Skip to main content
Book cover

BMVC92 pp 548–559Cite as

Ground Plane Obstacle Detection under variable Camera Geometry Using a Predictive Stereo Matcher

  • Conference paper
  • 178 Accesses

Abstract

A scheme is proposed for ground plane obstacle detection under conditions of variable camera geometry. It uses a predictive stereo matcher implemented in the PILUT architecture described below, in which is encoded the disparity map of the ground plane for the different viewing positions required to scan the work space. The research is the extension of Mallot et al’s (1989) scheme for ground plane obstacle detection which begins with an inverse perspective mapping of the left and right images that transforms the image locations of all points arising from the ground plane so that they have zero disparity: simple differencing of the resulting images then permits ready detection of obstacles. The essence of this physiologically-inspired method is to exploit knowledge of the prevailing camera geometry (to find epipolar lines) and the expectation of a ground plane (to predict the locations along epipolars of corresponding left/right image points of features arising from the ground plane).

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   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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. J. Albus, “A new approach to manipulator control: The cerebellar model articulation controller (CMAC)”, Trans. ASME -J. Dyn. Syst. Meas. Control, 1975,vol. 97, pp 220–227.

    Article  MATH  Google Scholar 

  2. J. Albus,“Data storage in the cerebellar model articulation controlled (CMAC)”, Trans. ASME -J Dyn. Syst. Meas. Control, 1975,vol 97, pp 228–233.

    Article  MATH  Google Scholar 

  3. P. Dean, J.E.W. Mayhew, N. Thacker, & P.M. Langdon, “Saccade control in a simulated robot camera-head system: neural net architectures for efficient learning of inverse kinematics.”, Biological Cybernetics, 1991, 66, 27–36.

    Article  Google Scholar 

  4. F. Girosi, T. Poggio, (1989) “Representation properties of networks: Kolmogorov’s theorem is irrelevant”, Neural Computation, 1989, vol. 1, no. 4 pp 465–469.

    Article  Google Scholar 

  5. S.H. Lane, M.G. Flax, D.A. Handelman, J.J. Gelfand, “Function approximation using multi-layered neural networks with B-spline receptive field functions.”, CSL Report 47,1991, 1–37

    Google Scholar 

  6. H.A. Mallot, E. Schulze, & K. Storjohann, “Neural network strategies for robot navigation.”, Proc. n’Euro,In G. Dreyfus & L. Personnaz (Ed.), 1988 ,Paris:

    Google Scholar 

  7. J.E.W. Mayhew, P. Dean,P. Langdon, “Artifical neural networks for the kinematic control of a stereo camera head”, (in preparation), 1992

    Google Scholar 

  8. J.E.W. Mayhew, H.C. Longuet-Higgins, “A computational model of binocular depth perception.”, Nature, 1982, 297 (5865) 376–379.

    Article  Google Scholar 

  9. T. Poggio,F. Girosi, “A theory of networks for approximation and learning.”,A.I. MEMO NO. 1140.,Atifical Inteligence Laboratory, Massachusetts Institute of Technology, 1989

    Google Scholar 

  10. T. Poggio, & F. Girosi, “Networks for approximation and learning.”, Proceedings of the IEEE, 78(9),1990, 1481–1497.

    Article  Google Scholar 

  11. T. Poggio, & F. Girosi, “Regularization algorithms for leaning that are equivalent to multilayer networks.”, Science,1990, 247, 978–982.

    Article  MATH  MathSciNet  Google Scholar 

  12. M.A.S. Potts, D.S. Broomhead, Time series þrediction with a radial basis function neural network., Adaptive Signal processing, Simon Haykin (Ed), Proceedings of SPIE 1565,1991, 255–266

    Chapter  Google Scholar 

  13. N.A. Thacker, J.E.W. Mayhew, “Optimal combination of stereo camera calibration from arbitrary stereo images.”, Image and Vision Computing (feb 1991). vol 9 no 1 27–32.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1992 Springer-Verlag London Limited

About this paper

Cite this paper

Cornell, S., Porrill, J., Mayhew, J.E.W. (1992). Ground Plane Obstacle Detection under variable Camera Geometry Using a Predictive Stereo Matcher. In: Hogg, D., Boyle, R. (eds) BMVC92. Springer, London. https://doi.org/10.1007/978-1-4471-3201-1_57

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-3201-1_57

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19777-5

  • Online ISBN: 978-1-4471-3201-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics