Real-Time Solution to the Absolute Pose Problem with Unknown Radial Distortion and Focal Length

Kúkelová, Zuzana; Bujnak, Martin; Pajdla, Tomáš

doi:10.1109/iccv.2013.350

Cited by 100 publications

(74 citation statements)

References 16 publications

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“…If the camera intrinsic parameters are known, we use the standard P3P algorithm [32]. Otherwise, we use the P5Pfr algorithm [33] to compute the intrinsic parameters (focal length, distortion parameters) along with the 6 DOF pose. In experiments, we computed only one distortion parameter, which makes the P5Pfr algorithm over-determined.…”

Section: Keyframes In the Mapmentioning

confidence: 99%

“…Table I illustrates the results. Note that the camera model used in the P5Pfr algorithm [33] and in [2] are different (in terms of the focal length and the lens distortion model), which implies that we cannot compare the exact values of these parameters. Nevertheless, the intrinsic parameters obtained by our method are close to those obtained with [2]; in particular, the focal length, which is typically the most important intrinsic parameter for camera localization, has an average error of 4% with respect to the ground truth (computed as the mean for the x and y axes).…”

Section: B Quantitative Analysismentioning

confidence: 99%

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Calibration of Non-overlapping Cameras Using an External SLAM System

Ataer-Cansızoğlu

Taguchi

Ramalingam³

et al. 2014

2014 2nd International Conference on 3D Vision

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We present a simple method for calibrating a set of cameras that may not have overlapping field of views. We reduce the problem of calibrating the non-overlapping cameras to the problem of localizing the cameras with respect to a global 3D model reconstructed with a simultaneous localization and mapping (SLAM) system. Specifically, we first reconstruct such a global 3D model by using a SLAM system using an RGB-D sensor. We then perform localization and intrinsic parameter estimation for each camera by using 2D-3D correspondences between the camera and the 3D model. Our method locates the cameras within the 3D model, which is useful for visually inspecting camera poses and provides a model-guided browsing interface of the images. We demonstrate the advantages of our method using several indoor scenes. International Conference on 3D Vision (3DV)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract-We present a simple method for calibrating a set of cameras that may not have overlapping field of views. We reduce the problem of calibrating the non-overlapping cameras to the problem of localizing the cameras with respect to a global 3D model reconstructed with a simultaneous localization and mapping (SLAM) system. Specifically, we first reconstruct such a global 3D model by using a SLAM system using an RGB-D sensor. We then perform localization and intrinsic parameter estimation for each camera by using 2D-3D correspondences between the camera and the 3D model. Our method locates the cameras within the 3D model, which is useful for visually inspecting camera poses and provides a model-guided browsing interface of the images. We demonstrate the advantages of our method using several indoor scenes.

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Section: Keyframes In the Mapmentioning

confidence: 99%

Section: B Quantitative Analysismentioning

confidence: 99%

Calibration of Non-overlapping Cameras Using an External SLAM System

Ataer-Cansızoğlu

Taguchi

Ramalingam³

et al. 2014

2014 2nd International Conference on 3D Vision

View full text Add to dashboard Cite

show abstract

“…More focus are attracted to solving the minimal problems. All these works [11,18,13,19,20,14] are based on the minimal solvers. In [11], a minimal solver using eight point correspondences is given to estimate the fundamental matrix and the single radial distortion parameter for the uncalibrated case.…”

Section: Introductionmentioning

confidence: 99%

“…A 4-point algorithm is given based on Gröbeer basis. In [20], simple concepts from linear algebra, instead of Gröbeer basis, are used that leads to a real-time solution to the same pose problem.…”

Section: Introductionmentioning

confidence: 99%

A Minimal Solution to Relative Pose with Unknown Focal Length and Radial Distortion

Jiang

Kuang²,

Solem

et al. 2015

Lecture Notes in Computer Science

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Abstract. In this paper, we study the minimal problem of estimating the essential matrix between two cameras with constant but unknown focal length and radial distortion. This problem is of both theoretical and practical interest and it has not been solved previously. We have derived a fast and stable polynomial solver based on Gröbner basis method. This solver enables simultaneous auto-calibration of focal length and radial distortion for cameras. For experiments, the numerical stability of the solver is demonstrated on synthetic data. We also evaluate on real images using either RANSAC or kernel voting. Compared with the standard minimal solver, which does not model the radial distortion, our proposed solver both finds a larger set of geometrically correct correspondences on distorted images and gives an accurate estimate of the radial distortion and focal length.

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“…In both PnP definitions, however, the camera is always assumed to be calibrated and known, allowing to transform image measurements into unit vectors, that point from the camera to the 3D scene points. More recent approaches extend this classical definition by including unknown camera parameters into the formulation such as the focal length (Wu, 2015) or radial distortion (Kukelova et al, 2013). …”

Section: Introductionmentioning

confidence: 99%

MLPNP – A REAL-TIME MAXIMUM LIKELIHOOD SOLUTION TO THE PERSPECTIVE-N-POINT PROBLEM

Urban¹,

Leitloff²,

Hinz³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, a statistically optimal solution to the Perspective-n-Point (PnP) problem is presented. Many solutions to the PnP problem are geometrically optimal, but do not consider the uncertainties of the observations. In addition, it would be desirable to have an internal estimation of the accuracy of the estimated rotation and translation parameters of the camera pose. Thus, we propose a novel maximum likelihood solution to the PnP problem, that incorporates image observation uncertainties and remains real-time capable at the same time. Further, the presented method is general, as is works with 3D direction vectors instead of 2D image points and is thus able to cope with arbitrary central camera models. This is achieved by projecting (and thus reducing) the covariance matrices of the observations to the corresponding vector tangent space.

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Real-Time Solution to the Absolute Pose Problem with Unknown Radial Distortion and Focal Length

Cited by 100 publications

References 16 publications

Calibration of Non-overlapping Cameras Using an External SLAM System

Calibration of Non-overlapping Cameras Using an External SLAM System

A Minimal Solution to Relative Pose with Unknown Focal Length and Radial Distortion

MLPNP – A REAL-TIME MAXIMUM LIKELIHOOD SOLUTION TO THE PERSPECTIVE-N-POINT PROBLEM

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Real-Time Solution to the Absolute Pose Problem with Unknown Radial Distortion and Focal Length

Cited by 100 publications

References 16 publications

Calibration of Non-overlapping Cameras Using an External SLAM System

Calibration of Non-overlapping Cameras Using an External SLAM System

A Minimal Solution to Relative Pose with Unknown Focal Length and Radial Distortion

MLPNP &ndash; A REAL-TIME MAXIMUM LIKELIHOOD SOLUTION TO THE PERSPECTIVE-N-POINT PROBLEM

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Product

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MLPNP – A REAL-TIME MAXIMUM LIKELIHOOD SOLUTION TO THE PERSPECTIVE-N-POINT PROBLEM