Camera Self-calibration Method Research Articles

Infrared Camera Array System and Self-Calibration Method for Enhanced Dim Target Perception

Infrared Camera Array System and Self-Calibration Method for Enhanced Dim Target Perception

Novel camera self-calibration method with clustering prior and nonlinear optimization from an image sequence

Novel camera self-calibration method with clustering prior and nonlinear optimization from an image sequence

A Robust Camera Self-calibration Method Based on Circular Oblique Images

Abstract. It is crucial to calibrate the camera’s intrinsic orientation elements and distortion parameters to ensure the photogrammetric accuracies. However, using nadir images to perform this task often leads to correlation between the intrinsic and extrinsic orientation elements, which will result in different camera calibration results by using different self-calibration strategies. It even has an impact on the follow-up processes and makes the product accuracy declined. To overcome this challenge, a robust camera calibration method based on circular oblique images was developed in this study. Firstly, circular oblique images with different viewing angles and camera distances were captured by unmanned aerial vehicle, following a specially designed circular flight path. Then the camera parameters were solved through the self-calibration bundle adjustment based on the circular oblique images. The experiments were carried out to compare the robustness and accuracy of nadir-image-based and circular-oblique-image-based methods. The standard deviation of focal lengths solved by different self-calibration strategies reduced from 12.99 pixels to 1.72 pixels, proving that the proposed method weakens the correlation between the intrinsic and extrinsic orientation elements and has strong robustness. The accuracy of aerial triangulation based on the camera parameters solved by the proposed method improved from 34.7 cm to 3.5cm, illustrating the effectiveness of the proposed method.

Contribution of 3D reconstruction techniques to the animated restoration of historical scenes

Abstract Amid the escalating demand for heightened realism across diverse sectors such as film, television, gaming, tourism, virtual display, and the preservation of cultural heritage, there has been a notable advancement in reverse modeling technology that facilitates the extraction of three-dimensional models from image sequences. This study focuses on the classification of 3D reconstruction for the animation restoration of historical scenes. It delves into the technical pathways for the 3D reconstruction of such scenes, employing a clustering-based Scale Invariant Feature Transform (SIFT) feature-matching acceleration algorithm for the extraction and matching of image features. Subsequently, it integrates the Bundler method of camera self-calibration with the Patch-based Multi-view Stereo (PMVS) algorithm for dense reconstruction, culminating in the assembly and testing of the reconstructed historical scene. The acceleration time of the SIFT algorithm is higher than that of the standard algorithm when the number of images is 10. The standard algorithm’s acceleration time is the longest when there are more than 10 images. When the number of images is greater than 50, the acceleration ratio of sigma=60 is greater than that of sigma=120. This indicates that the clustering-based SIFT algorithm is suitable for accelerated matching of large-scale image sets. The acceleration effect can be improved by appropriately decreasing the value of sigma. The maximum difference between the five independent vectors in model 1 and model 2 is 3.18. After scaling model 2, the difference is narrowed to[ 0.01,0.03]. The small error of the model in scenes 3~9 and the clear graphic texture indicate that the 3D reconstruction model of the historical scene designed in this paper has high accuracy and provides a model reference for the animation restoration of the historical scene.

Trinocular camera self-calibration based on spatio-temporal multi-layer optimization

In stereo vision systems with dynamically rotating cameras, the accuracy of camera self-calibration method is reduced due to the interference of space noise and mismatched features. To address this issue, a new self-calibration method for trinocular camera is proposed. Firstly, to obtain the uniformly distributed and high-quality matched feature points required for initial calibration of camera pose, according to the mapping relationship between the three-view feature matching points, a three-view grid feature support estimator is defined, and a three-view ring matching method with double-layer feature closed-loop verification is designed. Then, according to the projection relationship of spatial feature points, a new heterogeneous cross-projection optimization function based on closed-loop features is established, achieving accurate calibration of trinocular camera system. Comparison experiments of multiple scenes verify the effectiveness of the method, particularly in the low-textured scenes, where the average Sampson error ranged between 1.37e-11 and 0.061 pixel. Furthermore, the proposed method achieves higher calibration accuracy than the comparative method, which can improve the robustness of dynamic rotating cameras under spatial noise conditions.

An In-Orbit Stereo Navigation Camera Self-Calibration Method for Planetary Rovers with Multiple Constraints

In order to complete the high-precision calibration of the planetary rover navigation camera using limited initial data in-orbit, we proposed a joint adjustment model with additional multiple constraints. Specifically, a base model was first established based on the bundle adjustment model, second-order radial and tangential distortion parameters. Then, combining the constraints of collinearity, coplanarity, known distance and relative pose invariance, a joint adjustment model was constructed to realize the in orbit self-calibration of the navigation camera. Given the problem of directionality in line extraction of the solar panel due to large differences in the gradient amplitude, an adaptive brightness-weighted line extraction method was proposed. Lastly, the Levenberg-Marquardt algorithm for nonlinear least squares was used to obtain the optimal results. To verify the proposed method, field experiments and in-orbit experiments were carried out. The results suggested that the proposed method was more accurate than the self-calibration bundle adjustment method, CAHVOR method (a camera model used in machine vision for three-dimensional measurements), and vanishing points method. The average error for the flag of China and the optical solar reflector was only 1 mm and 0.7 mm, respectively. In addition, the proposed method has been implemented in China’s deep space exploration missions.

Camera Self-Calibration with GNSS Constrained Bundle Adjustment for Weakly Structured Long Corridor UAV Images

Camera self-calibration determines the precision and robustness of AT (aerial triangulation) for UAV (unmanned aerial vehicle) images. The UAV images collected from long transmission line corridors are critical configurations, which may lead to the “bowl effect” with camera self-calibration. To solve such problems, traditional methods rely on more than three GCPs (ground control points), while this study designs a new self-calibration method with only one GCP. First, existing camera distortion models are grouped into two categories, i.e., physical and mathematical models, and their mathematical formulas are exploited in detail. Second, within an incremental SfM (Structure from Motion) framework, a camera self-calibration method is designed, which combines the strategies for initializing camera distortion parameters and fusing high-precision GNSS (Global Navigation Satellite System) observations. The former is achieved by using an iterative optimization algorithm that progressively optimizes camera parameters; the latter is implemented through inequality constrained BA (bundle adjustment). Finally, by using four UAV datasets collected from two sites with two data acquisition modes, the proposed algorithm is comprehensively analyzed and verified, and the experimental results demonstrate that the proposed method can dramatically alleviate the “bowl effect” of self-calibration for weakly structured long corridor UAV images, and the horizontal and vertical accuracy can reach 0.04 m and 0.05 m, respectively, when using one GCP. In addition, compared with open-source and commercial software, the proposed method achieves competitive or better performance.

Development of a Camera Self-calibration Method for 10-parameter Mapping Function

Tomographic particle image velocimetry (PIV) is a widely used method that measures a three-dimensional (3D) flow field by reconstructing camera images into voxel images. In 3D measurements, the setting and calibration of the camera’s mapping function significantly impact the obtained results. In this study, a camera self-calibration technique is applied to tomographic PIV to reduce the occurrence of errors arising from such functions. The measured 3D particles are superimposed on the image to create a disparity map. Camera self-calibration is performed by reflecting the error of the disparity map to the center value of the particles. Vortex ring synthetic images are generated and the developed algorithm is applied. The optimal result is obtained by applying self-calibration once when the center error is less than 1 pixel and by applying self-calibration 2–3 times when it was more than 1 pixel; the maximum recovery ratio is 96%. Further self-correlation did not improve the results. The algorithm is evaluated by performing an actual rotational flow experiment, and the optimal result was obtained when self-calibration was applied once, as shown in the virtual image result. Therefore, the developed algorithm is expected to be utilized for the performance improvement of 3D flow measurements.

Analysis of Fish-Eye Lens Camera Self-Calibration

The fish-eye lens camera offers the advantage of efficient acquisition of image data through a wide field of view. However, unlike the popular perspective projection camera, a strong distortion effect appears as the periphery of the image is compressed. Such characteristics must be precisely analyzed through camera self-calibration. In this study, we carried out a fish-eye lens camera self-calibration while considering different types of test objects and projection models. Self-calibration was performed using the V-, A-, Plane-, and Room-type test objects. In the fish-eye lens camera, the V-type test object was the most advantageous for ensuring the accuracy of the principal point coordinates and focal length, because the correlations between parameters were relatively low. On the other hand, the other test objects were advantageous for ensuring the accuracy of distortion parameters because of the well-distributed image points. Based on the above analysis, we proposed, an accurate fish-eye lens camera self-calibration method that applies the V-type test object. The RMS-residuals of the proposed method were less than 1 pixel.

Robust and flexible method for calibrating the focal length of on-orbit space zoom camera

For the on-orbit space zoom camera, the camera focal length is in a constant process of change; accordingly, compared with calibrating other camera intrinsic parameters, calibrating the focal length has a practical significance for the space zoom camera. With the vanishing points obtained from the solar panel of human-made space satellites, this paper introduces a focal length self-calibration method for the on-orbit space zoom camera. First, the geometrical relationship and infinite homography of vanishing points at various camera positions are used to derive the method. To improve the accuracy and robustness performance of this approach, an optimization method is then proposed to nonlinearly optimize the camera focal length. Finally, simulation and real physical experiments demonstrate that the proposed method is flexible and accurate with good anti-noise interference and real-time capacity. The method proposed in this paper makes more realistic sense for a number of important space tasks.

Self-calibration of projective camera based on trajectory basis

To calibrate the projective camera, this paper presents a linear method for self-calibration of a projective camera based on the representing of deforming object by the trajectory basis. The trajectory basis can be modeled compactly in the domain of the Discrete Cosine Transform (DCT) basis vectors which can be predefined independent of the observed images, and the number of unknowns can be significantly reduced. At the same time, the camera self-calibration becomes a linear optimal problem, which can improve the robustness of the algorithm. The experiments with both simulate and real data show that the presented method can effectively calibrate the camera.

A LiDAR data-based camera self-calibration method

To find the intrinsic parameters of a camera, a LiDAR data-based camera self-calibration method is presented here. Parameters have been estimated using particle swarm optimization (PSO), enhancing the optimal solution of a multivariate cost function. The main procedure of camera intrinsic parameter estimation has three parts, which include extraction and fine matching of interest points in the images, establishment of cost function, based on Kruppa equations and optimization of PSO using LiDAR data as the initialization input. To improve the precision of matching pairs, a new method of maximal information coefficient (MIC) and maximum asymmetry score (MAS) was used to remove false matching pairs based on the RANSAC algorithm. Highly precise matching pairs were used to calculate the fundamental matrix so that the new cost function (deduced from Kruppa equations in terms of the fundamental matrix) was more accurate. The cost function involving four intrinsic parameters was minimized by PSO for the optimal solution. To overcome the issue of optimization pushed to a local optimum, LiDAR data was used to determine the scope of initialization, based on the solution to the P4P problem for camera focal length. To verify the accuracy and robustness of the proposed method, simulations and experiments were implemented and compared with two typical methods. Simulation results indicated that the intrinsic parameters estimated by the proposed method had absolute errors less than 1.0 pixel and relative errors smaller than 0.01%. Based on ground truth obtained from a meter ruler, the distance inversion accuracy in the experiments was smaller than 1.0 cm. Experimental and simulated results demonstrated that the proposed method was highly accurate and robust.

Efficient camera self-calibration method for remote sensing photogrammetry.

Internal parameter calibration of remote sensing cameras (RSCs) is a necessary step in remote sensing photogrammetry. On-orbit camera calibration widely adopts external ground control points (GCPs) to measure its internal parameters. However, accessible and available GCPs are not easy to achieve when cameras work on a satellite platform. In this paper, we propose an efficient camera self-calibration method using a micro-transceiver in conjunction with deep learning. A supervised learning set is produced by the micro-transceiver, where multiple two-dimensional diffraction grids are produced and transformed into multiple auto-collimating sub-beams equivalent to infinite target-point training examples. A deep learning network is used to invert the learnable internal parameters. The micro-transceiver can be easily integrated into the internal structure of RSCs allowing to calibrate them independently on external ground control targets.

On-orbit space camera self-calibration based on the orthogonal vanishing points obtained from solar panels

As one of the key steps of machine visual investigation, camera calibration is indispensable for obtaining an accurate 3D profile of measured objects and positioning the target. However, it is almost impossible to calibrate a camera on-orbit in real time, for the feature points in space are irregular. To self-calibrate camera parameters in orbit, we propose a method to calibrate the camera using the orthogonal vanishing points obtained from a solar panel, which is a common component of most man-made space satellites. Using two sets of images of orthogonal parallel ribs of a solar panel under any two positions, four vanishing points are achieved. Based on the geometrical property of the orthogonal vanishing point and the infinite homography relationship between the corresponding vanishing points under different camera positions, a camera self-calibration method is proposed, and constraint equations are established to calibrate the intrinsic parameters of the camera linearly. Aiming at restraining the influence of the noise on the calibration result, an objective function based on the inverse point characteristics of rectangular imaging is proposed, and the Levenberg–Marquardt optimization algorithm is used to nonlinearly optimize the intrinsic parameters. Simulated and experimental results show that the 2D reprojection error is 0.86 pixels. This method, therefore, has better applicability in some significant space and national defense missions, such as space rendezvous and docking, attack-defense, and on-orbit servicing. The optimization algorithm shows good anti-noise interference performance, therefore the robustness of the calibration algorithm is improved.

A METHOD FOR SELF-CALIBRATION IN SATELLITE WITH HIGH PRECISION OF SPACE LINEAR ARRAY CAMERA

At present, the on-orbit calibration of the geometric parameters of a space surveying camera is usually processed by data from a ground calibration field after capturing the images. The entire process is very complicated and lengthy and cannot monitor and calibrate the geometric parameters in real time. On the basis of a large number of on-orbit calibrations, we found that owing to the influence of many factors, e.g., weather, it is often difficult to capture images of the ground calibration field. Thus, regular calibration using field data cannot be ensured. This article proposes a real time self-calibration method for a space linear array camera on a satellite using the optical auto collimation principle. A collimating light source and small matrix array CCD devices are installed inside the load system of the satellite; these use the same light path as the linear array camera. We can extract the location changes of the cross marks in the matrix array CCD to determine the real-time variations in the focal length and angle parameters of the linear array camera. The on-orbit status of the camera is rapidly obtained using this method. On one hand, the camera’s change regulation can be mastered accurately and the camera’s attitude can be adjusted in a timely manner to ensure optimal photography; in contrast, self-calibration of the camera aboard the satellite can be realized quickly, which improves the efficiency and reliability of photogrammetric processing.

A METHOD FOR SELF-CALIBRATION IN SATELLITE WITH HIGH PRECISION OF SPACE LINEAR ARRAY CAMERA

Abstract. At present, the on-orbit calibration of the geometric parameters of a space surveying camera is usually processed by data from a ground calibration field after capturing the images. The entire process is very complicated and lengthy and cannot monitor and calibrate the geometric parameters in real time. On the basis of a large number of on-orbit calibrations, we found that owing to the influence of many factors, e.g., weather, it is often difficult to capture images of the ground calibration field. Thus, regular calibration using field data cannot be ensured. This article proposes a real time self-calibration method for a space linear array camera on a satellite using the optical auto collimation principle. A collimating light source and small matrix array CCD devices are installed inside the load system of the satellite; these use the same light path as the linear array camera. We can extract the location changes of the cross marks in the matrix array CCD to determine the real-time variations in the focal length and angle parameters of the linear array camera. The on-orbit status of the camera is rapidly obtained using this method. On one hand, the camera’s change regulation can be mastered accurately and the camera’s attitude can be adjusted in a timely manner to ensure optimal photography; in contrast, self-calibration of the camera aboard the satellite can be realized quickly, which improves the efficiency and reliability of photogrammetric processing.

Camera Self-Calibration with Varying Intrinsic Parameters by an Unknown Three-Dimensional Scene

In the present paper, we will propose a new and robust method of camera self-calibration having varying intrinsic parameters from a sequence of images of an unknown 3D object. The projection of two points of the 3D scene in the image planes is used to determine the projection matrices. The present method is based on the formulation of a non linear cost function from the determination of a relationship between two points of the scene with their opposite relative to the axis of abscise and their projections in the image planes. The resolution of this function with genetic algorithm enables us to estimate the intrinsic parameters of different cameras. The important of our approach reside in the use of a single pair of images which provides fewer equations, simplifies the mathematical complexities and minimizes the execution time of the application, the use of the data of the first image only without the use of the data of the second image, the use of any camera which makes the intrinsic parameters variable not constant and the use of a 3D scene reduces the planarity constraints. The experimental results on synthetic and real data prove the performance and robustness of our approach.

Self-calibration for a non-central catadioptric camera with approximate epipolar geometry

In spite of wide usage of catadioptric cameras, self-calibration for a non-central catadioptric camera remains very challenging work. In this paper, a novel self-calibration method for a non-central catadioptric camera is proposed. First, an approximate central projection is introduced. With a freely movable virtual viewpoint, the approximate central projection is able to compensate well the bias caused by the misalignment. Then a derived approximate epipolar geometry (AEG) for the non-central camera under the two-view case is given. Based on the AEG, a full self-calibration method using only two images is designed. Combined with an automatic initialization algorithm, the non-central catadioptric camera can be calibrated robustly without any manual intervention. Experiments on both synthetic data and real images verify the effectiveness of the proposed method.

Autonomous Star Camera Calibration and Spacecraft Attitude Determination

This paper presents two methods of star camera calibration to determine camera calibrating parameters (like principal point, focal length etc) along with lens distortions (radial and decentering). First method works autonomously utilizing star coordinates in three consecutive image frames thus independent of star identification or biased attitude information. The parameters obtained in autonomous self-calibration technique helps to identify the imaged stars with the cataloged stars. Least Square based second method utilizes inertial star coordinates to determine satellite attitude and star camera parameters with lens radial distortion, both independent of each other. Camera parameters determined by the second method are more accurate than the first method of camera self calibration. Moreover, unlike most of the attitude determination algorithms where attitude of the satellite depend on the camera calibrating parameters, the second method has the advantage of computing spacecraft attitude independent of camera calibrating parameters except lens distortions (radial). Finally Kalman filter based sequential estimation scheme is employed to filter out the noise of the LS based estimation.

Robust method for camera self-calibration by an unkown planar scene

In this paper we present a self-calibration method for a CCD camera with varying intrinsic parameters based on an unknown planar scene. The advantage of our method is reducing the number of images (two images) needed to estimate the parameters of the camera used. Moreover, self-calibration equations are related to the number of points matched (very numerous and easy to detect) rather than to the number of images, since the use of a large number of images requires high computation time. On the other hand, we base on the points matched, which are numerous, when estimating the projection matrices and homographies between the images. The latter are used with the images of the absolute conic to formulate a system of non-linear equations (self-calibration equations depend on the number of matched pairs). Finally, the intrinsic parameters of the camera can be obtained by minimizing a non-linear cost function in a two-step procedure: initialization and optimization. Experiment results show the robustness of our algorithms in terms of stability and convergence.