Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Visual Computing

ISVC 2006: Advances in Visual Computing pp 534–543Cite as

Autonomous Vehicle Video Aided Navigation – Coupling INS and Video Approaches

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4292))

Abstract

As autonomous vehicle systems become more prevalent, their navigation capabilities become increasingly critical. Currently most systems rely on a combined GPS/INS solution for vehicle pose computation, while some systems use a video-based approach. One problem with a GPS/INS approach is the possible loss of GPS data, especially in urban environments. Using only INS in this case causes significant drift in the computed pose. The video-based approach is not always reliable due to its heavy dependence on image texture. Our approach to autonomous vehicle navigation exploits the best of both of these by coupling an outlier-robust video-based solution with INS when GPS is unavailable. This allows accurate computation of the system’s current pose in these situations. In this paper we describe our system design and provide an analysis of its performance, using simulated data with a range of different noise levels.

This is a preview of subscription content, 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   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. Debrunner, C.H., Ahuja, N.: Segmentation and factorization-based motion and structure estimation for long image sequences. IEEE Trans. Pattern Anal. Mach. Intell. 20(2), 206–211 (1998)

    Article  Google Scholar 

  2. Davison, A.J.: Real-time simultaneous localisation and mapping with a single camera. In: International Conference on Computer Vision, Nice, France (2003)

    Google Scholar 

  3. Baker, C., Debrunner, C., Whitehorn, M.: 3D model generation using unconstrained motion of a hand-held video camera. In: The International Society for Optical Engineering. Springer, San Jose (2006)

    Google Scholar 

  4. Alenya, G., Martnez, E., Torras, C.: Fusing visual and inertial sensing to recover robot egomotion. Journal of Robotic Systems 21, 23–32 (2004)

    Article  Google Scholar 

  5. Diel, D.D., DeBitetto, P., Teller, S.: Epipolar Constraints for Vision-Aided Inertial Navigation. In: IEEE Workshop on Motion and Video Computing (WAVC/MOTION 2005). IEEE Computer Society, Los Alamitos (2005)

    Google Scholar 

  6. Deil, D.D.: Stochastic Constraints for Vision-Aided Inertial Navigation. In: Mechanical Engineering, Massachusetts Institute of Technology, p. 110 (2005)

    Google Scholar 

  7. Chai, L.: 3-D Motion and Structure Estimation Using Inertial Sensors and Computer Vision for Augmented Reality. In: Engineering Division, p. 110. Colorado School of Mines, Golden (2000)

    Google Scholar 

  8. Domke, J., Aloimonos, Y.: Integration of Visual and Inertial Information for Egomotion: a Stochastic Approach, Dept. of Computer Science, University of Maryland, College Park (2006)

    Google Scholar 

  9. Huster, A., Rock, S.M.: Relative Position Sensing by Fusing Monocular Vision and Inertial Rate Sensors. In: International Conference on Advanced Robotics, Proceedings of ICAR 2003, Coimbra, Portugal (2003)

    Google Scholar 

  10. Makadia, A., Daniilidis, K.: Correspondenceless Ego-Motion Estimation. In: IEEE International Conference on Robotics and Automation (2005)

    Google Scholar 

  11. Nguyen, K.: Inertial Data Fusion using Kalman Filter Methods for Augmented Reality. In: Engineering. Colorado School of Mines, Golden (1998)

    Google Scholar 

  12. Qian, G., Chellappa, R., Zheng, Q.: Robust structure from motion estimation using inertial data. J. Opt. Soc. Am. 18(12), 2982–2997 (2001)

    Article  Google Scholar 

  13. Rehbinder, H., Ghosh, B.K.: Mulit-rate fusion of visual and inertial data. In: International Conference on Multisensor Fusion and Integration for Intelligent Systems (2001)

    Google Scholar 

  14. Rihbinder, H., Ghosh, B.: Pose Estimation Using Line-Based Dynamic Vision and Inertial Sensors. IEEE Transactions on Automatic Control 48(2), 186–199 (2003)

    Article  Google Scholar 

  15. Roumeliotis, S.I., Johnson, A.E., Motgomery, J.F.: Augmenting inertial navigation with image-based motion estimation. In: IEEE International Conference on Robitics and Automation (2002)

    Google Scholar 

  16. Strelow, D., Singh, S.: Online Motion Estimation from Image and Inertial Measurements. In: Workshop on Integration of Vision and Inertial Sensors (INERVIS) (2002)

    Google Scholar 

  17. You, S., Neumann, U.: Integrated Inertial and Vision Tracking for Augmented Reality Registration. In: Virtual Reality Annual International Symposium. IEEE, Houston (1999)

    Google Scholar 

  18. You, S., Neumann, U., Azuma, R.: Hybrid inertial and vision tracking for augmented reality registration. In: IEEE Virtual Reality 1999. IEEE, Los Alamitos (1999)

    Google Scholar 

  19. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference (1988)

    Google Scholar 

  20. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Communications of the Association of Computing Machinery 24, 381–395 (1981)

    MathSciNet  Google Scholar 

  21. Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: International Joint Conference on Artificial Intelligence (1981)

    Google Scholar 

  22. Shi, J., Tomasi, C.: Good Features to Track. In: IEEE Conference on Computer Visioin and Pattern Recognition (CVPR 1994), Seattle (1994)

    Google Scholar 

  23. Flenniken, W.: Modeling Inertial Measurement Units and Analyzing the Effect of their Errors in Navigation Applications. Auburn University, Auburn (2005)

    Google Scholar 

  24. Brown, R.G., Hwang, P.Y.C.: Introduction to random signals and applied Kalman filtering, 2nd edn., p. 502. J. Wiley, New York (1997)

    MATH  Google Scholar 

  25. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2001)

    Google Scholar 

  26. Nister, D.: An efficient solution to the five-point relative pose problem. Computer Vision and Pattern Recognition (2003)

    Google Scholar 

  27. Ma, Y., Soatto, S.: An invitation to 3-d vision: from images to geometric models. Springer, New York (2004)

    MATH  Google Scholar 

  28. Torr, P.H.: MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding 78, 138–156 (2000)

    Article  Google Scholar 

  29. Brown, R., Hwang, P.: Introduction to Random Signals and Applied Kalman Filtering, 3rd edn. Wiley, New York (1992)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. PercepTek, Inc., Littleton, CO

    Chris Baker, Chris Debrunner & William Severson

  2. Colorado School of Mines, Golden, CO

    Sean Gooding & William Hoff

Authors
  1. Chris Baker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chris Debrunner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sean Gooding
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. William Hoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. William Severson
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Intel, 95052, Santa Clara, CA, USA

    Ara Nefian

  7. University of California, 430, Computer Science Building, 92697-3425, Irvine, CA, USA

    Gopi Meenakshisundaram

  8. Institute for Data Analysis and Visualization, P.O. Box

    Valerio Pascucci

  9. Department of Computer Science and Engineering, Czech Technical University in Prague, Czech

    Jiri Zara

  10. Rockwell Scientific, 1049 Camino Dos Rios, 91360, Thousand Oaks, CA, USA

    Jose Molineros

  11. Computer Graphics Group, Bielefeld University, D-33501, Bielefeld, Germany

    Holger Theisel

  12. Hewlett Packard Labs, Palo Alto, CA, USA

    Tom Malzbender

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Baker, C., Debrunner, C., Gooding, S., Hoff, W., Severson, W. (2006). Autonomous Vehicle Video Aided Navigation – Coupling INS and Video Approaches. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919629_54

Download citation

  • DOI: https://doi.org/10.1007/11919629_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48626-8

  • Online ISBN: 978-3-540-48627-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation