Skip to main content
SpringerLink
Log in

Accurate keyframe selection and keypoint tracking for robust visual odometry

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This paper presents a novel stereo visual odometry (VO) framework based on structure from motion, where a robust keypoint tracking and matching is combined with an effective keyframe selection strategy. In order to track and find correct feature correspondences a robust loop chain matching scheme on two consecutive stereo pairs is introduced. Keyframe selection is based on the proportion of features with high temporal disparity. This criterion relies on the observation that the error in the pose estimation propagates from the uncertainty of 3D points—higher for distant points, that have low 2D motion. Comparative results based on three VO datasets show that the proposed solution is remarkably effective and robust even for very long path lengths.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Notes

  1. https://drive.google.com/open?id=0B_3Nh0OK9BclM0I5VC1jNndTSTA.

References

  1. Badino, H., Yamamoto, A., Kanade, T.: Visual odometry by multi-frame feature integration. In: Proceedings of the International Workshop on Computer Vision for Autonomous Driving at ICCV (2013)

  2. Bellavia, F., Fanfani, M., Pazzaglia, F., Colombo, C.: Robust selective stereo SLAM without loop closure and bundle adjustment. In: Proceedings of 17th International Conference on Image Analysis and Processing, ICIAP 2013, pp. 462–471 (2013)

  3. Bellavia, F., Tegolo, D., Valenti, C.: Improving Harris corner selection strategy. IET Comput. Vis. 5(2), 87–96 (2011)

    Article  MathSciNet  Google Scholar 

  4. Bellavia, F., Tegolo, D., Valenti, C.: Keypoint descriptor matching with context-based orientation estimation. Image Vis. Comput. 32(9), 559–567 (2014)

    Article  Google Scholar 

  5. Cvisic, I., Petrovic, I.: Stereo odometry based on careful feature selection and tracking. In: Proceedings of the European Conference on Mobile Robots ECMR, pp. 1–6 (2015)

  6. Davison, A.: Real-time simultaneous localization and mapping with a single camera. In: Proceedings of the 9th IEEE International Conference on Computer Vision, pp. 1403–1410 (2003)

  7. Davison, A., Reid, I., Molton, N., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1052–1067 (2007)

    Article  Google Scholar 

  8. Fanfani, M., Bellavia, F., Pazzaglia, F., Colombo, C.: SAMSLAM: simulated annealing monocular SLAM. In: Proceedings of 15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013, pp. 515–522 (2013)

  9. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  10. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceedings of Computer Vision and Pattern Recognition. URL http://www.cvlibs.net/datasets/kitti/eval_odometry.php (2012)

  11. Geiger, A., Ziegler, J., Stiller, C.: StereoScan: Dense 3D reconstruction in real-time. In: IEEE Intelligent Vehicles Symposium (2011)

  12. Getreuer, P.: A survey of gaussian convolution algorithms. Image Process. On Line 3, 286–310 (2013)

    Article  Google Scholar 

  13. Hartley, R., Sturm, P.: Triangulation. Comput. Vis. Image Underst. 68(2), 146–157 (1997)

    Article  Google Scholar 

  14. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  15. Ho, K., Newman, P.: Detecting loop closure with scene sequences. Int. J. Comput. Vis. 74(3), 261–286 (2007)

    Article  Google Scholar 

  16. Horn, B.K.P.: Closed-form solution of absolute orientation using unit quaternions. J. Opt. Soc. Am. A 4(4), 629–642 (1987)

    Article  Google Scholar 

  17. Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proceedings of the IEEE/ACM International Symposium on Mixed and Augmented Reality, pp. 225–234 (2007)

  18. Klein, G., Murray, D.: Improving the agility of keyframe-based SLAM. In: Proceedings of the 10th European Conference on Computer Vision, pp. 802–815 (2008)

  19. Lee, G.H., Fraundorfer, F., Pollefeys, M.: RS-SLAM: RANSAC sampling for visual FastSLAM. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1655–1660 (2011)

  20. Lim, J., Pollefeys, M., Frahm, J.M.: Online environment mapping. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2011)

  21. Martull, S., Martorell, M.P., Fukui, K.: Realistic CG Stereo Image Dataset with Ground Truth Disparity Maps. In: Proceedings of the ICPR2012 workshop TrakMark2012, pp. 40–42. URL http://www.cvlab.cs.tsukuba.ac.jp/dataset/tsukubastereo.php (2012)

  22. Mei, C., Sibley, G., Cummins, M., Newman, P., Reid, I.: RSLAM: a system for large-scale mapping in constant-time using stereo. Int. J. Comput. Vis. 94, 198–214 (2011)

    Article  Google Scholar 

  23. Montiel, J., Civera, J., Davison, A.: Unified inverse depth parametrization for monocular SLAM. In: Proceedings of Robotics: Science and Systems. IEEE Press (2006)

  24. Mouragnon, E., Lhuillier, M., Dhome, M., Dekeyser, F., Sayd, P.: Real time localization and 3D reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 363–370 (2006)

  25. Newcombe, R., Lovegrove, S., Davison, A.: DTAM: Dense tracking and mapping in real-time. In: Proceedings of the 13th International Conference on Computer Vision (2011)

  26. Nistér, D., Naroditsky, O., Bergen, J.R.: Visual odometry. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–659 (2004)

  27. Paz, L., Piniés, P., Tardós, J., Neira, J.: Large-scale 6-DoF SLAM with stereo-in-hand. IEEE Trans. Robot. 24(5), 946–957 (2008)

    Article  Google Scholar 

  28. Persson, M., Piccini, T., Felsberg, M., Mester, R.: Robust stereo visual odometry from monocular techniques. In: Proceedings of the IEEE Intelligent Vehicles Symposium IV2015, pp. 686–691 (2015)

  29. Pretto, A., Menegatti, E., Bennewitz, M., Burgard, W.: A visual odometry framework robust to motion blur. In: Proceedings of the IEEE International Conference on Robotics and Automation (2009)

  30. Scaramuzza, D., Fraundorfer, F.: Visual odometry: part I: the first 30 years and fundamentals. IEEE Robot. Autom. Mag. 18(4), 80–92 (2011)

    Article  Google Scholar 

  31. Smith, M., Baldwin, I., Churchill, W., Paul, R., Newman, P.: The new college vision and laser data set. Int. J. Robot. Res 28(5), 595–599. URL http://www.robots.ox.ac.uk/NewCollegeData/ (2009)

  32. Strasdat, H., Davison, A.J., Montiel, J.M.M., Konolige, K.: Double window optimisation for constant time visual SLAM. In: Proceedings of the International Conference on Computer Vision, pp. 2352–2359 (2011)

  33. Strasdat, H., Montiel, J., Davison, A.: Visual SLAM: why filter? Image Vis. Comput. 30, 65–77 (2012)

    Article  Google Scholar 

  34. Strasdat, H., Montiel, J.M.M., Davison, A.J.: Scale drift-aware large scale monocular SLAM. In: Proceedings of Robotics: Science and Systems (2010)

  35. Triggs, B., McLauchlan, P., Hartley, R., Fitzgibbon, A.: Bundle adjustment - a modern synthesis. In: Proceedings of the International Workshop on Vision Algorithms: Theory and Practice, pp. 298–372 (2000)

Download references

Acknowledgments

This work was supported by the SUONO project (Safe Underwater Operations iN Oceans), SCN_00306, ranked first in the challenge on “Sea Technologies” of the competitive call named “Smart Cities and Communities” issued by the Italian Ministry of Education and Research.

Author information

Authors and Affiliations

  1. Computational Vision Group (CVG), University of Florence, Via S. Marta, 3, Florence, Italy

    Marco Fanfani, Fabio Bellavia & Carlo Colombo

Authors
  1. Marco Fanfani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fabio Bellavia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carlo Colombo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Fanfani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fanfani, M., Bellavia, F. & Colombo, C. Accurate keyframe selection and keypoint tracking for robust visual odometry. Machine Vision and Applications 27, 833–844 (2016). https://doi.org/10.1007/s00138-016-0793-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-016-0793-3

Keywords

Search

Navigation