Monocular Depth from Motion Using a New Closed-Form Solution

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.