New contributions on line-projections in omnidirectional vision - Bermudez-Cameo, Jesus (ELCVIA Electronic Letters on Computer Vision and Image Analysis, 2016)
Computer vision has an increasing interest in most fields of emerging technologies. A challenging topic in this field is to study how to enlarge the field of view of the camera systems to obtain more information of the environment in a single view. In particular, omnidirectional vision can be useful in many applications such as estimating location in robotics, autonomous driving and unmanned aerial vehicles. The wide field of view of omnidirectional cameras allows taking advantage of describing 3D scenarios using line features. On the one hand, line features represent natural landmarks in man-made environments, they are easy to understand, coincident with edges of constructive elements and often still present when having texture-less scenarios. On the other hand, long segments are especially useful for drift compensation because they are usually completely visible on the omnidirectional projection. However, in omnidirectional cameras line projections are distorted by the projection mapping becoming complex curves. This thesis is focused on the geometry of line projections (line-images) in omnidirectional systems. Main addressed topic of this work is line-image extraction on different kinds of central and non-central omnidirectional images. However, due to the nature of projection in omnidirectional cameras, other addressed topics are camera calibration and, in the case on non-central cameras, 3D reconstruction from single images.