An Optical Flow-Based Solution to the Problem of Range Identification in Perspective Vision Systems

A classical problem in machine vision is the range identification of an object moving in three-dimensional space from the two-dimensional image sequence obtained with a monocular camera. This study presents a novel reduced-order optical flow-based nonlinear observer that renders the proposed scheme suitable for depth estimation applications in both well-structured and unstructured environments. In this study, a globally exponentially stable observer is synthesized, where optical flow estimates are derived from tracking feature trajectory on the image plane over successive camera frames, to yield asymptotic estimates of feature depth at a desired convergence rate. Furthermore, the observer is shown to be finite-gain \(\mathcal {L}_{p}\) stable ?p∈1,∞] in the presence of exogenous disturbance influencing camera motion, and is applicable to a wider class of perspective systems than those considered by alternative designs. The observer requires minor apriori system information for convergence, and the convergence condition arises in a natural manner with an apparently intuitive interpretation. Numerical and experimental studies are used to validate and demonstrate robust observer performance in the presence of significant measurement noise.