Incorporating a wheeled vehicle model in a new monocular visual odometry algorithm for dynamic outdoor environments

Sensors (Switzerland)

Volumn 14, Issue 9, 2014, Pages 16159-16180

  Jiang, Yanhua ^a,b   Xiong, Guangming ^a   Chen, Huiyan ^a   Lee, Dah Jye ^c  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b BAIC Motor Corporation Ltd   (China)

^c Brigham Young University   (United States)

Author keywords

Monocular visual odometry; Motion estimation; Pose estimation; Vehicle dynamic model; Wheeled vehicles

Indexed keywords

OUTDOOR ENVIRONMENT; POSE ESTIMATION; VEHICLE DYNAMIC MODEL; VISUAL ODOMETRY; WHEELED VEHICLES;

MOTION ESTIMATION;

EID: 84907482029     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s140916159     Document Type: Article

References (27)

1. Scaramuzza D.; Fraundorfer, F. Visual Odometry Part I: The First 30 Years and Fundamentals. IEEE Robot. Autom. Mag. 2011, 18, 80–92.
2. Moravec H. Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover. Ph.D. Thesis, University of Stanford, Stanford, CA, USA, 1980.
3. Maimone M.; Cheng, Y.; Matthies, Y.L. Two Years of Visual Odometry on the Mars Exploration Rovers. J. Field Robot. 2007, 24, 169–186.
4. Taylor C.J.; Kriegman, D.J. Structure and motion from line segments in multiple images. IEEE Trans. Pattern Anal. Mach. Intell. 1995, 17, 1021–1032.
5. Wong K.-Y.K.; Mendonça, P.R.S.; Cipolla, R. Structure and motion estimation from apparent contours under circular motion. Image Vision Comput. 2002, 20, 441–448.
6. Pradeep V.; Lim, J. Egomotion using assorted features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 13–18 June 2010; pp. 1514–1521.
7. Gauglitz S.; Höllerer, T.; Turk, M. Evaluation of interest point detectors and feature descriptors for visual tracking. Int. J. Comput. Vision 2011, 3, 335–360.
8. Fischler M.A.; Bolles, R.C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Gr. Image Process. 1981, 24, 381–395.
9. Haralick R.M.; Lee, C.N.; Ottenberg, K.; Nölle, M. Review and analysis of solutions of the three point perspective pose estimation problem. Int. J. Comput. Vision 1994, 13, 331–356.
10. Nistér, D.; Naroditsky, O.; Bergen, J. Visual Odometry. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, 27 June–2 July 2004; pp. 652–659.
11. Nourani-Vatani, N.; Roberts, J.; Srinivasan, M.V. Practical visual odometry for car-like vehicles. In Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan, 12–17 May 2009; pp. 3551–3557.
12. Scaramuzza D. 1-point-ransac structure from motion for vehicle-mounted cameras by exploiting non-holonomic constraints. Int. J. Comput. Vision 2011, 95, 74–85.
13. Scaramuzza D.; Fraundorfer, F.; Siegwart, R. Real-time monocular visual odometry for on-road vehicles with 1-point ransac. In Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan, 12–17 May 2009; pp. 4293–4299.
14. Zhu M.; Ramalingam, S.; Taguchi, Y.; Garaas, T. Monocular Visual Odometry and Dense 3d Reconstruction for On-Road Vehicles. In Computer Vision–ECCV 2012. Workshops and Demonstrations; Springer Heidelberg: Berlin, Germany, 2012; pp. 596–606.
15. Scaramuzza D.; Censi, A.; Daniilidis, K. Exploiting motion priors in visual odometry for vehicle-mounted cameras with non-holonomic constraints. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA, 25–30 September 2011; pp. 4469–4476.
16. Hamme D.V.; Veelaert, P.; Philips, W. Robust Monocular Visual Odometry by Uncertainty Voting. In Proceedings of the IEEE Intelligent Vehicles Symposium, Baden-Baden, Germany, 5–9 June 2011; pp. 643–647.
17. Nistér, D. An efficient solution to the five-point relative pose problem. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26, 756–770.
18. Hartley R.; Zisserman, A. Multiple View Geometry in Computer Vision, 2nd ed.; Cambridge University Press: Cambridge, UK, 2000.
19. Hartley R.I. In Defense of the Eight-Point Algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 1997, 19, 580–593.
20. Stéwenius, C.; Engels, H.; Nistér, D. Recent developments on direct relative orientation. ISPRS J. Photogramm. Remote Sens. 2006, 60, 284–294.
21. Triggs B.; McLauchlan, P.F.; Hartley R.I.; Fitzgibbon, A.W. Bundle adjustment—A modern synthesis. In Vision Algorithms: Theory and Practice; Springer Heidelberg: Berlin, Germany, 2000; pp. 298–372.
22. Mitschke M. Dynamik der Kraftfahrzeuge: Band C: Fahrverhalten; Springer Heidelberg: Berlin, Germany, 1990. (in Germany)
23. Jiang Y.; Chen, H.; Xiong, G.; Gong, J.; Jiang Y. Kinematic constraints in visual odometry of intelligent vehicles. In Proceedings of the 2012 IEEE Intelligent Vehicles Symposium (IV), Alcala de Henares, Spain, 3–7 June 2012; pp. 1126–1131.
24. Geiger A. Kitti Vision Benchmark Suite. Available online: http://www.cvlibs.net/datasets/kitti/ (accessed on 22 May 2014).
25. Eigen. Available online: http://eigen.tuxfamily.org/index.php?title=Main_Page (accessed on 15 October 2013).
26. Oliensis J.; Genc, Y. New algorithms for two-frame structure from motion. In Proceedings of the IEEE International Conference on Computer Vision, Kerkyra, Greece, 20–27 September 1999; pp. 737–744.
27. Carsim. Available online: http://www.carsim.com/ (accessed on 11 March 2012).