Comparison and error analysis of the standard pin-hole and Scheimpflug camera calibration models

2013 ◽  
Author(s):  
Aritz Legarda ◽  
Alberto Izaguirre ◽  
Nestor Arana ◽  
Aitzol Iturrospe
Keyword(s):  
Error Analysis ◽  
Camera Calibration ◽  
Scheimpflug Camera ◽  
Calibration Models

Validation methods for geometric camera calibration

2020 ◽  
Vol 2020 (16) ◽  
pp. 150-1-150-6
Author(s):  
Paul Romanczyk
Keyword(s):  
Coordinate System ◽  
Camera Calibration ◽  
Image Plane ◽  
Test Validation ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Geometric Calibration ◽  
Calibration Models ◽  
The World ◽  
Assistance Systems

Camera-based advanced driver-assistance systems (ADAS) require the mapping from image coordinates into world coordinates to be known. The process of computing that mapping is geometric calibration. This paper provides a series of tests that may be used to assess the goodness of the geometric calibration and compare model forms: 1. Image Coordinate System Test: Validation that different teams are using the same image coordinates. 2. Reprojection Test: Validation of a camera’s calibration by forward projecting targets through the model onto the image plane. 3. Projection Test: Validation of a camera’s calibration by inverse projecting points through the model out into the world. 4. Triangulation Test: Validation of a multi-camera system’s ability to locate a point in 3D. The potential configurations for these tests are driven by automotive use cases. These tests enable comparison and tuning of different calibration models for an as-built camera.

A General Imaging Model Based Method for Scheimpflug Camera Calibration

2018 ◽  
Vol 38 (8) ◽  
pp. 0815009
Author(s):  
孙聪 Sun Cong ◽  
刘海波 Liu Haibo ◽  
陈圣义 Chen Shengyi ◽  
尚洋 Shang Yang
Keyword(s):  
Camera Calibration ◽  
Scheimpflug Camera ◽  
Model Based ◽  
Imaging Model

scholarly journals Robust Calibration of Cameras with Telephoto Lens Using Regularized Least Squares

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mingpei Liang ◽  
Xinyu Huang ◽  
Chung-Hao Chen ◽  
Gaolin Zheng ◽  
Alade Tokuta
Keyword(s):  
Error Analysis ◽  
Camera Calibration ◽  
Focal Length ◽  
Real Data ◽  
Order Error ◽  
First Order ◽  
Camera Parameters ◽  
The One ◽  
Well Posed ◽  
Telephoto Lens

Cameras with telephoto lens are usually used to recover details of an object that is either small or located far away from the cameras. However, the calibration of this kind of cameras is not as accurate as the one of cameras with short focal lengths that are commonly used in many vision applications. This paper has two contributions. First, we present a first-order error analysis that shows the relation between focal length and estimation uncertainties of camera parameters. To our knowledge, this error analysis with respect to focal length has not been studied in the area of camera calibration. Second, we propose a robust algorithm to calibrate the camera with a long focal length without using additional devices. By adding a regularization term, our algorithm makes the estimation of the image of the absolute conic well posed. As a consequence, the covariance of camera parameters can be reduced greatly. We further used simulations and real data to verify our proposed algorithm and obtained very stable results.

scholarly journals LiDAR-Camera Calibration Using Line Correspondences

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6319
Author(s):  
Zixuan Bai ◽  
Guang Jiang ◽  
Ailing Xu
Keyword(s):  
Error Analysis ◽  
Camera Calibration ◽  
Real Data ◽  
Point Clouds ◽  
Translation Vector ◽  
Parallel Lines ◽  
Novel Approach ◽  
Extrinsic Parameters ◽  
On Line ◽  
Data Error

In this paper, we introduce a novel approach to estimate the extrinsic parameters between a LiDAR and a camera. Our method is based on line correspondences between the LiDAR point clouds and camera images. We solve the rotation matrix with 3D–2D infinity point pairs extracted from parallel lines. Then, the translation vector can be solved based on the point-on-line constraint. Different from other target-based methods, this method can be performed simply without preparing specific calibration objects because parallel lines are commonly presented in the environment. We validate our algorithm on both simulated and real data. Error analysis shows that our method can perform well in terms of robustness and accuracy.

scholarly journals AN ITERATIVE APPROACH FOR SELF-CALIBRATING BUNDLE ADJUSTMENT

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 97-102
Author(s):  
J. L. Wang
Keyword(s):  
Camera Calibration ◽  
Measurement Accuracy ◽  
Bundle Adjustment ◽  
Controlled Environment ◽  
Calibration Model ◽  
Iterative Approach ◽  
Virtual Image ◽  
Advantages And Disadvantages ◽  
Novel Approach ◽  
Calibration Models

Abstract. Obtaining accurate image interior and exterior orientations is the key to improve 3D measurement accuracy besides reliable and accurate image matching. A majority of cameras used for those tasks are non-metric cameras. Non-metric cameras commonly suffer various distortions. Generally, there are two ways to remove these distortions: 1) conducting prior camera calibration in a controlled environment; 2) applying self-calibrating bundle adjustment in the application environment. Both approaches have their advantages and disadvantages but one thing is common that there is no universal calibration model available so far which can remove all sorts of distortions on images and systemic errors of image orientations. Instead of developing additional calibration models for camera calibration and self-calibrating adjustment, this paper presents a novel approach which applies self-calibrating bundle adjustment in an iterative fashion: after performing a conventional self-calibrating bundle adjustment, the image coordinates of tie points are re-calculated using the newly obtained self-calibration model coefficients, and the self-calibrating bundle adjustment is applied again in the hope that the remaining distortions and systematic errors will be reduced further within next a few iterations. Using a “virtual image” concept this iterative approach does not require to resample images or/and re-measure tie points during iterations, only costs a few additional iterations computational resource. Several trails under various application environments are conducted using this proposed iterative approach and the results indicate that not only the distortions can be reduced further but also image orientations become much stable after a few iterations.

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