Abstract
Passive navigation refers to the ability of an organism or a robot that moves in its environment to determine its own motion precisely on the basis of some perceptual input, for the purposes of kinetic stabilization. The problem has been treated, for the most part, as a general recovery from dynamic imagery problem, and it has been formulated as the general 3-D motion estimation (or structure from motion) module. Consequently, if a robust solution to the passive navigation problem—as it has been formulated in the recovery paradigm—is achieved, we will immediately be able to solve many other important problems, as simple applications of the general principle. However, despite numerous theoretical results, no technique has found applications in systems that can perform well in the real world. In this paper, we outline some of the reasons behind this and we develop a robust solution to the passive navigation problem which is
-
purposive, in the sense that it does not claim any generality. It just solves the kinetic stabilization problem and cannot be used as it is for other problems related to 3-D motion.
-
qualitative, in the sense that the solution comes as the answer to a series of simple yes/no questions and not as the result of complicated numerical processing.
-
active, in the sense that the activity of the observer (in this case “saccades”) is essential for the solution of the problem.
The input to the perceptual process of kinetic stabilization that we have developed is the normal flow, i.e. the projection of the optic flow along the direction of the image gradient.
Contributions of this work are the fact that translation can be estimated reliably from a normal flow field that also contains rotation.
This article was processed using the LATEX macro package with ECCV92 style.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Adiv, G.: Determining three-dimensional motion and structure from optical flow generated by several moving objects. IEEE Trans. PAMI 7 (1985a) 384–401.
Adiv, G.: Inherent ambiguities in recovering 3D motion and structure from a noisy flow field. Proc. IEEE Conference on Computer Vision and Pattern Recognition (1985b) 70–77.
Aloimonos, J.: Purposive and qualitative active vision. Proc. DARPA Image Understanding Workshop (1990a) 816–828.
Aloimonos, J.: Perspective approximations. Image and Vision Computing 8 (1990b) 177–192.
Aloimonos, J., Brown, CM.: The relationship between optical flow and surface orientation. Proc. International Conference on Pattern Recognition, Montreal, Canada (1984).
Aloimonos, J., Weiss, I., Bandopadhay, A.: Active vision. Int'l. J. Comp. Vision 2 (1988) 333–356.
Ballard, D.H.: Parameter networks. Artificial Intelligence 22 (1984) 235–267.
Bruss, A., Horn, B.K.P.: Passive navigation. Computer Vision, Graphics Image Processing 21 (1983) 3–20.
Duriç, Z., Aloimonos, J.: Passive navigation: An active and purposive solution. Technical Report CAR-TR-560, Computer Vision Laboratory, Center for Automation Research, University of Maryland, College Park (1991).
Horn, B.K.P.: Relative Orientation. MIT AI Memo 994 (1988).
Horn, B.K.P., Weldon, E.J.: Computationally efficient methods of recovering translational motion. Proc. International Conference on Computer Vision (1987) 2–11.
Longuet-Higgins, H.C.: A computer algorithm for reconstructing a scene from two projections. Nature 293 (1981) 133–135.
Longuet-Higgins, H.C., Prazdny, K.: The interpretation of a moving retinal image. Proc. Royal Soc. London B 208 (1980) 385–397.
Marr, D.: Vision (W.H. Freeman, San Francisco (1982).
Negahdaripour, S.: Ph.D. Thesis, MIT Artificial Intelligence Laboratory (1986).
Nelson, R.C., Aloimonos, J.: Finding motion parameters from spherical flow fields (Or the advantages of having eyes in the back of your head). Biological Cybernetics 58 (1988) 261–273.
Nelson, R., Aloimonos, J.: Using flow field divergence for obstacle avoidance in visual navigation. IEEE Trans. PAMI 11 (1989) 1102–1106.
Spetsakis, M.E., Aloimonos, J.: Optimal computing of structure from motion using point correspondences in two frames. Proc. International Conference on Computer Vision (1988).
Spetsakis, M.E., Aloimonos, J.: Unification theory of structure from motion. Technical Report CAR-TR-482, Computer Vision Laboratory, Center for Automation Research, University of Maryland, College Park (1989).
Spetsakis, M.E., Aloimonos, J.: Structure from motion using line correspondences. Int'l. J. Computer Vision 4 (1990) 171–183.
Tsai, R.Y., Huang, T.S.: Uniqueness and estimation of three dimensional motion parameters of rigid objects with curved surfaces. IEEE Trans. PAMI 6 (1984) 13–27.
Ullman, S.: The Interpretation of Visual Motion (MIT Press, Cambridge, MA (1979).
Weng, J., Huang, T.S., Ahuja, N.: A two step approach to optimal motion and structure estimation. Proc. IEEE Computer Society Workshop on Computer Vision (1987).
Young, G.S., Chellappa, R.: 3-D motion estimation using a sequence of noisy stereo images. Proc. IEEE Conference on Computer Vision and Pattern Recognition (1988).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1992 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aloimonos, Y., Duriç, Z. (1992). Active egomotion estimation: A qualitative approach. In: Sandini, G. (eds) Computer Vision — ECCV'92. ECCV 1992. Lecture Notes in Computer Science, vol 588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55426-2_55
Download citation
DOI: https://doi.org/10.1007/3-540-55426-2_55
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-55426-4
Online ISBN: 978-3-540-47069-4
eBook Packages: Springer Book Archive