Skip to main content
SpringerLink
Log in

Divide and Conquer: Efficient Density-Based Tracking of 3D Sensors in Manhattan Worlds

  • Conference paper
  • First Online:
Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10115))

Included in the following conference series:

Abstract

3D depth sensors such as LIDARs and RGB-D cameras have become a popular choice for indoor localization and mapping. However, due to the lack of direct frame-to-frame correspondences, the tracking traditionally relies on the iterative closest point technique which does not scale well with the number of points. In this paper, we build on top of more recent and efficient density distribution alignment methods, and notably push the idea towards a highly efficient and reliable solution for full 6DoF motion estimation with only depth information. We propose a divide-and-conquer technique during which the estimation of the rotation and the three degrees of freedom of the translation are all decoupled from one another. The rotation is estimated absolutely and drift-free by exploiting the orthogonal structure in man-made environments. The underlying algorithm is an efficient extension of the mean-shift paradigm to manifold-constrained multiple-mode tracking. Dedicated projections subsequently enable the estimation of the translation through three simple 1D density alignment steps that can be executed in parallel. An extensive evaluation on both simulated and publicly available real datasets comparing several existing methods demonstrates outstanding performance at low computational cost.

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

Access this chapter

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

Institutional subscriptions

References

  1. Bachrach, A., Prentice, S., He, R., Henry, P., Huang, A.S., Krainin, M., Maturana, D., Fox, D., Roy, N.: Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments. Int. J. Rob. Res. 31, 1320–1343 (2012)

    Article  Google Scholar 

  2. Bohren, J., Rusu, R.B., Jones, E.G., Marder-Eppstein, E., Pantofaru, C., Wise, M., Mösenlechner, L., Meeussen, W., Holzer, S.: Towards autonomous robotic butlers: lessons learned with the PR2. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 5568–5575. IEEE (2011)

    Google Scholar 

  3. Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. In: Robotics-DL Tentative, International Society for Optics and Photonics, pp. 586–606 (1992)

    Google Scholar 

  4. Pomerleau, F., Magnenat, S., Colas, F., Liu, M., Siegwart, R.: Tracking a depth camera: parameter exploration for fast ICP. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3824–3829. IEEE (2011)

    Google Scholar 

  5. Pomerleau, F., Colas, F., Siegwart, R., Magnenat, S.: Comparing ICP variants on real-world data sets. Auton. Rob. 34, 133–148 (2013)

    Article  Google Scholar 

  6. Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohi, P., Shotton, J., Hodges, S., Fitzgibbon, A.: KinectFusion: real-time dense surface mapping and tracking. In: 2011 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 127–136. IEEE (2011)

    Google Scholar 

  7. Whelan, T., Kaess, M., Fallon, M., Johannsson, H., Leonard, J., McDonald, J.: Kintinuous: spatially extended KinectFusion. In: RSS Workshop on RGB-D: Advanced Reasoning with Depth Cameras, Sydney, Australia (2012)

    Google Scholar 

  8. Jian, B., Vemuri, B.C.: Robust point set registration using gaussian mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1633–1645 (2011)

    Article  Google Scholar 

  9. Chui, H., Rangarajan, A.: A new algorithm for non-rigid point matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 44–51. IEEE (2000)

    Google Scholar 

  10. Chui, H., Rangarajan, A.: A feature registration framework using mixture models. In: Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 190–197. IEEE (2000)

    Google Scholar 

  11. Tsin, Y., Kanade, T.: A correlation-based approach to robust point set registration. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3023, pp. 558–569. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24672-5_44

    Chapter  Google Scholar 

  12. Fitzgibbon, A.W.: Robust registration of 2D and 3D point sets. Image Vis. Comput. 21, 1145–1153 (2003)

    Article  Google Scholar 

  13. Yang, J., Li, H., Jia, Y.: Go-ICP: solving 3D registration efficiently and globally optimally. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1457–1464. IEEE (2013)

    Google Scholar 

  14. Kerl, C., Sturm, J., Cremers, D.: Robust odometry estimation for RGB-D cameras. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 3748–3754. IEEE (2013)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  17. 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, Heidelberg (2014). doi:10.1007/978-3-319-10605-2_54

    Google Scholar 

  18. Schöps, T., Engel, J., Cremers, D.: Semi-dense visual odometry for ar on a smartphone. In: 2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 145–150. IEEE (2014)

    Google Scholar 

  19. Kneip, L., Zhou, Y., Li, H.: SDICP: semi-dense tracking based on iterative closest points. In: Xianghua Xie, M.W.J., Tam, G.K.L. (eds.) Proceedings of the British Machine Vision Conference (BMVC), pp. 100.1–100.12. BMVA Press, Guildford (2015)

    Google Scholar 

  20. Weingarten, J., Siegwart, R.: 3D slam using planar segments. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3062–3067. IEEE (2006)

    Google Scholar 

  21. Trevor, A.J., Rogers III, J.G., Christensen, H., et al.: Planar surface slam with 3D and 2D sensors. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 3041–3048. IEEE (2012)

    Google Scholar 

  22. Taguchi, Y., Jian, Y.D., Ramalingam, S., Feng, C.: Point-plane slam for hand-held 3D sensors. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 5182–5189. IEEE (2013)

    Google Scholar 

  23. Coughlan, J.M., Yuille, A.L.: Manhattan world: compass direction from a single image by Bayesian inference. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 941–947. IEEE (1999)

    Google Scholar 

  24. Košecká, J., Zhang, W.: Video compass. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 476–490. Springer, Heidelberg (2002). doi:10.1007/3-540-47979-1_32

    Chapter  Google Scholar 

  25. Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33715-4_54

    Chapter  Google Scholar 

  26. Straub, J., Rosman, G., Freifeld, O., Leonard, J.J., Fisher, J.W.: A mixture of manhattan frames: beyond the manhattan world. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3770–3777. IEEE (2014)

    Google Scholar 

  27. Straub, J., Bhandari, N., Leonard, J.J., Fisher III, J.W.: Real-time manhattan world rotation estimation in 3D. In: IROS (2015)

    Google Scholar 

  28. Fukunaga, K., Hostetler, L.D.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory 21, 32–40 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  29. Holz, D., Holzer, S., Rusu, R.B., Behnke, S.: Real-time plane segmentation using RGB-D cameras. In: Röfer, T., Mayer, N.M., Savage, J., Saranlı, U. (eds.) RoboCup 2011. LNCS (LNAI), vol. 7416, pp. 306–317. Springer, Heidelberg (2012). doi:10.1007/978-3-642-32060-6_26

    Chapter  Google Scholar 

  30. He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1397–1409 (2013)

    Article  Google Scholar 

  31. Carreira-Perpiñán, M.: A review of mean-shift algorithms for clustering. arXiv preprint (2015). arXiv:1503.00687

  32. Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D slam systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012)

    Google Scholar 

  33. Handa, A., Whelan, T., McDonald, J., Davison, A.J.: A benchmark for RGB-D visual odometry, 3D reconstruction and slam. In: IEEE International Conference on Robotics and Automation (ICRA) (2014)

    Google Scholar 

  34. Lu, Y., Song, D.: Robustness to lighting variations: an RGB-D indoor visual odometry using line segments. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 688–694. IEEE (2015)

    Google Scholar 

Download references

Acknowledgement

The research leading to these results is supported by Australian Centre for Robotic Vision. The work is furthermore supported by ARC grants DE150101365. Yi Zhou acknowledges the financial support from the China Scholarship Council for his Ph.D. Scholarship No. 201406020098.

Author information

Authors and Affiliations

  1. Research School of Engineering, The Australian National University, Canberra, Australia

    Yi Zhou, Laurent Kneip, Cristian Rodriguez & Hongdong Li

  2. Australian Centre for Robotic Vision, Canberra, Australia

    Yi Zhou, Laurent Kneip, Cristian Rodriguez & Hongdong Li

Authors
  1. Yi Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laurent Kneip
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cristian Rodriguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongdong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi Zhou .

Editor information

Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 326 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Zhou, Y., Kneip, L., Rodriguez, C., Li, H. (2017). Divide and Conquer: Efficient Density-Based Tracking of 3D Sensors in Manhattan Worlds. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10115. Springer, Cham. https://doi.org/10.1007/978-3-319-54193-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54193-8_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54192-1

  • Online ISBN: 978-3-319-54193-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation