RECURSIVE 3-D MOTION ESTIMATION FROM A MONOCULAR IMAGE SEQUENCE

The problem considered here involves the design and application of a recursive algorithm to a sequence or 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, consisting of fixed features on the object, the image plane coordinates of which have been extracted from successive images in the sequence. The measured data are the noisy image plane coordinates of this set (or of a subset of this set) of object match points, taken from each image in the sequence. High image plane noise levels (up to ~ 10 percent of the object image size) are allowed. 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 Cramér-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. © 1990 IEEE