Robust monocular depth perception using feature pairs and approximate motion

The authors present a novel approach to the problem of constructing a depth map from a sequence of monocular images. The approach requires knowledge only of the axial translation component of the moving camera. When the configuration of the camera is known with respect to the mobile platform, this information can be readily obtained by projecting displacement data from a wheel encoder or range sensors onto the focal axis. It is hypothesized that there is a pair of feature points which have the same depth. Based on this hypothesis, the depths are calculated for all pairs of feature points found in the image. As the robot moves, the relative location of two points changes in a specific and predictable manner on the image plane if they are actually at the same depth. The motion of each pair of points is observed, and if it is consistent with the predicted behavior, the hypothesis is accepted. Accepted pairs create a graph structure which contains depth relations among the feature points. The algorithm is robust against rotational and translational motion noises, and its performance has been experimentally demonstrated