Motion bias and structure distortion induced by calibration errors

Zucchelli, Marco; Košecká, Jana

doi:10.5244/c.15.68

Cited by 7 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, a distortion leads to systematic correlated errors in the image position [22] and thus, in the 3D reconstruction. Also the uncertainties of calibration parameters lead to biased measurements [23]. In the following, the influence of these uncertainties on the reconstructed motion is analyzed.…”

Section: B Uncertainties Of Observationsmentioning

confidence: 99%

Reliable estimation of heart surface motion under stochastic and unknown but bounded systematic uncertainties

Bogatyrenko

Noack

Hanebeck

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-A reliable estimation of heart surface motion is an important prerequisite for the synchronization of surgical instruments in robotic beating heart surgery. In general, only an imprecise description of the heart dynamics and measurement systems is available. This means that the estimation of heart motion is corrupted by stochastic and systematic uncertainties. Without consideration of these uncertainties, the obtained results will be inaccurate and a safe robotic operation cannot be guaranteed. Until now, existing approaches for estimating the motion of the heart surface are either deterministic or treat only stochastic uncertainties. The proposed method extends the heart motion estimation to the simultaneous consideration of stochastic and unknown but bounded systematic uncertainties. It computes dynamic bounds in order to provide the surgeon with a guidance by constraining the motion of the surgical instruments and thereby protecting sensitive tissue.

show abstract

Section: B Uncertainties Of Observationsmentioning

confidence: 99%

Reliable estimation of heart surface motion under stochastic and unknown but bounded systematic uncertainties

Bogatyrenko

Noack

Hanebeck

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

show abstract

“…This mapping p : R 3 → R 2 is obtained during camera calibration. Errors in the calibration will lead to false assumptions on the mapping, which can impact all subsequent inferences and deteriorate a system's overall performance [22,36,4,40,5,1]. The detection and prevention of errors is therefore a critical aspect of the calibration.…”

Section: Introductionmentioning

confidence: 99%

Inferring bias and uncertainty in camera calibration

Hagemann¹,

Knorr²,

Janssen³

et al. 2021

Preprint

View full text Add to dashboard Cite

Accurate camera calibration is a precondition for many computer vision applications. Calibration errors, such as wrong model assumptions or imprecise parameter estimation, can deteriorate a system's overall performance, making the reliable detection and quantification of these errors critical. In this work, we introduce an evaluation scheme to capture the fundamental error sources in camera calibration: systematic errors (biases) and uncertainty (variance). The proposed bias detection method uncovers smallest systematic errors and thereby reveals imperfections of the calibration setup and provides the basis for camera model selection. A novel resampling-based uncertainty estimator enables uncertainty estimation under non-ideal conditions and thereby extends the classical covariance estimator. Furthermore, we derive a simple uncertainty metric that is independent of the camera model. In combination, the proposed methods can be used to assess the accuracy of individual calibrations, but also to benchmark new calibration algorithms, camera models, or calibration setups. We evaluate the proposed methods with simulations and real cameras.

show abstract

“…However, detailed information was not provided regarding how the measurement noise covariance was calculated. Zucchelli and Kosecka [16] discussed how to propagate the uncertainty of a camera's intrinsic parameters into a covariance matrix that characterizes the noisy feature positions in the 3D space. However, this still needs to be further propagated into the image space in order for it to be useful for visual SLAM.…”

Section: Introductionmentioning

confidence: 99%

Camera Sensor Model for Visual SLAM

Zhang

2007

Fourth Canadian Conference on Computer and Robot Vision (CRV '07)

View full text Add to dashboard Cite

In this paper, we present a technique for the construction of a camera sensor model for visual SLAM. The proposed method is an extension of the general camera calibration procedure and requires the camera to observe a planar checkerboard pattern shown at different orientations. By iteratively placing the pattern at different distances from the camera, we can find a relationship between the measurement noise covariance matrix and the range. We conclude that the error distribution of a camera sensor follows a Gaussian distribution, based on the Geary's test, and the magnitude of the error variance is linearly related to the range between the camera and the features being observed. Our sensor model can potentially benefit visual SLAM algorithms by varying its measurement noise covariance matrix with range.

show abstract

Motion bias and structure distortion induced by calibration errors

Cited by 7 publications

References 14 publications

Reliable estimation of heart surface motion under stochastic and unknown but bounded systematic uncertainties

Reliable estimation of heart surface motion under stochastic and unknown but bounded systematic uncertainties

Inferring bias and uncertainty in camera calibration

Camera Sensor Model for Visual SLAM

Contact Info

Product

Resources

About