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A Comparison of Feature Detectors and Descriptors in RGB-D SLAM Methods

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Image Analysis and Recognition (ICIAR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9164))

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Abstract

In RGB-D based SLAM methods, robot motion is generally computed by detecting and matching feature points in image frames obtained from an RGB-D sensor. Thus, feature detectors and descriptors used in a SLAM method significantly affect the performance. In this work, impacts of feature detectors and descriptors on the performance of an RGB-D based SLAM method are studied. SIFT, SURF, BRISK, ORB, FAST, GFTT, STAR feature detectors and SIFT, SURF, BRISK, ORB, BRIEF, FREAK feature descriptors are evaluated in terms of accuracy and speed.

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References

  1. Thrun, S., Burgard, W., Fox, D.: A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping. IEEE Int. Conf. Robot. Autom. (ICRA) 1, 321–328 (2000)

    Google Scholar 

  2. Triebel, R., Burgard, W.: Improving simultaneous mapping and localization in 3D using global constraints. Natl. Conf. Artif. Intell. 3, 1330–1335 (2005)

    Google Scholar 

  3. Kang, J.G., An, S.Y., Kim, S., Oh, S.Y.: Sonar based simultaneous localization and mapping using a neuro evolutionary optimization. In: International Joint Conference on Neural Networks, pp. 1516–1523 (2009)

    Google Scholar 

  4. Konolige, K., Agrawal, M.: FrameSLAM: from bundle adjustment to real-time visual mapping. IEEE Trans. Robot. 24(5), 1066–1077 (2008)

    Article  Google Scholar 

  5. Clemente, L., Davison, A., Reid, I., Neira, J., Tardos, J.: Mapping large loops with a single hand-held camera. Robotics: Science and Systems (RSS) (2007)

    Google Scholar 

  6. Davison, A.: Real-time simultaneous localisation and mapping with a single camera. IEEE Int. Conf. Comput. Vis. 2, 1403–1410 (2003)

    Google Scholar 

  7. Endres, F., Hess, J., Sturm, J., Cremers, D., Burgard, W.: 3-D mapping with an RGB-D camera. IEEE Trans. Robot. 30(1), 177–187 (2014)

    Article  Google Scholar 

  8. Lowe, D.: Discriminative image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  9. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  10. Leutenegger, S., Chli, M., Siegwart, R.: BRISK: binary robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision, pp. 2548–2555 (2011)

    Google Scholar 

  11. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision, pp. 2564–2571 (2011)

    Google Scholar 

  12. Rosten, E., Drummond, T.W.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  13. Shi, J., Tomasi, C.: Good features to track. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)

    Google Scholar 

  14. Agrawal, M., Konolige, K., Blas, M.R.: CenSurE: center surround extremas for realtime feature detection and matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 102–115. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  15. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  16. Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: fast retina keypoint. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 510–517 (2012)

    Google Scholar 

  17. Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: using depth cameras for dense 3D modeling of indoor environments. In: 12th International Symposium on Experimental Robotics (ISER) (2010)

    Google Scholar 

  18. 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 

  19. Segal, A., Haehnel, D., Thrun, S.: Generalized-ICP. Robotics: Science and Systems (RSS) (2009)

    Google Scholar 

  20. Grisetti, G., Grzonka, S., Stachniss, C., Pfaff, P., Burgard, W.: Efficient estimation of accurate maximum likelihood maps in 3D. In: IEEE International Conference on Intelligent Robots and Systems, pp. 3472–3478 (2007)

    Google Scholar 

  21. Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: using kinect-style depth cameras for dense 3D modeling of indoor environments. Int. J. Robot. Res. 31(5), 647–663 (2012)

    Article  Google Scholar 

  22. Calonder, M., Lepetit, V., Fua, P.: Keypoint signatures for fast learning and recognition. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 58–71. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  23. Konolige, K.: Sparse sparse bundle adjustment. In: British Machine Vision Conference (BMVC) (2010)

    Google Scholar 

  24. Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard, W.: An evaluation of the RGB-D SLAM system. In: IEEE International Conference on Robotics and Automation, pp. 1691–1696 (2012)

    Google Scholar 

  25. Kummerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: g2o: a general framework for graph optimization. In: IEEE International Conference on Robotics and Automation (2011)

    Google Scholar 

  26. Fioraio, N., Konolige, K.: Realtime visual and point cloud slam. In: RGB-D Workshop on Advanced Reasoning with Depth Cameras at Robotics: Science and Systems (RSS) (2011)

    Google Scholar 

  27. Grisetti, G., Kummerle, R., Stachniss, C., Burgard, W.: A tutorial on graph-based SLAM. IEEE Intell. Transp. Syst. Mag. 2(4), 31–43 (2010)

    Article  Google Scholar 

  28. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference (1988)

    Google Scholar 

  29. Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D slam systems. In: IEEE International Conference on Intelligent Robots and Systems, pp. 573–580 (2012)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Hacettepe University, Ankara, Turkey

    Oguzhan Guclu & Ahmet Burak Can

Authors
  1. Oguzhan Guclu
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  2. Ahmet Burak Can
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Correspondence to Oguzhan Guclu .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

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Guclu, O., Can, A.B. (2015). A Comparison of Feature Detectors and Descriptors in RGB-D SLAM Methods. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_32

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  • DOI: https://doi.org/10.1007/978-3-319-20801-5_32

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