Skip to main content
SpringerLink
Log in

Feature selection for reliable data association in visual SLAM

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

Abstract

A method for feature selection in visual simultaneous localization and mapping (SLAM) is presented based on the potential data association cost. It is put into practice through a mechanism termed predictive virtual matching test, which measures the goodness of any new feature by examining the predictive repeatability and compatibility with the other features and potential candidates. For each new feature, the test is conducted in its predictive virtual search region (PVSR) in the image frame where the feature is initially detected. The relationship between PVSR and the predicted search window determined by the next time step’s innovation covariance matrix is analyzed theoretically through backward inference. Since the process of feature selection is directly guided by the requirement of subsequent data association, it can automatically adapt to the time-varying uncertainty underlying the SLAM state estimate. Experiment results show that the feature selection mechanism effectively improves the reliability of data association by preventing bad features from being initialized, and consequently the consistency of SLAM estimate is better ensured.

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

Similar content being viewed by others

References

  1. Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proceedings of International Symposium on Mixed and Augmented Reality (ISMAR’07) (2007)

  2. Nistér D., Naroditsky D., Bergen J.: Visual odometry for ground vehicle applications. J. Field Robot. 23(1), 3–20 (2006)

    Article  MATH  Google Scholar 

  3. Mayol, W.W., Davison, A.J., Tordoff, B.J., Molton, N.D., Murray, D.W.: Interaction between hand and wearable camera in 2D and 3D environments. In: Proceedings of British Machine Vision Conference (BMVC’04) (2004)

  4. 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 

  5. Walter M.R., Eustice R.M., Leonard J.J.: Exactly sparse extended information filters for feature-based SLAM. Int. J. Robot. Res. 26(4), 335–359 (2007)

    Article  Google Scholar 

  6. Eade, E., Drummond, T.: Scalable monocular SLAM. In: Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR’06) (2006)

  7. Civera J., Davison A., Montiel M.: Inverse depth parametrization for monocular SLAM. IEEE Trans. Robot. 24(5), 932–945 (2008)

    Article  Google Scholar 

  8. Chekhlov, D., Pupilli, M., Mayol, W., Calway, A.: Robust real-time visual SLAM using scale prediction and exemplar based feature description. In: Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR’07) (2007)

  9. Chekhlov, D., Pupilli, M., Mayol, W., Calway, A.: Real-time and robust monocular SLAM using predictive multi-resolution descriptors. In: Proceedings of 2nd International Symposium on Visual Computing (2006)

  10. Munguia, R., Grau, A.: Camera localization and mapping using delayed feature initialization and inverse depth parametrization. In: Proceedings of IEEE Conference on Emerging Technologies and Factory Automation (ETFA’07), pp. 981–988 (2007)

  11. Eade, E., Drummond, T.W.: Edge landmarks in monocular SLAM. In: Proceedings of 17th British Machine Vision Conference (BMVC’06) (2006)

  12. Smith, P., Reid, I., Davison, A.: Real-time monocular SLAM with straight lines. In: Proceedings of 17th British Machine Vision Conference (BMVC’06), vol. 1, pp. 17–26 (2006)

  13. Molton, N., Reid, I., Davison, A.: Locally planar patch features for real-time structure from motion. In: Proceedings of 15th British Machine Vision Conference (BMVC’04) (2004)

  14. Shi, J., Tomasi, C.: Good feature to track. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’94), pp. 593–600 (1994)

  15. Lowe D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  16. Neira J., Tardos J.D.: Data association in stochastic mapping using the joint compatibility test. IEEE Trans. Robot. Autom. 17(6), 890–897 (2001)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  18. Nistér D.: Preemptive RANSAC for live structure and motion estimation. Mach. Vis. Appl. 16(5), 321–329 (2005)

    Article  Google Scholar 

  19. Lemaire T., Berger C., Jung I.-K., Lacroix S.: Vision-based SLAM: stereo and monocular approaches. Int. J. Comput. Vis. 74(3), 343–364 (2007)

    Article  Google Scholar 

  20. Mikolajczyk K., Tuytelaars T., Schmid C., Zisserman A., Matas J., Schaffalitzky F., Kadir T., Van Gool L.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1/2), 43–72 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tsinghua National Laboratory of Information Science and Technology, Department of Automation, Tsinghua University, Beijing, 100084, People’s Republic of China

    Zongying Shi, Zhibin Liu, Xianliang Wu & Wenli Xu

Authors
  1. Zongying Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhibin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xianliang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wenli Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zongying Shi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shi, Z., Liu, Z., Wu, X. et al. Feature selection for reliable data association in visual SLAM. Machine Vision and Applications 24, 667–682 (2013). https://doi.org/10.1007/s00138-012-0440-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-012-0440-6

Keywords

Search

Navigation