vehicle Detection and Tracking Framework with Pavement Constraints Based on Machine Learning and Particle Filter Algorithm","volume":"27","author":"Wang","year":"2014","journal-title":"Chin. J. Mech. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2034","DOI":"10.1109\/TCE.2009.5373766","article-title":"A Surveillance Robot with Hopping Capabilities for Home Security","volume":"55","author":"Song","year":"2010","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_7","unstructured":"Ciuonzo, D., Buonanno, A., D\u2019Urso, M., and Palmieri, F.A.N. (2011, January 5\u20138). Distributed Classification of Multiple Moving Targets with Binary Wireless Sensor Networks. Proceedings of the International Conference on Information Fusion, Chicago, IL, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"31869","DOI":"10.3390\/s151229892","article-title":"Quantitative Evaluation of Stereo Visual Odometry for Autonomous Vessel Localisation in Inland Waterway Sensing Applications","volume":"15","author":"Kriechbaumer","year":"2015","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhu, J.S., Li, Q., Cao, R., Sun, K., Liu, T., Garibaldi, J.M., Li, Q.Q., Liu, B.Z., and Qiu, G.P. (2019). Indoor Topological Localization Using a Visual Landmark Sequence. Remote Sens., 11.","DOI":"10.3390\/rs11010073"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1002\/rob.21757","article-title":"An architecture for robust UAV navigation in GPS-denied areas","volume":"35","author":"Ragel","year":"2018","journal-title":"J. Field Robot."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, G.C., Chen, Z.J., Li, Y., and Su, Z.D. (2019). Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11020149"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"10454","DOI":"10.3390\/s140610454","article-title":"Occupancy Grid Mapping in Urban Environments from a Moving On-Board Stereo-Vision System","volume":"14","author":"Li","year":"2014","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MRA.2011.943233","article-title":"Visual Odometry [Tutorial]","volume":"18","author":"Scaramuzza","year":"2011","journal-title":"Robot. Autom. Mag. IEEE"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1504\/IJVD.2018.099709","article-title":"Collision probability prediction algorithm for cooperative overtaking based on TTC and conflict probability estimation method","volume":"77","author":"Chen","year":"2018","journal-title":"Int. J. Veh. Des."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2878","DOI":"10.1109\/LRA.2018.2846813","article-title":"Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect","volume":"3","author":"Yang","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mou, X.Z., and Wang, H. (2018). Wide-Baseline Stereo-Based Obstacle Mapping for Unmanned Surface Vehicles. Sensors, 18.","DOI":"10.3390\/s18041085"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/s11263-011-0441-3","article-title":"1-Point-RANSAC Structure from Motion for Vehicle-Mounted Cameras by Exploiting Non-holonomic Constraints","volume":"95","author":"Scaramuzza","year":"2011","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1002\/rob.21809","article-title":"Laser-visual-inertial odometry and mapping with high robustness and low drift","volume":"35","author":"Zhang","year":"2018","journal-title":"J. Field Robot."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1049\/iet-its.2014.0100","article-title":"Robust visual odometry estimation of road vehicle from dominant surfaces for large-scale mapping","volume":"9","author":"Siddiqui","year":"2014","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_20","unstructured":"Ji, Z., and Singh, S. (2015, January 26\u201330). Visual-Lidar Odometry and Mapping: Low-Drift, Robust, and Fast. Proceedings of the IEEE International Conference on Robotics and Automation, Seattle, WA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s11554-014-0425-y","article-title":"A temporally consistent grid-based visual odometry framework for multi-core architectures","volume":"10","author":"Demaeztu","year":"2015","journal-title":"J. Real Time Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1038\/293133a0","article-title":"A computer algorithm for reconstructing a scene from two projections","volume":"293","year":"1981","journal-title":"Nature"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/0262-8856(88)90003-0","article-title":"3D positional integration from image sequences","volume":"6","author":"Harris","year":"1988","journal-title":"Image Vis. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1002\/rob.20184","article-title":"Two years of Visual Odometry on the Mars Exploration Rovers","volume":"24","author":"Maimone","year":"2010","journal-title":"J. Field Robot."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.1109\/TITS.2014.2298492","article-title":"Vision-Only Localization","volume":"15","author":"Lategahn","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1109\/TITS.2011.2177522","article-title":"Simultaneous Localization and Mapping for Path-Constrained Motion","volume":"13","author":"Hasberg","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MRA.2012.2182810","article-title":"Visual Odometry: Part II: Matching, Robustness, Optimization, and Applications","volume":"19","author":"Fraundorfer","year":"2012","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_28","first-page":"3","article-title":"Visual odometry for ground vehicle applications","volume":"23","author":"Naroditsky","year":"2010","journal-title":"J. Field Robot."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Scaramuzza, D., Fraundorfer, F., and Siegwart, R. (2009, January 12\u201317). Real-Time Monocular Visual Odometry for on-Road Vehicles with 1-Point RANSAC. Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152255"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TRO.2016.2597321","article-title":"On-Manifold Preintegration for Real-Time Visual-Inertial Odometry","volume":"33","author":"Forster","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pascoe, G., Maddern, W., Tanner, M., Pini\u00e9s, P., and Newman, P. (2017, January 21\u201326). Nid-Slam: Robust Monocular Slam Using Normalised Information Distance. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.158"},{"key":"ref_32","unstructured":"Nister, D., Naroditsky, O., and Bergen, J. (July, January 27). Visual Odometry. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA."},{"key":"ref_33","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","author":"Tardos","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1109\/34.473228","article-title":"Structure and motion from line segments in multiple images","volume":"17","author":"Taylor","year":"1995","journal-title":"Pattern Anal. Mach. Intell. IEEE Trans."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/S0262-8856(02)00015-X","article-title":"Structure and motion estimation from apparent contours under circular motion","volume":"20","author":"Wong","year":"2002","journal-title":"Image Vis. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pradeep, V., and Lim, J. (2010, January 13\u201318). Egomotion Using Assorted Features. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539792"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1109\/TPAMI.2004.17","article-title":"An efficient solution to the five-point relative pose problem","volume":"26","author":"David","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/BF02028352","article-title":"Review and analysis of solutions of the three point perspective pose estimation problem","volume":"13","author":"Haralick","year":"1994","journal-title":"Int. J. Comput. Vis."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Song, Y., Nuske, S., and Scherer, S. (2017). A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors. Sensors, 17.","DOI":"10.3390\/s17010011"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"16581","DOI":"10.1007\/s11042-016-3939-4","article-title":"Ego-motion estimation concepts, algorithms and challenges: An overview","volume":"76","author":"Khan","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, Z., Zheng, W.J., Wang, H., and Liu, J.G. (2017). Monocular Visual-Inertial SLAM: Continuous Preintegration and Reliable Initialization. Sensors, 17.","DOI":"10.3390\/s17112613"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A flexible new technique for camera calibration","volume":"22","author":"Zhang","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intel."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/0278364916679498","article-title":"1 year, 1000 km: The Oxford RobotCar dataset","volume":"36","author":"Maddern","year":"2017","journal-title":"Int. J. Robot. Res."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","unstructured":"Sturm, J., Engelhard, N., Endres, F., Burgard, W., and Cremers, D. (2012, January 7\u201312). A Benchmark for the Evaluation of RGB-D SLAM Systems. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Portugal.","DOI":"10.1109\/IROS.2012.6385773"},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"6205","DOI":"10.1109\/TIE.2016.2573765","article-title":"Stereo Visual-Inertial Odometry with Multiple Kalman Filters Ensemble","volume":"63","author":"Yong","year":"2016","journal-title":"IEEE Trans. Ind. Electron."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/18\/2139\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:20:00Z","timestamp":1760188800000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/18\/2139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,13]]},"references-count":47,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["rs11182139"],"URL":"https:\/\/doi.org\/10.3390\/rs11182139","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,9,13]]}}}