This study proposes a novel complete-order nonlinear structure and motion observer for monocular vision systems subjected to significant measurement noise. In contrast with previous studies that assume noise-free measurements, and require prior knowledge of either the relative motion of the camera or scene geometry, the proposed scheme assumes a single component of linear velocity as known. Under a persistency of excitation condition, the observer then relies on filtered estimates of optical flow to yield exponentially convergent estimates of the unknown motion parameters and feature depth that converge to a uniform, ultimate bound in the presence of measurement noise. The unknown linear and angular velocities are assumed to be generated using an imperfectly known model that incorporates a bounded uncertainty, and optical flow estimation is accomplished using a robust differentiator that is based on the sliding-mode technique. Numerical results are used to validate and demonstrate superior observer performance compared to an alternative leading design in the presence of model uncertainty and measurement noise.