Monocular depth has been found using estimation, closed-form
solution and learning techniques. Estimation and closed-form solution compute
the depth from motion, while learning techniques calculate the depth using a
single image with a depth map as a supervisor. This paper presents a new closed
form solution for monocular depth from motion. The proposed method builds
on the notation that an interest point in an image of a static scene has a static
world location. Camera pose and calibration parameters are used as constraints
to provide the depth solution. The proposed method is verified through real
experiments on indoor mobile robot platform. The effect of uncertainty in the
solution variables is studied and the results are benchmarked to groundtruth.