Skip to main content

Sensor Models and Multisensor Integration

  • Chapter
Autonomous Robot Vehicles

Abstract

We maintain that the key to intelligent fusion of disparate sensory information is to provide an effective model of sensor capabilities. A sensor model is an abstraction of the actual sensing process. It describes the information a sensor is able to provide, how this information is limited by the environment, how it can be enhanced by information obtained from other sensors, and how it may be improved by active use of the physical sensing device. The importance of having a model of sensor performance is that capabilities can be estimated a priori and, thus, sensor strategies developed in line with information requirements.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  • Allen, P. 1987. Object recognition using vision and touch. Boston: Kluwer Academic.

    Book  Google Scholar 

  • Athans, M. 1987. Command and control theory: a challenge to controlscience. IEEE Trans. Automatic Control 32 (4): 286–293.

    Article  Google Scholar 

  • Ayache, N., and Faugeras, O. 1987 (London). Building, registrating, and fusing noisy visual maps. Int. Conf. Computer Vision.

    Google Scholar 

  • Bachrach, M. 1975. Group decisions in the face of differences of opinion. Management Sci. 22: 182.

    Article  MathSciNet  Google Scholar 

  • Bajesy, R., Krotkov, E., and Mintz, M. 1986. Models of errors and mistakes in machine perception. Technical Report MS-CIS-86–26, U. Pennsylvania, Dept. Computer Science.

    Google Scholar 

  • Bolle, R. M., and Cooper, D. B. 1986. On optimally combining pieces of information, with application to estimating 3-d complex-object position from range data. IEEE Trans. Pattern Analysis and Machine Intelligence 8: 619.

    Article  Google Scholar 

  • Cameron, A. R., Daniel, R., and Durrant-Whyte, H. 1988 (April 25–29, Philadelphia, Pa.). Touch and motion. Proc. IEEE Int. Conf. Robotics and Automation.

    Google Scholar 

  • Durrant-Whyte, H. F. 1987a. Consistent integration and propagation of disparate sensor information. Int. J. Robotics Research 6 (3): 3–24.

    Article  Google Scholar 

  • Durrant-Whyte, H. F. 1987b. Integration, coordination, and control of multi-sensor robot systems. Boston: Kluwer Academic.

    Book  Google Scholar 

  • Durrant-Whyte, H. F. 1988. Uncertain geometry in robotics. IEEE J. Robotics and A utomation 4 ( 1 ): 23–31.

    Article  Google Scholar 

  • Faugeras, O., and Ayache, N. 1986 ( San Francisco, Calif.). Building visual maps by combining noisy stereo measurements. Proc. IEEE Conf Robotics and Automation.

    Google Scholar 

  • Flynn, A. M. 1985. Redundant sensors for mobile robot navigation. Technical Report M.Sc. Thesis, MIT.

    Google Scholar 

  • Gelb, M. 1974. Applied optimal estimation. Cambridge, Mass.: MIT Press.

    Google Scholar 

  • Hager, G. 1987. Information maps for active sensor control. Technical Report MS-CIS-87–07, U. Pennsylvania, Dept. Computer Science.

    Google Scholar 

  • Hager, G., and Durrant-Whyte, H. F. 1988. Information and multi-sensor coordination. In Uncertainty in Artificial Intelligence 2, North-Holland.

    Google Scholar 

  • Harsanyi, S. 1977. Rational behavior and bargaining. New York: Cambridge University Press.

    Book  MATH  Google Scholar 

  • Henderson, T. 1987. Workshop on multi-sensor integration. Technical Report UUCS-87–006, U. Utah Computer Science.

    Google Scholar 

  • Henderson, T., and Hansen, C. 1985. The specification of distributed sensing and control. J. Robotic Systems 2:387– 396.

    Google Scholar 

  • Ho, Y. C. 1980. Team decision theory and information structures. Proc. IEEE 68: 644.

    Article  Google Scholar 

  • Ho, Y. C., and Chu, K. C. 1972. Team decision theory and information structures in optimal control. IEEE Trans. Automatic Control 17: 15.

    Article  MATH  MathSciNet  Google Scholar 

  • Huber, P. J. 1981. Robust statistics. New York: Wiley.

    Book  MATH  Google Scholar 

  • Krotkov, E. 1986. Focusing. Technical Report MS-CIS-86– 22, U. Pennsylvania Dept. Computer Science.

    Google Scholar 

  • Marshak, J., and Radnor, R. 1972. The economic theory of teams. New Haven, Conn.: Yale University Press.

    Google Scholar 

  • McKendall, R., and Mintz, M. 1987. Models of sensor noise and optimal algorithms for estimation and quantization in vision systems. Technical Report MC-CIS-87, U. Pennsylvania Dept. Computer Science.

    Google Scholar 

  • Nash, J. F. 1950. The bargaining problem. Econometrica 155.

    Google Scholar 

  • Porril, J., Pollard, S. B., and Mayhew, J. E. W. 1987. Optimal combination of multiple sensors including stereo vision. Image and Vision Computing 5: 174–180.

    Article  Google Scholar 

  • Terzopoulos, D. 1986. Integrating visual information from multiple sources. In From pixels to predicates, ed. A. P. Pentland. Ablex Press.

    Google Scholar 

  • Weerahandi, S., and Zidek, J. V. 1981. Multi-bayesian statistical decision theory. J. Royal Statistical Society 44: 85.

    Article  MathSciNet  Google Scholar 

  • Weerahandi, S., and Zidek, J. V. 1983. Elements of multi- bayesian decision theory. The Annals of Statistics 11: 1032.

    MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1990 AT&T

About this chapter

Cite this chapter

Durrant-Whyte, H.F. (1990). Sensor Models and Multisensor Integration. In: Cox, I.J., Wilfong, G.T. (eds) Autonomous Robot Vehicles. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-8997-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4613-8997-2_7

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4613-8999-6

  • Online ISBN: 978-1-4613-8997-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy