Abstract
Auto navigation and vehicle distance estimation are of the critical importance in intelligent vehicles. This paper proposes a novel approach to solve these two problems simultaneously by using a monocular camera. First, we introduce an incremental depth parameterized model to overcome the scale ambiguity problem in monocular vision, which differs from other parameterized models with reduced state-dimension and consistent observability. In addition, the view-field of camera is divided into static and dynamic parts so that the egomotion and vehicle-space can be calculated simultaneously. Outdoor experiments are implemented to validate the effectiveness of the proposed approach.
Similar content being viewed by others
References
Sorelo M A, Naranjo J E, Gonzdez C, et al. Vision-based adaptive cruise control for intelligent road vehicles. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, 2004. 64–69
Hartley R, Zisserman A. Multiple View Geometry in Computer Vision. Cambridge: Cambridge University Press, 2004
Schill F, Mahony R, Corke P. Estimating ego-motion in panoramic image sequences with inertial measurements. In: Pradalier C, Siegwart R, Hirzinger G, eds. Robotics Research. Springer Tracts in Advanced Robotics, Vol. 70. Berlin/Heidelberg: Springer, 2011. 87–101
Yamaguchi K, Kato T, Ninomiya Y. Vehicle ego-motion estimation and moving object detection using a monocular camera. In: the 18th International Conference on Pattern Recognition, Hong Kong, 2006. 610–613
Fraundorfer F, Scaramuzza D, Pollefeys M. A constricted bundle adjustment parameterization for relative scale estimation in visual odometry. In: IEEE International Conference on Robotics and Automation, Anchorage, 2010. 1899–1904
Davison A J. Real-time simultaneous localization and mapping with a single camera. In: Proceedings of the 9th IEEE International Conference on Computer Vision, Nice, 2003. 1403–1410
Civera J. Real-time EKF-based structure from motion. Dissertation of Doctoral Degree. Zaragoza University, 2009. 37–45
Zhao L, Huang S D, Yan L, et al. Parallax angle parametrization for monocular SLAM. In: IEEE International Conference on Robotics and Automation, Shanghai, 2011. 3117–3124
Taylor C N, Veth M J, Raquet J F, et al. Comparison of two image and inertial sensor fusion techniques for navigation in unmapped environments. IEEE Trans Aerosp Electron Syst, 2011, 47: 946–958
Sun Z H, Bebis G, Miller R. On-road vehicle detection: a review. IEEE Trans Patt Anal Mach Intell, 2006, 28: 694–711
Miksch M, Yang B, Zimmermann K. Motion compensation for obstacle detection based on homography and odometric data with virtual camera perspectives. In: IEEE Intelligent Vehicles Symposium, San Diego, 2010. 1152–1158
Cui J Z, Liu F Q, Li Z P, et al. Vehicle localisation using a single camera. In: IEEE Intelligent Vehicles Symposium, San Diego, 2010. 871–876
Zielke T, Brauckmann M, von Seelen W. Intensity and edge-based symmetry detection with an application to carfollowing. CVGIP-Image Understand, 1993, 58: 177–190
Dickmanns E. Vehicles capable of dynamic vision: a new breed of technical beings? Artif Intell, 1998, 103: 49–76
Streller D, Furstenberg K, Dietmayer K. Vehicle and object models for robust tracking in traffic scenes using laser range images. In: the IEEE 5th International Conference On Intelligent Transportation Systems, Singapore, 2002. 118–123
Alessandretti G, Broggi A, Cerri P. Vehicle and guard rail detection using radar and vision data fusion. IEEE Trans Intell Transp Syst, 2007, 8: 95–105
Chang B R, Tsai H F, Young C P. Intelligent data fusion system for predicting vehicle collision warning using vision/GPS sensing. Expert Syst Appl, 2010, 37: 2439–2450
Davison A J, Molton N D, Reid I D, et al. MonoSLAM: real-time single camera SLAM. IEEE Trans Patt Anal Mach Intell, 2007, 29: 1052–1067
Civera J, Davison A J, Montiel J. Inverse depth parametrization for monocular SLAM. IEEE Trans Robot, 2008, 24: 932–945
Civera J, Grasa O G, Davison A J, et al. 1-Point RANSAC for extended Kalman filtering: application to real-time structure from motion and visual odometry. J Field Robot, 2010, 27: 609–631
Triggs B, McLauchlan P, Hartley R, et al. Bundle adjustment-a modern synthesis. In: Proceedings of the International Workshop on Vision Algorithms: Theory and Practice. London: Springer-Verlag, 2000. 298–372
Mouragnon E, Lhuillier M, Dhome M, et al. Generic and real-time structure from motion using local bundle adjustment. Image Vis Comput, 2009, 27: 1178–1193
McInroy J E, Qi Z. A novel pose estimation algorithm based on points to regions correspondence. J Math Imaging Vis, 2008, 30: 195–207
Yang D F, Wang S C, Li H B, et al. Performance enhancement of large-ship transfer alignment: a moving horizon approach. J Navig, 2013, 66: 17–33
Seelen W V, Curio C, Gayko J, et al. Scene analysis and organization of behavior in driver assistance systems. In: IEEE International Conference of Image Processing, 2000, Vancouver. 524–527
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yang, D., Sun, F., Wang, S. et al. Simultaneous estimation of ego-motion and vehicle distance by using a monocular camera. Sci. China Inf. Sci. 57, 1–10 (2014). https://doi.org/10.1007/s11432-013-4884-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11432-013-4884-8