Applied Intelligence

, Volume 2, Issue 3, pp 251–270 | Cite as

A general dynamic vision architecture for UGV and UAV

  • E. D. Dickmanns


The expectation-based 4D approach to dynamic machine vision exploiting integral spatiotemporal models of objects in the real world is discussed in the application domains of unmanned ground and air vehicles. The method has demonstrated superior performance over the last half decade in autonomous road vehicle guidance with three different vans and busses, with an AGV on the factory floor and with completely autonomous relative state estimation for a twin turboprop aircraft in the landing approach to a runway without any external support; in all application areas only a small set of conventional microcomputers was sufficient for realizing the system. This shows the computational efficiency of the method combining both conventional engineering type algorithms and artificial intelligence components in a well balanced way.

The modularity of the approach is demonstrated in a simulation set-up serving both the ground- and the air vehicle applications. Expermental results in both areas are discussed.

Key words

Machine vision vision architecture vehicle guidance state estimation modeling 


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  1. 1.
    R.E. Kalman, “A new approach to linear filtering and prediction problems,” Trans. ASME, Series D, Journal of Basic Engineering, pp. 35–45, 1960.Google Scholar
  2. 2.
    H.J., Wuensche, “Bewegungssteuerung durch rechnersehen,” Fachberichte Messen, Steuern, Regeln, No. 20, Springer-Verlag, Berlin, 1988 (Motion control by computer vision).Google Scholar
  3. 3.
    E.D. Dickmanns and V. Graefe, a) “Dynamic monocular machine vision,” b) “Application of dynamic monocular machine vision,” J. Machine Vision Application, pp. 223–261, 1988.Google Scholar
  4. 4.
    E.D. Dickmanns, and B. Mysliwetz, “Recursive 3D road and relative ego-state recognition,” IEEE-Trans. PAMI, Special Issue on ‘Interpretation of 3D Scenes’, Feb. 1992.Google Scholar
  5. 5.
    G.J. Bierman, “Measurement updating using the U-D factorization,” in Proc. IEEE Control and Decision Conf., Houston, TX, 1975, pp. 337–346.Google Scholar
  6. 6.
    B. Mysliwetz, “Parallelrechner-basierte Bildfolgeninterpretation zur autonomen Fahrzeugsteuerung,” Dissertation, Fakultät für Luft- und Raumfahrttechnik der Universität der Bundeswehr München, 1990 (Image sequence interpretation for autonomous guidance of vehicles based on parallel processors).Google Scholar
  7. 7.
    G.J., Bierman, Factorization Methods for Discrete Sequential Estimation, Acad. Press: New York, 1979.Google Scholar
  8. 8.
    P.S., Maybeck, Stochastic Models, Estimation and Control, Vol. 1, Acad. Press: New York, 1979.Google Scholar
  9. 9.
    K. Kuhnert, “Zur Echtzeit-Bildfolgenanalyse mit Vorwissen,” Dissertation, Fakultät für Luft-und Raumfahrttechnik der Universität der Bundeswehr München, 1988 (Real-time image sequence analysis using knowledge).Google Scholar
  10. 10.
    E.D. Dickmanns, “Subject-object discrimination in 4D- dynamic scene interpretation for machine vision,” Proceedings IEEE-Workshop on Visual Motion, Newport Beach, 1989, pp. 298–304.Google Scholar
  11. 11.
    J. Schick and E.D. Dickmanns, “Simultaneous estimation of 3D shape and motion of objects by computer vision,” IEEE-Second Workshop on Visual Motion, Princeton, 1991.Google Scholar
  12. 12.
    E.D., Dickmanns, “4D dynamic vision for intelligent motion control,“ Int. Journal for Engineering Applications of AI (IJEAAI), Vol. 4, No. 4, pp. 301–307, 1991.Google Scholar
  13. 13.
    S.S., Iyengar and A., Elfes, “Autonomous Mobile Robots: Control, Planning, and Architecture,” IEEE Comp. Soc. Press: Los Alamitos, 1991.Google Scholar
  14. 14.
    E.D. Dickmanns and A. Zapp, “A curvature-based scheme for improving road vehicle guidance by computer vision,” in Mobile Robots, SPIE-Proc. Vol. 727, Cambridge, MA, 1986, pp. 161–168.Google Scholar
  15. 15.
    E.D. Dickmanns, and T. Christians, “Relative 3D-state estimation for autonomous visual guidance of road vehicles,” in Intelligent Autonomous Systems 2, edited by T. Kanade, Amsterdam, pp. 683–693, 1989.Google Scholar
  16. 16.
    E.D. Dickmanns, B. Mysliwetz, and T. Christians, “Spatio-temporal guidance of autonomous vehicles by computer vision,” IEEE-Trans.on Systems, Man and Cybernetics, Vol. 20, No. 6, (Special Issue on Unmanned Vehicles and Intelligent Robotic Systems), pp. 1273–1284, 1990.Google Scholar
  17. 17.
    B. Mysliwetz and E.D. Dickmanns, “Distributed scene analysis for autonomous road vehicle guidance,” Proc. SPIE Conf. on Mobile Robots, Vol. 852, Cambridge, MA, 1987, pp. 72–79.Google Scholar
  18. 18.
    C. Hock, “ATHENE, ein Projekt zur Landmarkennavigation,” 6. Fachgespräch Autonome Mobile Systeme, Karlsruhe, 1990, (Athene, a project for landmark navigation).Google Scholar
  19. 19.
    G. Eberl, “Automatischer Landeanflug durch Rechnersehen,” Dissertation, Fakultät für Luft- und Raumfahrttechnik der Universität der Bundeswehr München, 1987, (Automatic landing approach by computer vision).Google Scholar
  20. 20.
    E.D. Dickmanns, “Computer vision for flight vehicles,” Zeitschrift für Flugwissenschaft und Weltraumforschung (ZFW), 1988.Google Scholar
  21. 21.
    R. Schell and E.D. Dickmanns, “Autonomous automatic landing through computer vision,” in AGARD Conf. Proc. No. CP-455: ‘Advances in Techniques and Technologies for Air Vehicle Navigation and Guidance’, 1989, pp. 24.1–24.9.Google Scholar
  22. 22.
    R. Schell, “Bordautonomer automatischer Landeanflug aufgraund bildhafter und inertialer Meßdatenauswertung,” Dissertation, UniBw München, Fakultät LRT, 1992, (On-board autonomous automatic landing approach based on visual and inertial data evaluation).Google Scholar
  23. 23.
    B. Mysliwetz and E.D. Dickmanns, “A vision system with active gaze control for real-time interpretation of well structured scenes,” in Proc. of 1-st Conference on Intelligent Autonomous Systems (IAS), edited by L.O. Hertzberger, Amsterdam, 1986, pp. 477–483.Google Scholar
  24. 24.
    E.D., Dickmanns, “Simulation for the development of a visual autopilot-system for road vehicles,” in Automotive Simulation, edited by M.R., Heller Springer-Verlag, Berlin, 1989, pp. 11–22.Google Scholar

Copyright information

© Kluwer Academic Publishers 1992

Authors and Affiliations

  • E. D. Dickmanns
    • 1
  1. 1.Universität der Bundeswehr München, Fakultät für Luft-und Raumfahrttechnik (LRT)Institut für Systemdynamik und FlugmechanikNeubibergGermany

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