Determining Intrinsic Parameters and Pose of Cameras from Single View with Variable Focal Length

2014 ◽  
Vol 635-637 ◽  
pp. 1011-1017
Author(s):  
Gui Hua Liu ◽  
Hui Min Long
Keyword(s):  
Focal Length ◽  
Pinhole Camera ◽  
Projection Matrix ◽  
Camera Model ◽  
Intrinsic Parameter ◽  
Simulation Data ◽  
Single View ◽  
Estimated Parameters ◽  
Orientation Matrix ◽  
Camera Location

This study claims an algorithm of calibration which is executed on the basis of projection matrix. This algorithm directly estimates intrinsic parameter on the basis of rotation matrix’s unitary orthogonality combined with Cholesky decomposition from the obtained projection matrix. Then, false is excluded by rotation matrix’s determinant constraints, and ultimately, camera location and orientation matrix are obtained and estimated parameters are optimized with the minimum error of reprojection residual being cost function. This algorithm is taken under a pinhole camera model and can calibrate the camera from single view with variable focal length. Both simulation data and true image experiments have proved the feasibility and robustness of this algorithm.

scholarly journals VALIDATION OF LIDAR CALIBRATION USING A LIDAR SIMULATOR

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 39-44
Author(s):  
G. Lee ◽  
J. Cheon ◽  
I. Lee
Keyword(s):  
Lidar Measurement ◽  
Intrinsic Parameter ◽  
Geometric Information ◽  
Simulation Data ◽  
Scanning Method ◽  
Intrinsic Parameters ◽  
Estimated Parameters ◽  
Surrounding Environment ◽  
Extrinsic Parameters ◽  
Lidar Calibration

Abstract. LIDAR is being widely used for mapping and modelling because it accurately scans and acquires 3D geometric information of the surrounding environment. In order to improve the accuracy of the LIDAR measurement, it is necessary to precisely estimate the intrinsic parameters as well as extrinsic parameters and eliminate the systematic errors. Many studies are conducted to eliminate these errors caused by the intrinsic parameters of LIDAR. However, when the result of intrinsic calibration is verified using actual LIDAR data, there is a problem that other error factors cannot be excluded. Therefore, in this study, the LIDAR intrinsic calibration is verified by using a LIDAR simulator that simulates the mechanism of the actual LIDAR. When constructing a LIDAR simulator, the systematic error is inserted according to the intrinsic parameter model of LIDAR. And according to the method of scanning with LIDAR, it is divided into upright scanning and tilted scanning, and the error included LIDAR simulation data is generated. After that, the intrinsic parameters are estimated by applying the plane-based intrinsic calibration. Since values of the intrinsic parameters are known, they are compared with the estimated parameters, and the results of estimate are analyzed according to the scanning method.

scholarly journals DoF-Dependent and Equal-Partition Based Lens Distortion Modeling and Calibration Method for Close-Range Photogrammetry

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5934
Author(s):  
Xiao Li ◽  
Wei Li ◽  
Xin’an Yuan ◽  
Xiaokang Yin ◽  
Xin Ma
Keyword(s):  
Optical Axis ◽  
Focal Length ◽  
Calibration Method ◽  
Depth Of Field ◽  
Model Parameters ◽  
Lens Distortion ◽  
Camera Model ◽  
Distortion Model ◽  
Manual Adjustment ◽  
Close Range

Lens distortion is closely related to the spatial position of depth of field (DoF), especially in close-range photography. The accurate characterization and precise calibration of DoF-dependent distortion are very important to improve the accuracy of close-range vision measurements. In this paper, to meet the need of short-distance and small-focal-length photography, a DoF-dependent and equal-partition based lens distortion modeling and calibration method is proposed. Firstly, considering the direction along the optical axis, a DoF-dependent yet focusing-state-independent distortion model is proposed. By this method, manual adjustment of the focus and zoom rings is avoided, thus eliminating human errors. Secondly, considering the direction perpendicular to the optical axis, to solve the problem of insufficient distortion representations caused by using only one set of coefficients, a 2D-to-3D equal-increment partitioning method for lens distortion is proposed. Accurate characterization of DoF-dependent distortion is thus realized by fusing the distortion partitioning method and the DoF distortion model. Lastly, a calibration control field is designed. After extracting line segments within a partition, the de-coupling calibration of distortion parameters and other camera model parameters is realized. Experiment results shows that the maximum/average projection and angular reconstruction errors of equal-increment partition based DoF distortion model are 0.11 pixels/0.05 pixels and 0.013°/0.011°, respectively. This demonstrates the validity of the lens distortion model and calibration method proposed in this paper.

A Flexible Pinhole Camera Model for Coherent Nonuniform Sampling

2014 ◽  
Vol 34 (4) ◽  
pp. 30-41
Author(s):  
Voicu Popescu ◽  
Bedrich Benes ◽  
Paul Rosen ◽  
Jian Cui ◽  
Lili Wang
Keyword(s):  
Nonuniform Sampling ◽  
Pinhole Camera ◽  
Camera Model

scholarly journals Initial Tracking Parameter Estimation of Magnetic Ship Based on PSO

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Jianfei Ma ◽  
Kai Ding ◽  
Bing Yan ◽  
Wen Dong
Keyword(s):  
Filter Bank ◽  
Prior Information ◽  
Unscented Kalman Filter ◽  
Focal Length ◽  
Pso Algorithm ◽  
Estimated Parameters ◽  
Straight Line ◽  
Line Path ◽  
The Magnetic Field ◽  
Double Observer

We consider the problem of tracking a surface magnetic ship as it travels in a straight line path with the exertion of a magnetometer located at the seabed. Note that the initial filter parameters are prior information and the tracking performance depends on the initial filter parameters, and traditional estimation of initial filter parameters is to apply the filter bank algorithm, but there are several obvious defects in this method. In this paper, a novel algorithm based on the particle swarm optimization (PSO) algorithm is proposed to estimate initial parameters of the filter, and the model of uniformly magnetized ellipsoid is adopted to fit the magnetic field of the ship. The simulation results show that, under the condition of no prior information, the estimated ship parameters based on the observation of the single-observer are invalid, whereas the estimated ship parameters based on the observation of the double-observer are valid. Further, the estimated results of real-world recorded magnetic signals show that the ship parameters estimated by PSO based on the double-observer are also valid, as the estimated parameters are used as the initial parameters of the unscented Kalman filter (UKF), and a ship can be tracked effectively by the UKF filter. Moreover, the estimated half focal length can be used as a feature to distinguish noise environment, ships with different sizes, and mine sweepers.

scholarly journals Sky camera geometric calibration using solar observations

2016 ◽  
Vol 9 (9) ◽  
pp. 4279-4294 ◽  
Author(s):  
Bryan Urquhart ◽  
Ben Kurtz ◽  
Jan Kleissl
Keyword(s):  
Focal Length ◽  
Image Plane ◽  
Calibration Procedure ◽  
Objective Lens ◽  
The Sun ◽  
Camera Model ◽  
Image Processing Algorithm ◽  
Position Measurement ◽  
Special Equipment ◽  
Geometric Calibration

Abstract. A camera model and associated automated calibration procedure for stationary daytime sky imaging cameras is presented. The specific modeling and calibration needs are motivated by remotely deployed cameras used to forecast solar power production where cameras point skyward and use 180° fisheye lenses. Sun position in the sky and on the image plane provides a simple and automated approach to calibration; special equipment or calibration patterns are not required. Sun position in the sky is modeled using a solar position algorithm (requiring latitude, longitude, altitude and time as inputs). Sun position on the image plane is detected using a simple image processing algorithm. The performance evaluation focuses on the calibration of a camera employing a fisheye lens with an equisolid angle projection, but the camera model is general enough to treat most fixed focal length, central, dioptric camera systems with a photo objective lens. Calibration errors scale with the noise level of the sun position measurement in the image plane, but the calibration is robust across a large range of noise in the sun position. Calibration performance on clear days ranged from 0.94 to 1.24 pixels root mean square error.

Camera Calibration Technique Based on HALCON Machine Vision

2013 ◽  
Vol 365-366 ◽  
pp. 666-671
Author(s):  
Xue Ping Liu ◽  
Yi Chang Chen ◽  
Zu Fu Pang
Keyword(s):  
Machine Vision ◽  
Camera Calibration ◽  
Pinhole Camera ◽  
Camera Model ◽  
Calibration Technique ◽  
Distortion Factor ◽  
Full Account ◽  
Practical Applications ◽  
Model Instance ◽  
Cross Platform

According to the perspective projection performed by a pinhole camera, taking full account of the lens radial distortion factor, the transformation between the camera coordinate has been analyzed. Combining with camera model instance of surface mount devices, the system uses Halcon machine vision software which has a powerful geometric and image computing functions to solve the camera calibration parameters and the accuracy of calibration results has been verified in practical applications. This method has advantages of computational efficiency and good value of cross-platform porting applications.

Sign in / Sign up

Export Citation Format

Share Document