Skip to main content

Spatio-temporal Support for Range Flow Based Ego-Motion Estimators

  • Conference paper
Computer Analysis of Images and Patterns (CAIP 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8048))

Included in the following conference series:

  • 4285 Accesses

Abstract

A real-time range flow based ego-motion estimator for a moving depth sensor is presented. The estimator recovers the translation and rotation components of a sensor’s motion and integrates these temporally. To ensure accurate inter-frame motion estimates, an iterative form of the estimator is developed. To minimise drift in the pose, additional temporal constraint is provided through the use of anchor frames. The algorithm is evaluated on the recently published TUB RGB-D Benchmark. Performance is commensurate with alternative methodologies such as SLAM but at a fraction of the computational cost.

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

Access this chapter

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anandan, P., Bergen, J.R., Hanna, K.J., Hingorani, R.: Hierarchical Model-Based Motion Estimation, pp. 1–22. Kluwer Academic Publishers, Boston (1993)

    Google Scholar 

  2. Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A Database and Evaluation Methodology for Optical Flow. International Journal of Computer Vision 92(1), 1–31 (2011)

    Article  Google Scholar 

  3. Barron, J.L., Spies, H.: The Fusion of Image and Range Flow. In: Proceedings of the 10th International Workshop on Theoretical Foundations of Computer Vision: Multi-Image Analysis, London, UK, pp. 171–189 (2001)

    Google Scholar 

  4. Besl, P.J., McKay, N.D.: A Method for Registration of 3-D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)

    Article  Google Scholar 

  5. Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: MonoSLAM: Real-time Single Camera SLAM. IEEE Transactions Pattern Analysis Machine Intelligence 29(6), 1052–1067 (2007)

    Article  Google Scholar 

  6. Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard, W.: An Evaluation of the RGB-D SLAM System. In: Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), St. Paul, MA, USA (May 2012)

    Google Scholar 

  7. Gottfried, J.-M., Fehr, J., Garbe, C.S.: Computing Range Flow from Multi-modal Kinect Data. In: Bebis, G. (ed.) ISVC 2011, Part I. LNCS, vol. 6938, pp. 758–767. Springer, Heidelberg (2011)

    Google Scholar 

  8. Harville, M., Rahimi, A., Darrell, T., Gordon, G., Woodfill, J.: 3D Pose Tracking with Linear Depth and Brightness Constraints. In: International Conference on Computer Vision, pp. 206–213 (1999)

    Google Scholar 

  9. Horn, B.K.P., Harris, J.: Rigid Body Motion from Range Image Sequences. CVGIP: Image Understanding 3(1), 1–13 (1991)

    Article  Google Scholar 

  10. Newcombe, R., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., 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 (2011)

    Google Scholar 

  11. Holmes, S.A., Klein, G., Murray, D.W.: An O(N^2) Square Root Unscented Kalman filter for Visual Simultaneous Localization and Mapping. IEEE Transactions Pattern Analysis Machine Intelligence 31(7), 1251–1253 (2009)

    Article  Google Scholar 

  12. Sabata, B., Aggarwal, J.K.: Estimation of Motion from a Pair of Range Images: A review. CVGIP 54(3), 309–324 (1991)

    Article  MATH  Google Scholar 

  13. Salvi, J., Matabosch, C., Fofi, D., Forest, J.: A Review of Recent Range Image Registration Methods with Accuracy Evaluation. Image and Vision Computing 25(5), 578–596 (2007)

    Article  Google Scholar 

  14. Spies, H., Jahne, B., Barron, J.L.: Range Flow Estimation. Computer Vision and Image Understanding 85, 209–231 (2002)

    Article  MATH  Google Scholar 

  15. Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A Benchmark for the Evaluation of RGB-D SLAM Systems. In: International Conference on Intelligent Robot Systems (October 2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Jones, G.A., Hunter, G. (2013). Spatio-temporal Support for Range Flow Based Ego-Motion Estimators. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40246-3_66

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40246-3_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40245-6

  • Online ISBN: 978-3-642-40246-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics