Skip to main content
SpringerLink
Log in
Book cover

International Joint Conference on Computer Vision, Imaging and Computer Graphics

VISIGRAPP 2016: Computer Vision, Imaging and Computer Graphics Theory and Applications pp 475–492Cite as

Real-Time Visual Odometry by Patch Tracking Using GPU-Based Perspective Calibration

  • Conference paper
  • First Online:

  • 1092 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 693))

Abstract

In this paper we describe VOPT (Visual Odometry by Patch Tracking), a robust algorithm for visual odometry, which is able to operate with sparse or dense maps computed by simultaneous localization and mapping (SLAM) algorithms. By using an iterative multi-scale procedure, VOPT is able to estimate the individual motion, photometric correction and reliability tracking confidence of a set of planar patches. In order to overcome the high computational cost of the patch adjustment, we use a GPU-based least-square solver, achieving real-time performance. The algorithm can also be used as a building block to other procedures for automatic initialization and recovery of 3D scene. Our tests show that VOPT outperforms the well-known PTAMM and the state-of-art ORB-SLAM algorithm in challenging videos using the same input maps.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Alcantarilla, P.F., Nuevo, J., Bartoli, A.: Fast explicit diffusion for accelerated features in nonlinear scale spaces. In: British Machine Vision Conference (BMVC) (2013)

    Google Scholar 

  2. Bay, H., Tuytelaars, T., Van Gool, L.: Surf: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) Computer Vision - ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Berlin/Heidelberg (2006). doi:10.1007/11744023_32

    Chapter  Google Scholar 

  3. Birchfield, S.: Derivation of kanade-lucas-tomasi tracking equation, December 2014. https://www.ces.clemson.edu/~stb/klt/birchfield-klt-derivation.pdf

  4. Blender Online Community: Blender - a 3D modelling and rendering package. Amsterdam (2014). http://www.blender.org

  5. Castle, R., Klein, G., Murray, D.: Video-rate localization in multiple maps for wearable augmented reality. In: IEEE International Symposium on Wearable Computers (ISWC), pp. 15–22 (2008)

    Google Scholar 

  6. Concha, A., Civera, J.: Using superpixels in monocular SLAM. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 365–372 (2014)

    Google Scholar 

  7. Concha, A., Civera, J.: Dpptam: dense piecewise planar tracking and mapping from a monocular sequence. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5686–5693. IEEE (2015)

    Google Scholar 

  8. Engel, J., Sturm, J., Cremers, D.: Semi-dense visual odometry for a monocular camera. In: IEEE International Conference on Computer Vision (ICCV), pp. 1449–1456 (2013)

    Google Scholar 

  9. Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 834–849. Springer, Cham (2014). doi:10.1007/978-3-319-10605-2_54

    Google Scholar 

  10. Forster, C., Pizzoli, M., Scaramuzza, D.: SVO: fast semi-direct monocular visual odometry. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 15–22 (2014)

    Google Scholar 

  11. Hall, B.C.: Lie Groups, Lie Algebras, and Representations: An Elementary Introduction, vol. 222. Springer, Heidelberg (2015)

    Google Scholar 

  12. Han, T.D., Abdelrahman, T.S.: Reducing branch divergence in GPU programs. In: Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units, p. 3. ACM (2011)

    Google Scholar 

  13. Harris, M., et al.: Optimizing parallel reduction in CUDA. NVIDIA Develop. Technol. 2(4) (2007)

    Google Scholar 

  14. Klein, G.: Visual tracking for augmented reality. Ph.D. thesis, University of Cambridge (2006)

    Google Scholar 

  15. Klein, G., Murray, D.: Parallel tracking and mapping on a camera phone. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 83–86, October 2009

    Google Scholar 

  16. Lim, H., Lim, J., Kim, H.J.: Real-time 6-dof monocular visual slam in a large-scale environment. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 1532–1539. IEEE (2014)

    Google Scholar 

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

    Article  Google Scholar 

  18. Maxime, M., Comport, A., Rives, P.: Real-time dense visual tracking under large lighting variations. In: British Machine Vision Conference (BMVC), pp. 45.1–45.11. BMVA Press (2011)

    Google Scholar 

  19. Minetto, R., Leite, N., Stolfi, J.: AFFTrack: robust tracking of features in variable-zoom videos. In: IEEE International Conference on Image Processing (ICIP), pp. 4285–4288 (2009)

    Google Scholar 

  20. Mohr, P., Kerbl, B., Donoser, M., Schmalstieg, D., Kalkofen, D.: Retargeting technical documentation to augmented reality. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3337–3346. ACM (2015)

    Google Scholar 

  21. Mur-Artal, R., Montiel, J., Tardos, J.D.: Orb-slam: a versatile and accurate monocular slam system. IEEE Trans. Robot. 31(5), 1147–1163 (2015)

    Article  Google Scholar 

  22. NACODEAL: NACODEAL - Natural Communication Device for Assisted Living. European Union Project (2014). www.nacodeal.eu. Ambient Assisted Living Joint Programme ref. AAL-2010-3-116

  23. Newcombe, R., Izadi, S., Hillige, O., Molyneaux, D., Kim, D., Davison, A., Kohli, P., Shotton, J., Hodges, S., Fitzgibbon, A.: KinectFusion: real-time dense surface mapping and tracking. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 127–136 (2011)

    Google Scholar 

  24. Newcombe, R., Lovegrove, S., Davison, A.: DTAM: dense tracking and mapping in real-time. In: IEEE International Conference on Computer Vision (ICCV), pp. 2320–2327 (2011)

    Google Scholar 

  25. Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn. 2, 2161–2168 (2006)

    Google Scholar 

  26. Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1508–1515. IEEE (2005)

    Google Scholar 

  27. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: International Conference on Computer Vision, Barcelona (2011)

    Google Scholar 

  28. Ruijters, D., Romeny, B., Suetens, P.: Efficient GPU-based texture interpolation using uniform B-Splines. J. Graph. GPU Game Tools 13(4), 61–69 (2008). http://dx.doi.org/10.1080/2151237X.2008.10129269

  29. Saracchini, R.F.V., Ortega, C.C.: An easy to use mobile augmented reality platform for assisted living using pico-projectors. In: Chmielewski, L.J., Kozera, R., Shin, B.-S., Wojciechowski, K. (eds.) ICCVG 2014. LNCS, vol. 8671, pp. 552–561. Springer, Cham (2014). doi:10.1007/978-3-319-11331-9_66

    Google Scholar 

  30. Scaramuzza, D., Fraundorfer, F.: Visual odometry [tutorial]. Robot. Autom. Mag. IEEE 18(4), 80–92 (2011)

    Article  Google Scholar 

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

    Google Scholar 

  32. Straub, J., Hilsenbeck, S., Schroth, G., Huitl, R., Moller, A., Steinbach, E.: Fast relocalization for visual odometry using binary features. In: IEEE International Conference on Image Processing (ICIP), pp. 2548–2552 (2013)

    Google Scholar 

  33. Vacchetti, L., Lepetit, V., Fua, P.: Stable real-time 3D tracking using online and offline information. IEEE Trans. Pattern Anal. Mach. Intell. 26(10), 1385–1391 (2004). http://dx.doi.org/10.1109/TPAMI.2004.92

  34. Weiss, S., Achtelik, M., Lynen, S., Achtelik, M., Kneip, L., Chli, M., Siegwart, R.: Monocular vision for long-term micro aerial vehicle state estimation: a compendium. J. Field Robot. 30(5), 803–831 (2013). http://dx.doi.org/10.1002/rob.21466

  35. Whelan, T., Kaess, M., Fallon, M., Johannsson, H., Leonard, J., McDonald, J.: Kintinuous: spatially extended kinectfusion. Technical report MIT-CSAIL-TR-2012-020, MIT (2012)

    Google Scholar 

Download references

Acknowledgements

This work was funded by the Ambient Assisted Living Joint Programme as part of the project Natural Communication Device for Assisted Living, ref. AAL-2010-3-116 [22].

Author information

Authors and Affiliations

  1. Department of Simulation and Control, Technological Institute of Castilla y León, Burgos, Spain

    Rafael F. V. Saracchini & Carlos A. Catalina

  2. Department of Informatics, Federal University of Technology, Curitiba, Paraná, Brazil

    Rodrigo Minetto

  3. Institute of Computing, State University of Campinas, Campinas, Brazil

    Jorge Stolfi

Authors
  1. Rafael F. V. Saracchini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos A. Catalina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rodrigo Minetto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jorge Stolfi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael F. V. Saracchini .

Editor information

Editors and Affiliations

  1. Escola Superior de Tecnologia do IPS, Setúbal, Portugal

    José Braz

  2. MiraLab, University of Geneva, Carouge, Switzerland

    Nadia Magnenat-Thalmann

  3. LISA - ISTIA, University of Angers, Angers, France

    Paul Richard

  4. Department of Computer Science and Electrical Engineering, Jacobs University, Bremen, Germany

    Lars Linsen

  5. University of Groningen, Groningen, The Netherlands

    Alexandru Telea

  6. Università di Catania, Catania, Italy

    Sebastiano Battiato

  7. Research Innovation Center, Canon U.S.A. Inc., San Jose, California, USA

    Francisco Imai

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Saracchini, R.F.V., Catalina, C.A., Minetto, R., Stolfi, J. (2017). Real-Time Visual Odometry by Patch Tracking Using GPU-Based Perspective Calibration. In: Braz, J., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2016. Communications in Computer and Information Science, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-64870-5_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-64870-5_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64869-9

  • Online ISBN: 978-3-319-64870-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation