Passive navigation as a pattern recognition problem

The most basic visual capabilities found in living organisms are based on motion. Machine vision, of course, does not have to copy animal vision, but the existence of reliably functioning vision modules in nature gives us some reason to believe that it is possible for an artificial system to work in the same or a similar way. In this article it is argued that many navigational capabilities can be formulated as pattern recognition problems. An appropriate retinotopic representation of the image would make it possible to extract the information necessary to solve motion-related tasks through the recognition of a set of locations on the retina. This argument is illustrated by introducing a representation of image motion by which an observer's egomotion could be derived from information globally encoded in the image-motion field. In the past, the problem of determining a system's own motion from dynamic imagery has been considered as one of the classical visual reconstruction problems, wherein local constraints have been employed to compute from exact 2-D image measurements (correspondence, optical flow) the relative 3-D motion and structure of the scene in view. The approach introduced here is based on new global constraints defined on local normal-flow measurements—the spatio-temporal derivatives of the image-intensity function. Classifications are based on orientations of normal-flow vectors, which allows selection of vectors that form global patterns in the image plane. The position of these patterns is related to the 3-D motion of the observer, and their localization provides the axis of rotation and the direction of translation. The constraints introduced are utilized in algorithmic procedures formulated as search techniques. These procedures are very stable, since they are not affected by small perturbations in the image measurements. As a matter of fact, the solution to the two directions of translation and rotation is not affected, as long as the measurement of the sign of the normal flow is correct.Part of this work was accomplished while the author was visiting the Royal Institute of Technology, Stockholm, Sweden. The research was supported in part by ARPA, ONR, and Swedish National Science Foundation.