Skip to main content

A Brute Force Approach to Depth Camera Odometry

  • Chapter
Consumer Depth Cameras for Computer Vision

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

  • 5403 Accesses

Abstract

By providing direct access to 3D information of the environment, depth cameras are particularly useful for perception applications such as Simultaneous Localization And Mapping or object recognition. With the introduction of the Kinect in 2010, Microsoft released a low cost depth camera that is now intensively used by researchers, especially in the field of indoor robotics. This chapter introduces a new 3D registration algorithm that can deal with considerable sensor motion. The proposed approach is designed to take advantage of the powerful computational scalability of Graphics Processing Units (GPUs).

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Armesto, L., Minguez, J., Montesano, L.: A generalization of the metric-based iterative closest point technique for 3D scan matching. In: IEEE International Conference on Robotics and Automation, pp. 1367–1372 (2010)

    Chapter  Google Scholar 

  2. Besl, P., McKay, H.: A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  3. Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4, 25–30 (1965)

    Article  Google Scholar 

  4. Brusco, M., Andreetto, M., Giorgi, A., Cortelazzo, G.: 3D registration by textured spin-images. In: International Conference on 3-D Digital Imaging and Modeling, pp. 262–269 (2005)

    Chapter  Google Scholar 

  5. Chu, J., Mei Nie, C.: Multi-view point clouds registration and stitching based on sift feature. In: International Conference on Computer Research and Development, vol. 1, pp. 274–278. (2011)

    Google Scholar 

  6. Enqvist, O., Jiang, F., Kahl, F.: A brute-force algorithm for reconstructing a scene from two projections. In: IEEE Conference on Computer Vision and Pattern Recognition (2011)

    Google Scholar 

  7. Fioraio, N., Konolige, K.: Realtime visual and point cloud slam. In: Robotics Science and Systems Conference (2011)

    Google Scholar 

  8. Greenspan, M., Yurick, M.: Approximate K-d tree search for efficient ICP. In: International Conference on 3-D Digital Imaging and Modeling, pp. 442–448 (2003)

    Google Scholar 

  9. Hu, S., Zha, H., Zhang, A.: Registration of multiple laser scans based on 3d contour features. In: International Conference on Information Visualization, pp. 725–730 (2006)

    Google Scholar 

  10. Jost, T., Hugli, H.: A multi-resolution ICP with heuristic closest point search for fast and robust 3D registration of range images. In: International Conference on 3-D Digital Imaging and Modeling, pp. 427–433 (2003)

    Google Scholar 

  11. Kitaaki, Y., Okuda, H., Kage, H., Sumi, K.: High speed 3-d registration using gpu. In: SICE Annual Conference, pp. 3055–3059 (2008)

    Chapter  Google Scholar 

  12. Li, H., Hartley, R.: The 3D–3D registration problem revisited. In: International Conference on Computer Vision (2007)

    Google Scholar 

  13. May, S., Droeschel, D., Fuchs, S., Holz, D., Nuchter, A.: Robust 3D-mapping with time-of-flight cameras. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1673–1678 (2009)

    Google Scholar 

  14. Neumann, D., Lugauer, F., Bauer, S., Wasza, J., Hornegger, J.: Real-time RGB-d mapping and 3-D modeling on the GPU using the random ball cover data structure. In: International Conference on Computer Vision—Workshop on Consumer Depth Cameras for Computer Vision, pp. 1161–1167 (2011)

    Google Scholar 

  15. Nuchter, A., Lingemann, K., Hertzberg, J., Surmann, H.: 6D SLAM with approximate data association. In: International Conference on Advanced Robotics, pp. 242–249 (2005)

    Google Scholar 

  16. Nuchter, A., Lingemann, K., Hertzberg, J.: Cached K-d tree search for ICP algorithms. In: International Conference on 3-D Digital Imaging and Modeling, pp. 419–426 (2007)

    Chapter  Google Scholar 

  17. Ohno, K., Nomura, T., Tadokoro, S.: Real-time robot trajectory estimation and 3d map construction using 3d camera. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5279–5285 (2006)

    Chapter  Google Scholar 

  18. Park, S.-Y., Choi, S.-I., Moon, J., Kim, J., Park, Y.W.: Real-time 3D registration of stereo-vision based range images using GPU. In: Workshop on Applications of Computer Vision (2009)

    Google Scholar 

  19. Qiu, D., May, S., Nüchter, A.: GPU-accelerated nearest neighbor search for 3D registration. In: International Conference on Computer Vision Systems, pp. 194–203 (2009)

    Chapter  Google Scholar 

  20. Rusu, R., Blodow, N., Marton, Z., Beetz, M.: Aligning point cloud views using persistent feature histograms. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3384–3391 (2008)

    Google Scholar 

  21. Strand, M., Erb, F., Dillmann, R.: Range image registration using an octree based matching strategy. In: International Conference on Mechatronics and Automation, pp. 1622–1627 (2007)

    Chapter  Google Scholar 

  22. Tamaki, T., Abe, M., Raytchev, B., Kaneda, K.: Softassign and EM-ICP on GPU. In: International Conference on Networking and Computing, pp. 179–183 (2010)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jonathan Israël .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this chapter

Cite this chapter

Israël, J., Plyer, A. (2013). A Brute Force Approach to Depth Camera Odometry. In: Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K. (eds) Consumer Depth Cameras for Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4640-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4640-7_3

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4639-1

  • Online ISBN: 978-1-4471-4640-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics