Abstract
The motion parameter estimation for a class of movements in the space by using stereo vision is considered by observing a group of points. The considered motion equation can cover a wide class of practical movements in the space. The observability of this class of movement is clarified. The estimation algorithm for the motion parameters which are all time-varying is developed based on the second method of Lyapunov. The assumptions about the perspective system are reasonable and have apparently physical interpretations. The proposed recursive algorithm requires minor a priori knowledge about the system. Experimental results show the proposed algorithm is effective even in the presence of measurement noises.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Calway, A.: Recursive estimation of 3D motion and surface structure from local affine flow parameters. IEEE Trans. on Pattern Analysis and Machine Intelligence 27, 562–574 (2005)
Chen, X., Kano, H.: A new state observer for perspective systems. IEEE Trans. Automatic Control 47, 658–663 (2002)
Chen, X., Kano, H.: State Observer for a class of nonlinear systems and its application to machine vision. IEEE Trans. Aut. Control 49, 2085–2091 (2004)
Chiuso, A., Favaro, P., Jin, H., Soatto, S.: Structure from motion causally integrated over time. IEEE Trans Pattern Analysis & Machine Intelligence 24, 523–535 (2002)
Doretto, G., Soatto, S.: Dynamic shape and appearance models. IEEE Trans. on Pattern Analysis and Machine Intelligence 28, 2006–2019 (2006)
Dayawansa, W., Ghosh, B., Martin, C., Wang, X.: A necessary and sufficient condition for the perspective observability problem. Systems & Control Letters 25, 159–166 (1994)
Ghosh, B.K., Inaba, H., Takahashi, S.: Identification of Riccati dynamics under perspective and orthographic observations. IEEE Trans. on Automatic Control 45, 1267–1278 (2000)
Jankovic, M., Ghosh, B.K.: Visually guided ranging from observation of points, lines and curves via an identifier based nonlinear observer. Systems & Control Letters 25, 63–73 (1995)
Kanatani, K.: Group-Theoretical Methods in Image Understanding. Springer, Heidelberg (1990)
Loucks, E.P.: A perspective System Approach to Motion and Shape Estimation in Machine Vision. Ph.D Thesis, Washington Univ. (1994)
Reif, K., Sonnemann, F., Unbehauen, R.: An EKF-based nonlinear observer with a prescribed degree of stability. Automatica 34, 1119–1123 (1998)
Satry, S., Bodson, M.: Adaptive Control, Stability, Convergence, and Robustness. Prentice Hall, Englewood Cliffs (1989)
Soatto, S.: 3-D structure from visual motion: Modelling, representation and observability. Automatica 33, 1287–1321 (1997)
Xirouhakis, Y., Delopoulos, A.: Least squares estimation of 3D shape and motion of rigid objects from their orthographic projections. IEEE Trans. on Pattern Analysis and Machine Intelligence 22, 393–399 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, X. (2009). Stereo Vision Based Motion Parameter Estimation. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-04020-7_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04019-1
Online ISBN: 978-3-642-04020-7
eBook Packages: Computer ScienceComputer Science (R0)