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Vision-based robot localization based on the efficient matching of planar features

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Abstract

Real-time accurate localization is a key component of any autonomous mobile robot. Visual localization algorithms usually rely on feature matching between the current view and a map using point descriptors. Many descriptors such as SIFT or SURF are designed to recognize features seen from different viewpoints, but in robotics context, robot movement can be modeled to bring useful information for the matching problem. Here we detail a feature-matching solution using a local 3D model of the features that exploits the motion model of the robot. We compare our method against the SIFT descriptor in a simple matching experiment. The method is then combined with prediction models to achieve autonomous navigation of a mobile robot. Experiments showed that localization remains possible despite severe viewpoint change.

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References

  1. Araujo, H., Carceroni, R.L., Brown, C.M.: A fully projective formulation to improve the accuracy of Lowe’s pose-estimation algorithm. Comp Vision Image Underst 70(2), 227–238 (1998)

    Article  Google Scholar 

  2. Baker, S., Matthews, I.: Lucas-kanade 20 years on: a unifying framework. Int J Comp Vision 56(3), 221–255 (2004)

    Article  Google Scholar 

  3. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comp Vision Image Underst 110(3), 346–359 (2008)

    Article  Google Scholar 

  4. Berger, C., Lacroix, S.: Using planar facets for stereovision SLAM. In: International Conference on Intelligent Robots and Systems, pp 1606–1611 (2008)

  5. Charmette, B., Royer, E., Chausse, F.: Matching Planar Features for Robot Localization. In: International Symposium on Visual Computing, Springer, p 201 (2009)

  6. Charmette, B., Royer, E., Chausse, F.: Efficient planar features matching for robot localization using gpu. In: Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, pp 16–23 (2010)

  7. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey vision conference (1988)

  8. Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. 2 edn, Cambridge University Press (2004) (ISBN: 0521540518)

  9. Kalman, R.E.: A new approach to linear filtering and prediction problems. J Basic Eng 82(Series D):35–45 (1960)

  10. Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: International Symposium on Mixed and Augmented Reality (2007)

  11. Koser, K., Koch, R.: Perspectively invariant normal features. In: International Conference on Computer Vision, pp 1–8 (2007)

  12. Lenain, R., Thuilot, B., Cariou, C., Martinet, P.: Model predictive control for vehicle guidance in presence of sliding: application to farm vehicles path tracking. In: International Conference on Robotics and Automation, IEEE, pp 885–890 (2005)

  13. Lepetit, V., Fua, P.: Keypoint recognition using random forests and random ferns. In: Decision Forests for Computer Vision and Medical Image Analysis, Springer, pp 111–124 (2013)

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

    Article  Google Scholar 

  15. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: International Conference on Computer Vision and Pattern Recognition (2003)

  16. Molton, N., Davison, A., Reid, I.: Locally planar patch features for real-time structure from motion. In: British Machine Vision Conference (2004)

  17. Morel, J.M., Asift, GYu.: A new framework for fully affine invariant image comparison. SIAM J Imaging Sci 2(2), 438–469 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  18. Pietzsch, T.: Planar features for visual slam. KI 2008: Advances in Artificial Intelligence, pp 119–126 (2008)

  19. Royer, E., Lhuillier, M., Dhome, M., Lavest, J.M.: Monocular vision for mobile robot localization and autonomous navigation. Int J Comp Vision 74, 237–260 (2007)

    Article  MATH  Google Scholar 

  20. Tola, E., Lepetit, V., Fua, P.: A fast local descriptor for dense matching. In: International Conference on Computer Vision and Pattern Recognition, pp 1–8 (2008)

  21. Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle adjustment-a modern synthesis. Lecture Notes in Computer Science, pp 298–372 (1999)

  22. Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Found Trends Comp Graph Vision 3(3), 177–280 (2008)

  23. Williams, B., Klein, G., Reid, I.: Real-time SLAM relocalisation. In: International Conference on Computer Vision, pp 1–8 (2007)

  24. Wu, C., Clipp, B., Li, X., Frahm, J.M., Pollefeys, M.: 3D model matching with Viewpoint-Invariant Patches (VIP). In; International Conference on Computer Vision and Pattern Recognition, pp 1–8 (2008)

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Acknowledgments

This work was financed by the ANR (French National Research Agency) under the cityVIP project framework.

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Authors and Affiliations

  1. Institut Pascal, Clermont Université, Université d’Auvergne, BP 10448, Clermont-Ferrand, 63000, France

    Baptiste Charmette, Eric Royer & Frédéric Chausse

  2. CNRS, UMR 6602, IP, Aubiere, 63171, France

    Baptiste Charmette, Eric Royer & Frédéric Chausse

Authors
  1. Baptiste Charmette
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  2. Eric Royer
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  3. Frédéric Chausse
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Correspondence to Frédéric Chausse.

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Charmette, B., Royer, E. & Chausse, F. Vision-based robot localization based on the efficient matching of planar features. Machine Vision and Applications 27, 415–436 (2016). https://doi.org/10.1007/s00138-016-0759-5

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  • DOI: https://doi.org/10.1007/s00138-016-0759-5

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