Skip to main content

PixelLaser: Computing Range from Monocular Texture

  • Conference paper
  • 1602 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6455))

Abstract

The impressive advances in robotic spatial reasoning over the past decade have relied primarily on rich sensory data provided by laser range finders. Relative to cameras, however, lasers are heavy, bulky, power-hungry, and expensive. This work proposes and evaluates an image-segmentation pipeline that produces range scans from ordinary webcameras. Starting with a nearest-neighbor classification of image patches, we investigate the tradeoffs in accuracy, resolution, calibration, and speed that come from estimating range-to-obstacles using only single images. Experiments atop the low-cost iRobot Create platform demonstrate the accessibility and power of this pixel-based alternative to laser scans.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Blas, R., Agrawal, M., Sundaresan, A., Konolige, K.: Fast color/texture segmentation for outdoor robots. In: Proceedings, IEEE IROS, Nice, France, pp. 4078–4085 (September 2008)

    Google Scholar 

  2. Buhmann, J., Burgard, W., Cremers, A.B., Fox, D., Hofmann, T., Schneider, F., Strikos, J., Thrun, S.: The Mobile Robot Rhino. AI Magazine 16(2), 31–38 (Summer 1995)

    Google Scholar 

  3. Fletcher, F., Teller, S., Olson, E., Moore, D., Kuwata, Y., How, J., Leonard, J., Miller, I., Campbell, M., Huttenlocher, D., Nathan, A., Kline, F.R.: The MIT - Cornell Collision and Why it Happened. Journal of Field Robotics 25(10), 775–807 (2008)

    Article  Google Scholar 

  4. Hoiem, D., Efros, A.A., Hebert, M.: Recovering Surface Layout from an Image. International Journal of Computer Vision 75(1), 151–172 (2007)

    Article  MATH  Google Scholar 

  5. Horswill, I.: Analysis of Adaptation and Environment. Artificial Intelligence 73, 1–30 (1995)

    Article  Google Scholar 

  6. Kanade, T., Kanade, B.Y., Morris, D.D.: Factorization methods for structure from motion. Phil. Trans. of the Royal Society of London, Series A 356, 1153–1173 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  7. Laws, K.: Rapid texture identification. In: Proceedings, SPIE. Image Processing for Missile Guidance, vol. 238, pp. 376–380 (1980)

    Google Scholar 

  8. Marder-Eppstein, E., Berger, E., Foote, T., Gerkey, B., Konolige, K.: The Office Marathon: Robust Navigation in an Indoor Office Environment. In: IEEE ICRA 2010, pp. 300–307 (2010)

    Google Scholar 

  9. Muja, M., Lowe, D.G.: Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration. In: Proceedings, VISAPP 2009 (2009)

    Google Scholar 

  10. OpenCV’s, homepage http://opencv.willowgarage.com/wiki/ (accessed 07/17/2010)

  11. Plagemann, C., Enres, F., Hess, J., Stachniss, C., Burgard, W.: Monocular Range Sensing: A non-parametric learning approach. In: IEEE ICRA 2008, pp. 929–934. IEEE Press, Los Alamitos (2008)

    Google Scholar 

  12. Saxena, A., Chung, S.H., Ng, A.: 3-D Depth Reconstruction from a Single Still Image. International Journal of Computer Vision 76(1), 53–69 (2008)

    Article  Google Scholar 

  13. Steux, B., El Hamzaoui, O.: CoreSLAM: a SLAM Algorithm in less than 200 lines of C code. In: Submission ICARCV 2010 (2010), www.openslam.org/coreslam.html

  14. Taylor, T., Geva, S., Boles, W.W.: Monocular Vision as Range Sensor. In: Proceedings, CIMCA, Gold Coast, Australia, July 12-14, pp. 566–575 (2004)

    Google Scholar 

  15. Thrun, S., Bennewitz, M., Burgard, W., Cremers, A.B., Dellaert, F., Fox, D., Hähnel, D., Rosenberg, C., Roy, N., Schulte, J., Schulz, D.: MINERVA: A second-generation museum tour-guide robot. In: Proceedings, IEEE ICRA 1999, pp. 1999–2005. IEEE Press, Los Alamitos (1999)

    Google Scholar 

  16. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)

    MATH  Google Scholar 

  17. Urmson, C., Baker, C., Dolan, J., Rybski, P., Salesky, B., Whittaker, W.L., Ferguson, D., Darms, M.: Autonomous Driving in Traffic: Boss and the Urban Challenge. AI Magazine 30(2), 17–29 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lesperance, N., Leece, M., Matsumoto, S., Korbel, M., Lei, K., Dodds, Z. (2010). PixelLaser: Computing Range from Monocular Texture. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17277-9_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17276-2

  • Online ISBN: 978-3-642-17277-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics