Regularised Energy Model for Robust Monocular Ego-motion Estimation

Hsiang-Jen Chien, Reinhard Klette

2017

Abstract

For two decades, ego-motion estimation is an actively developing topic in computer vision and robotics. The principle of existing motion estimation techniques relies on the minimisation of an energy function based on re-projection errors. In this paper we augment such an energy function by introducing an epipolar-geometry-derived regularisation term. The experiments prove that, by taking soft constraints into account, a more reliable motion estimation is achieved. It also shows that the implementation presented in this paper is able to achieve a remarkable accuracy comparative to the stereo vision approaches, with an overall drift maintained under 2% over hundreds of metres.

References

  1. Badino, H., Yamamoto, A., Kanade, T.: Visual odometry by multi-frame feature integration. Int. ICCV Workshop Computer Vision Autonomous Driving (2013)
  2. Engels, C., Stewenius, H., Nister, D.: Bundle adjustment rules. In Proc. Photogrammetric Computer Vision (2006)
  3. Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM, vol. 24, no. 6, pp. 381-395 (1981)
  4. Forster, C., Pizzoli, M., Scaramuzza, D.: SVO: Fast semidirect monocular visual odometry. In: Proc. IEEE Int. Conf. Robotics Automation, pp. 15-22 (2014)
  5. Geiger, A., Lenz, P., Stiller, C., and Urtasun, R.: Vision meets robotics: The KITTI dataset. Int. J. Robotics Research, vol. 32, no. 11, pp. 1231-1237 (2013)
  6. Geng, H., Chien, H.-J., Nicolescu, R., Klette, R.: Egomotion estimation and reconstruction with Kalman filters and GPS integration. In: Proc. Computer Analysis of Images and Patterns, vol. 9256, pp. 399-410 (2015)
  7. Hartley, R. I., Zisserman, A. : Multiple View Geometry in Computer Vision, second edition. Cambridge University Press, Cambridge (2004)
  8. Hu, G., Huang, S., Zhao, L., Alempijevic A., Dissanayake, G.: A robust RGB-D SLAM algorithm. In Proc.: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 1714-1719 (2012)
  9. Klette, R.: Concise Computer Vision. Springer, London (2014)
  10. Konolige, K., Agrawal, M.: FrameSLAM: From bundle adjustment to real-time visual mapping. IEEE Trans. Robotics, vol. 5, no. 24, pp. 1066-1077 (2008)
  11. Lepetit, V, Moreno-Noguer, F., Fua. P.: EPnP: An accurate O(n) solution to the PnP problem. Int. J. Computer Vision, vol. 81, pp. 155-166 (2009)
  12. Levenberg, K.A.: Method for the solution of certain nonlinear problems in least squares. The Quarterly Applied Math., vol. 2, pp. 164-168 (1944)
  13. Morales, S., Klette, R.: Kalman-filter based spatio-temporal disparity integration. Pattern Recognition Letters, vol. 34, no. 8, pp. 873-883 (2013)
  14. Sampson, P.D.: Fitting conic sections to 'very scattered' data: An iterative refinement of the Bookstein algorithm. Computer Graphics Image Processing, vol. 18, no. 1, pp. 97-108 (1982)
  15. Scaramuzza, D., Fraundorfer, F.: Visual odometry: Part I - The first 30 years and fundamentals. IEEE Robotics Automation Magazine, vol. 18, pp. 80-92 (2011)
  16. Tomasi, C., Kanade, T.: Detection and tracking of point features. Carnegie Mellon University Technical Report, CMU-CS-91-132 (1991)
  17. Vaudrey, T., Badino, H., Gehrig, S.: Integrating disparity images by incorporating disparity rate. In. Proc. Robot Vision, LNCS 4931, pp. 29-42 (2008)
  18. Wu, F.C., Zhang, Q., Hu, Z.Y.: Efficient suboptimal solutions to the optimal triangulation. Int. J. Computer Vision, vol. 91, no. 1, pp. 77-106 (2011)
  19. Zhengyou, Z.: Determining the epipolar geometry and its uncertainty: A review. Int. J. Computer Vision, vol. 2, no. 27, pp. 161-198 (1998)
Download


Paper Citation


in Harvard Style

Chien H. and Klette R. (2017). Regularised Energy Model for Robust Monocular Ego-motion Estimation . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 361-368. DOI: 10.5220/0006100303610368


in Bibtex Style

@conference{visapp17,
author={Hsiang-Jen Chien and Reinhard Klette},
title={Regularised Energy Model for Robust Monocular Ego-motion Estimation},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={361-368},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006100303610368},
isbn={978-989-758-227-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Regularised Energy Model for Robust Monocular Ego-motion Estimation
SN - 978-989-758-227-1
AU - Chien H.
AU - Klette R.
PY - 2017
SP - 361
EP - 368
DO - 10.5220/0006100303610368