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Kalman Filter

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References

  1. Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng 82 (Series D):35–45

    Article  Google Scholar 

  2. Maybeck PS (1979) Stochastic models, estimation and control, vol 1. Volume 141 of mathematics in science and engineering. Academic, New York

    Google Scholar 

  3. Welch G, Bishop G (1995) An introduction to the Kalman filter. Technical report TR95-041, Department of Computer Science, University of North Carolina at Chapel Hill

    Google Scholar 

  4. Sorenson HW (1970) Least-squares estimation: from gauss to kalman. IEEE Spectr 7:63–68

    Article  Google Scholar 

  5. Brown RG, Hwang PYC (1996) Introduction to random signals and applied Kalman filtering: with MATLAB exercises and solutions, 3rd edn. Wiley, New York

    Google Scholar 

  6. Gelb A (1974) Applied optimal estimation. MIT, Cambridge

    Google Scholar 

  7. Grewal MS, Andrews AP (2001) Kalman filtering theory and practice using MATLAB, 2nd edn. Information and system sciences series. Wiley, New York

    Google Scholar 

  8. Jacobs O (1993) Introduction to control theory, 2nd edn. Oxford University Press, Oxford/New York

    MATH  Google Scholar 

  9. Lewis FL (1986) Optimal estimation: with an introduction to stochastic control theory. Wiley, New York

    MATH  Google Scholar 

  10. Stuller J, Krishnamurthy G (1983) Kalman filter formulation of low-level television image motion estimation. Comput Vis Graph Image Process 21(2):169–204

    Article  Google Scholar 

  11. Matthies L, Kanade T, Szeliski R (1989) Kalman filter-based algorithms for estimating depth from image sequences. Int J Comput Vis 3(3):209–238

    Article  Google Scholar 

  12. van Pabst JV, Krekel PFC (1993) Multisensor data fusion of points, line segments, and surface segments in 3D space. Proc. SPIE, Sensor Fusion VI, 190 (August 20, 1993), pp 190–201

    Article  Google Scholar 

  13. Azarbayejani A, Pentland A (1995) Recursive estimation of motion, structure, and focal length. IEEE Trans Pattern Anal Mach Intell 17(6):562–575

    Article  Google Scholar 

  14. Bradski G (2000) The OpenCV Library. Dr. Dobb’s Journal of Software Tools. http://opencv.willowgarage.com/wiki/CiteOpenCV

  15. MATLAB (2012) Version 8.0.0.273 (R2012b). The MathWorks Inc. Natick, Massachusetts. http://www.mathworks.com/products/matlab/

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Welch, G. (2014). Kalman Filter. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_716

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