Recursive 3-D motion estimation from a monocular image sequence
Broida, T.J.; Chandrashekhar, S.; Chellappa, R.
Aerospace and Electronic Systems, IEEE Transactions on
Volume 26, Issue 4, Jul 1990 Page(s):639 - 656
Digital Object Identifier 10.1109/7.55557
Summary:Consideration is given to the design and application of a
recursive algorithm to a sequence of images of a moving object to
estimate both its structure and kinematics. The object is assumed to be
rigid, and its motion is assumed to be smooth in the sense that it can
be modeled by retaining an arbitrary number of terms in the appropriate
Taylor series expansions. Translational motion involves a standard
rectilinear model, while rotational motion is described with
quaternions. Neglected terms of the Taylor series are modeled as process
noise. A state-space model is constructed, incorporating both kinematic
and structural states, and recursive techniques are used to estimate the
state vector as a function of time. A set of object match points is
assumed to be available. The problem is formulated as a parameter
estimation and tracking problem which can use an arbitrarily large
number of images in a sequence. The recursive estimation is done using
an iterated extended Kalman filter (IEKF), initialized with the output
of a batch algorithm run on the first few frames. Approximate Cramer-Rao
lower bounds on the error covariance of the batch estimate are used as
the initial state estimate error covariance of the IEKF. The performance
of the recursive estimator is illustrated using both real and synthetic
image sequences
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