New contributions on line-projections in omnidirectional vision

Q4 Computer Science
J. Bermudez-Cameo
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引用次数: 1

Abstract

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.
全向视觉中直线投影的新贡献
计算机视觉在大多数新兴技术领域受到越来越多的关注。如何扩大相机系统的视场,在单一视场中获取更多的环境信息,是该领域的一个具有挑战性的课题。特别是,全方位视觉可以在许多应用中发挥作用,例如机器人,自动驾驶和无人驾驶飞行器的位置估计。全方位相机的宽视场允许利用线特征来描述3D场景。一方面,线条特征代表了人造环境中的自然地标,它们易于理解,与构造元素的边缘一致,并且在没有纹理的场景中仍然存在。另一方面,长段对漂移补偿特别有用,因为它们通常在全向投影上是完全可见的。然而,在全向相机中,由于投影映射成为复杂的曲线,直线投影被扭曲。本文主要研究全向系统中线投影(线像)的几何特性。本文主要研究了不同类型的中心和非中心全向图像的线图像提取问题。然而,由于全向相机的投影性质,其他讨论的主题是相机校准,以及在非中心相机的情况下,从单个图像进行3D重建。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Electronic Letters on Computer Vision and Image Analysis
Electronic Letters on Computer Vision and Image Analysis Computer Science-Computer Vision and Pattern Recognition
CiteScore
2.50
自引率
0.00%
发文量
19
审稿时长
12 weeks
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