Straight lines have to be straight

Devernay, Frédéric; Faugeras, Olivier

doi:10.1007/pl00013269

Cited by 591 publications

(242 citation statements)

References 21 publications

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“…A final comment is that while it is not possible to recommend this or that camera model in general, it seems a good idea to enhance "ideal" models for cameras with large fields of view (e.g., the equiangular one), with classical radial or tangential models, as proposed for example by Devernay and Faugeras [113], Kannala and Brandt [266], Gennery [161], and Mei and Rives [339].…”

Section: So Many Modelsmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

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A printed and bound version is available at a special discount price of US35 from Now Publishers. This can be obtained by entering the promotional code CGV006023 on https://www.nowpublishers.com/bookorder.aspx?doi=0600000023&product=CGVInternational audienceThis survey is mainly motivated by the increased availability and use of panoramic image acquisition devices, in computer vision and various of its applications. Different technologies and different computational models thereof exist and algorithms and theoretical studies for geometric computer vision ("structure-from-motion") are often re-developed without highlighting common underlying principles. One of the goals of this survey is to give an overview of image acquisition methods used in computer vision and especially, of the vast number of camera models that have been proposed and investigated over the years, where we try to point out similarities between different models. Results on epipolar and multi-view geometry for different camera models are reviewed as well as various calibration and self-calibration approaches, with an emphasis on non-perspective cameras. We finally describe what we consider are fundamental building blocks for geometric computer vision or structure-from-motion: epipolar geometry, pose and motion estimation, 3D scene modeling, and bundle adjustment. The main goal here is to highlight the main principles of these, which are independent of specific camera models

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Section: So Many Modelsmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

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“…where the CamProj(·) function performs perspective division by z followed by the application of a calibrated camera model as in [6], which uses the radial distortion model of [16]. The edgelet direction d is obtained by differentiating the above with respect to motion along the z-axis in the E-frame:…”

Section: Projecting and Finding Edgelets In Keyframesmentioning

confidence: 99%

Improving the Agility of Keyframe-Based SLAM

Klein

Murray

2008

Lecture Notes in Computer Science

266

227

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Abstract. The ability to localise a camera moving in a previously unknown environment is desirable for a wide range of applications. In computer vision this problem is studied as monocular SLAM. Recent years have seen improvements to the usability and scalability of monocular SLAM systems to the point that they may soon find uses outside of laboratory conditions. However, the robustness of these systems to rapid camera motions (we refer to this quality as agility) still lags behind that of tracking systems which use known object models. In this paper we attempt to remedy this. We present two approaches to improving the agility of a keyframe-based SLAM system: Firstly, we add edge features to the map and exploit their resilience to motion blur to improve tracking under fast motion. Secondly, we implement a very simple inter-frame rotation estimator to aid tracking when the camera is rapidly panning -and demonstrate that this method also enables a trivially simple yet effective relocalisation method. Results show that a SLAM system combining points, edge features and motion initialisation allows highly agile tracking at a moderate increase in processing time.

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“…The most extended function for modeling the spherical aberration of a fisheye lens is the tangent. Inspired in the model described in [11], we propose the following inverse rectification expression showing the relation between a pixel in the distorted image and in the undistorted one:…”

Section: A Image Acquisitionmentioning

confidence: 99%

“…For a fisheye lens, this angle must be close to 90 o (given by the manufacturer) but we need the exact value to compute undistorsion correctly. In [11], a calibration method that looks for the angle that generates the smaller amount of straight lines in the undistorted image is proposed. Note that when the image is undistorted straight lines are broken, obtaining a great number of short segments.…”

Section: A Image Acquisitionmentioning

confidence: 99%

Ceiling mosaics through information-based SLAM

Roda

Sáez

Escolano

2007

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper, we present a novel approach to computing ceiling mosaics based on Information Theory. The only sensor of the robot is a digital camera oriented to the ceiling of the map, which is used to approximate the Simultaneous Localization and Mapping (SLAM) problem. We have divided the algorithm into two steps: (i) action estimation, which approximates the actions of the robot maximizing the Mutual Information between consecutive views; and (ii) global rectification, which rectifies the drift of the global trajectory minimizing the entropy of the map. Moreover, a fisheye lens is used to recover enough information from ceilings, reducing their inherent ambiguity. Such lenses produce a semi-spherical aberration in the images, that must be rectified using some information about calibration. In order to do so, we propose a novel technique for image rectification, also based on Information Theory. Finally, we present some experimental results using real data, that prove the robustness of the method.

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Straight lines have to be straight

Cited by 591 publications

References 21 publications

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Improving the Agility of Keyframe-Based SLAM

Ceiling mosaics through information-based SLAM

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