Inferring Bias and Uncertainty in Camera Calibration

Hagemann, Annika; Knorr, Moritz; Janssen, Holger; Stiller, Christoph

doi:10.1007/s11263-021-01528-x

Cited by 11 publications

(5 citation statements)

References 32 publications

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“…Before hand-eye calibration, the camera needs to be calibrated internally [6]. In this study, Halcon's algorithm is used to calibrate the camera internal reference, so the calibration board used in Halcon's official routine is also used.…”

Section: Internal Reference Calibrationmentioning

confidence: 99%

Research on Large Field of View Mosaic Technology Based on Surface Structured Light

Gao

Mao

Liao

2022

Advances in Transdisciplinary Engineering

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In the actual production measurement process, the vision of machine vision is to broaden the application scene of surface structured light and realize the surface measurement and detection of large parts. This paper introduces a 3D image mosaic system based on fringe projection. The sensor system is composed of a DLP projector and a 3D camera, and is fixed at the end of the robot arm. The three-dimensional point cloud data of the workpiece surface to be measured is obtained by moving the manipulator and using the raster projection method. An algorithm of rough positioning using manipulator is proposed, and the automobile splicing experiment is carried out. Experimental results show that this method can effectively obtain 3D point cloud data and has good stitching accuracy.

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Section: Internal Reference Calibrationmentioning

confidence: 99%

Research on Large Field of View Mosaic Technology Based on Surface Structured Light

Gao

Mao

Liao

2022

Advances in Transdisciplinary Engineering

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“…The purpose of calibration is to obtain a mapping model between the target that is in three-dimensional coordinate space and the two-dimensional image [22]. Essentially, the internal parameters, external parameters, and distortion coefficients that are obtained through this calibration are the basis of the image correction, parameter inversion, and three-dimensional reconstruction.…”

Section: Stereo Calibrationmentioning

confidence: 99%

Research on a Measurement Method for the Ocean Wave Field Based on Stereo Vision

Sun

Wang

et al. 2022

Applied Sciences

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The wave parameter is an important environmental input condition. Traditional contact wave measurement methods are unable to meet the requirements of high precision, non-contact, and ship wave field assessment. Alternatively, stereo vision technology can realize a non-contact and mobile form of measurement. However, this technology suffers from poor timeliness and adaptability. This paper proposes a comprehensive wave measurement method that is based on stereo vision, wherein the gridding of siftGPU is used to achieve the fast matching of large images. The whole algorithm can be run within 6 s and it guarantees more than 20,000 feature-matching logarithms. Furthermore, by utilizing the least squares method and sea surface wave surface theory, the sea surface base level can be calculated without control points, along with the inversion of the sea wave parameters (wave height, period, and wave direction) and error point fitting. The rationality and superiority of the algorithm were verified through multiple comparison experiments. Compared with the Richard Brancker Research (RBR) wave height meter, the measurement error of the wave height is less than 10%, the period error is less than 0.5 s, and the wave direction error is less than 10° with the proposed method.

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“…High values may indicate large systematic errors or inaccurate calibration. Nevertheless, they cannot give a complete information about the quality of the calibration [ 32 ]. Some camera calibration software packages, such as those implemented in Matlab [ 33 ] or OpenCV [ 34 ], also calculate the expanded uncertainty of both interior and exterior camera parameters based in Zhang’s method [ 6 ] for a 95% confidence level, assuming they follow a normal probability distribution.…”

Section: Introductionmentioning

confidence: 99%

“…The results of camera calibration using the software mentioned above should be interpreted with caution for the following reasons: The method of estimating parameter uncertainties leads to the assumption that the model is linear, when, in fact, it is highly non-linear. Although there are studies dedicated to the analysis of the uncertainty of camera parameters [ 31 , 32 , 36 , 37 ], none of them have verified the hypothesis of normality of the calibrated parameters. The accuracy of the parameters with these software has not been verified with a suitable alternative method.…”

Section: Introductionmentioning

confidence: 99%

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Novel Bayesian Inference-Based Approach for the Uncertainty Characterization of Zhang’s Camera Calibration Method

Gutiérrez-Moizant,

Boada,

Ramírez-Berasategui

et al. 2023

Sensors

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Camera calibration is necessary for many machine vision applications. The calibration methods are based on linear or non-linear optimization techniques that aim to find the best estimate of the camera parameters. One of the most commonly used methods in computer vision for the calibration of intrinsic camera parameters and lens distortion (interior orientation) is Zhang’s method. Additionally, the uncertainty of the camera parameters is normally estimated by assuming that their variability can be explained by the images of the different poses of a checkerboard. However, the degree of reliability for both the best parameter values and their associated uncertainties has not yet been verified. Inaccurate estimates of intrinsic and extrinsic parameters during camera calibration may introduce additional biases in post-processing. This is why we propose a novel Bayesian inference-based approach that has allowed us to evaluate the degree of certainty of Zhang’s camera calibration procedure. For this purpose, the a prioriprobability was assumed to be the one estimated by Zhang, and the intrinsic parameters were recalibrated by Bayesian inversion. The uncertainty of the intrinsic parameters was found to differ from the ones estimated with Zhang’s method. However, the major source of inaccuracy is caused by the procedure for calculating the extrinsic parameters. The procedure used in the novel Bayesian inference-based approach significantly improves the reliability of the predictions of the image points, as it optimizes the extrinsic parameters.

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Inferring Bias and Uncertainty in Camera Calibration

Cited by 11 publications

References 32 publications

Research on Large Field of View Mosaic Technology Based on Surface Structured Light

Research on Large Field of View Mosaic Technology Based on Surface Structured Light

Research on a Measurement Method for the Ocean Wave Field Based on Stereo Vision

Novel Bayesian Inference-Based Approach for the Uncertainty Characterization of Zhang’s Camera Calibration Method

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