This paper addresses the issue of optimal motion and structure estimat
ion from monocular image sequences of a rigid scene. The new method ha
s the following characteristics: (1) the dimension of the search space
in the nonlinear optimization is drastically reduced by exploiting th
e relationship between structure and motion parameters; (2) the degree
of reliability of the observations and estimates is effectively taken
into account; (3) the proposed formulation allows arbitrary interfram
e motion; (4) the information about the structure of the scene, acquir
ed from previous images, is systematically integrated into the new est
imations; (5) the integration of multiple views using this method give
s a large 2.5D visual map, much larger than that covered by any single
view. It is shown also that the scale factor associated with any two
consecutive images in a monocular sequence is determined by the scale
factor of the first two images. Our simulation results and experiments
with long image sequences of real world scenes indicate that the opti
mization method developed in this paper not only greatly reduces the c
omputational complexity but also substantially improves the motion and
structure estimates over those produced by the linear algorithms. (C)
1994 Academic Press, Inc.