Pacific-Rim Symposium on Image and Video Technology

Image and Video Technology pp 27-37 | Cite as

Multi-frame Feature Integration for Multi-camera Visual Odometry

  • Hsiang-Jen Chien
  • Haokun Geng
  • Chia-Yen Chen
  • Reinhard Klette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9431)


State-of-the-art ego-motion estimation approaches in the context of visual odometry (VO) rely either on Kalman filters or bundle adjustment. Recently proposed multi-frame feature integration (MFI [1]) techniques aim at finding a compromise between accuracy and computation efficiency. In this paper we generalise an MFI algorithm towards the full use of multi-camera-based visual odometry for achieving more consistent ego-motion estimation in a parallel scalable manner. A series of experiments indicated that the generalised integration technique contributes to an improvement of above 70 % over our direct VO implementation, and further improved the monocular MFI technique by more than 20 %.


Visual odometry Ego-motion estimation Feature tracking 


  1. 1.
    Badino, H., Yamamoto, A., Kanade, T.: Visual odometry by multi-frame feature integration. In: International Workshop on Computer Vision Autonomous Driving (ICCV) (2013)Google Scholar
  2. 2.
    Geiger, A., Ziegler, J., Stiller, C.: StereoScan: dense 3D reconstruction in real-time. In: Intelligent Vehicles Symposium (IV) (2011)Google Scholar
  3. 3.
    Bay, H., Van Gool, L., Tuytelaars, T.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Choi, S., Kim, T., Yu W.: Performance evaluation of RANSAC family. In: Proceedings of the British Machine Vision Conference (2009)Google Scholar
  5. 5.
    Engels, C., Stewenius, H., Nister, D.: Bundle adjustment rules. In: Proceedings of Photogrammetric Computer Vision (2006)Google Scholar
  6. 6.
    Forster, C., Pizzoli, M., Scaramuzza, D.: SVO: Fast semi-direct monocular visual odometry. In: Proceedings of the IEEE International Conference on Robotics Automation, pp. 15–22 (2014)Google Scholar
  7. 7.
    Fraundorfer, F., Scaramuzza, D.: Visual odometry: part II - matching, robustness, and applications. IEEE Robot. Autom. Mag. 19, 78–90 (2012)CrossRefGoogle Scholar
  8. 8.
    Geiger A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceedings of the Conference on Computer Vision Pattern Recognition (2012)Google Scholar
  9. 9.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)CrossRefMATHGoogle Scholar
  10. 10.
    Hirschmüller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. In: Proceedings of the Conference Computer Vision Pattern Recognition, vol. 2, pp. 807–814 (2005)Google Scholar
  11. 11.
    Jiang, R., Wang, S., Klette, R.: Statistical modeling of long-range drift in visual odometry. In: Koch, R., Huang, F. (eds.) ACCV 2010 Workshops, Part II. LNCS, vol. 6469, pp. 214–224. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Kitt, B., Geiger, A., Lategahn, H.: Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme. In: Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 486–492 (2010)Google Scholar
  13. 13.
    Klette, R.: Concise Computer Vision. Springer, London (2014)CrossRefMATHGoogle Scholar
  14. 14.
    Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: an accurate O(n) solution to the PnP problem. Int. J. Comput. Vis. 81, 155–166 (2009)CrossRefGoogle Scholar
  15. 15.
    Scaramuzza, D., Fraundorfer, F.: Visual odometry: part I - the first 30 years and fundamentals. IEEE Robot. Autom. Mag. 18, 80–92 (2011)CrossRefGoogle Scholar
  16. 16.
    Zhang, Z., Shan Y.: Incremental motion estimation through local bundle adjustment. Technical report MSR-TR-01-54, Microsoft (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hsiang-Jen Chien
    • 1
  • Haokun Geng
    • 2
  • Chia-Yen Chen
    • 3
  • Reinhard Klette
    • 1
  1. 1.School of EngineeringAuckland University of TechnologyAucklandNew Zealand
  2. 2.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand
  3. 3.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungTaiwan

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