Camera Calibration Method Research Articles

Precise Camera Calibration Method Based on Eccentricity Correction of Circular Target

Abstract Acquiring high-precision and robust calibration methods for determining the intrinsic and extrinsic parameters of cameras is essential to ensuring the accuracy of visual measurements and three-dimensional reconstruction. This paper addresses the challenge of reduced calibration accuracy in camera systems caused by eccentricity errors in the extraction of circular target feature centers. We propose a precise calibration method that corrects these eccentricity errors associated with circular targets. The method begins by solving for the homography matrix of each calibration image using the coordinates of both image points and target points. Subsequently, the homography matrix is used to generate the frontal view of the circular target, enabling the precise extraction and back-mapping of the circular target's center based on this frontal view. Finally, using the corrected coordinates of the circle center and the calibration method proposed by Zhang in the paper "A Flexible New Technique for Camera Calibration", we accurately determine the intrinsic and extrinsic parameters of the camera. Simulation experiments show the proposed calibration method aligns perfectly with ground truth without noise. In practical tests, it outperformed the checkerboard and traditional circular target methods, reducing reprojection errors by 0.0295 and 0.0176 pixels, respectively. Additionally, the cross-ratio experiment showed a 0.076% reduction in error for corrected circle center points. These results highlight significant improvements in calibration accuracy, meeting high-precision needs in visual sensing.

Universal underwater camera calibration method based on forward multilayer refractive model

Universal underwater camera calibration method based on forward multilayer refractive model

Calibration and applications of the all-sky camera at the Ali Observatory in Tibet

Abstract A high-precision calibration method for all-sky cameras has been realized using images from the Ali observatory in Tibet, providing application results for atmospheric extinction, night sky brightness, and known variable stars. This method achieves high-precision calibration for individual all-sky images, with the calibration process introducing deviations of less than 0.5 pixels. Within a 70-degree zenith angle, the calibration deviation of the images is less than 0.25 pixels. Beyond this angle, the calibration deviation increases significantly due to the sparser distribution of stars. Increasing the number of stars with zenith angles greater than 70 degrees used for calibration can improve the calibration accuracy for areas beyond the 70-degree zenith angle, reducing the calibration deviation at an 85-degree zenith angle to 0.2 pixels. Analysis of the all-sky images indicates that the atmospheric extinction coefficient at the Ali Observatory is approximately 0.20, and the night sky background brightness is about 21 magnitudes per square arcsecond, suggesting the presence of urban light pollution.

A flexible and effective calibration method for fiber viewing camera in multi-object fiber-fed telescope

Abstract The positioning accuracy of the robotic fiber positioners (RFPs) is a crucial issue because incorrect fiber placement can significantly affect the spectral quality. The fiber viewing camera (FVC) is implemented to guide the fiber positioning. However, the current camera calibration methods are inconvenient for the narrow space of the telescope. To solve this problem, an innovative calibration method is proposed in this study. Multiple small planar targets are placed on the focal plane to form a spliced large target (SLT) to calibrate the lens aberrations. Then, the home position of the RFPs is used as a variable control point (VCP) to calibrate the extrinsic parameters. Furthermore, a weighted multiple harmonic (WMH) method is proposed to obtain accurate centroids of the deformed light spot. A fiber position detection system is constructed in the laboratory. Physical experiments verify the effectiveness and flexibility of the proposed method, which has practical engineering significance. 

Round fruit picking system based on machine vision

Abstract Traditional fruit quality selection mainly relies on manual labor, which is costly and time-consuming, and the results are difficult to meet the accuracy requirements. To solve this problem, this paper proposes a fruit-picking system based on machine vision technology using MATLAB as a software tool. It takes round fruits as the research object, introducing Zhang’s camera calibration method, making the target picture reach the ideal state through image pre-processing, such as edging processing, filtering and denoising, binary processing, size eigenvalue extraction, etc. Then, the fruit size grade is evaluated by using the Sobel edge detection algorithm. The fruit color grade is evaluated using the HSV detection algorithm to assess the fruit color grade, and a feature extraction method is used to assess the relative score of the defect degree. Finally, a weighted composite score is used to evaluate the fruit quality grade. This paper also designed a visualized GUI interface. The results prove that the system can accurately and quickly perform fruit selection and quality rating.

Selection of an Appropriate Extrinsic Camera Calibration Method for Handheld Mobile Mapping Systems

Mobile mapping systems integrate multiple sensors to collect large volumes of geospatial data in motion. With the growing demand for mapping enclosed and hard-to-reach areas, there has been significant advancement in handheld mobile mapping systems utilizing SLAM (Simultaneous Localization and Mapping) technology. To ensure theirdata is efficiently usable, these systems should produce oriented images, which are essential for visualization, point cloud colorization, and monoplotting. Achieving so requiresprecise extrinsic camera calibration. This research provides a comprehensive overview of existing extrinsic camera calibration methods and evaluates their suitability for applicationin handheld mobile mapping systems. The goal is to identify a method that meets the accuracy and practical needs of these systems, facilitating more effective data processingand utilization in challenging environments.

A High-Quality and Convenient Camera Calibration Method Using a Single Image

Existing camera calibration methods using a single image have exhibited some limitations. These limitations include relying on large datasets, using inconveniently prepared calibration objects instead of commonly used planar patterns such as checkerboards, and requiring further improvement in accuracy. To address these issues, a high-quality and convenient camera calibration method is proposed, which only requires a single image of the commonly used planar checkerboard pattern. In the proposed method, a nonlinear objective function is derived by leveraging the linear distribution characteristics exhibited among corners. An algorithm based on enumeration theory is designed to minimize this function. It calibrates the first two radial distortion coefficients and principal points. The focal length and extrinsic parameters are linearly calibrated from the constraints provided by the linear projection model and the unit orthogonality of the rotation matrix. Additionally, a guideline is explored through theoretical analysis and numerical simulation to ensure calibration quality. The quality of the proposed method is evaluated by both simulated and real experiments, demonstrating its comparability with the well-known multi-image-based method and its superiority over advanced single-image-based methods.

A flexible camera calibration method for pose vision measurement system of roadheader

Cameras used for visual measurement in underground roadway construction sites are difficult to calibrate. Therefore, this study proposed an on-site camera calibration method that does not require manual intervention. First, the virtual point and line features on the plane target are constructed using pole-polar constraints. Second, a homography model based on point-and-line features is established. Finally, a combined objective function based on the dynamic weight coefficient is proposed to optimise the calibration results further. Relevant simulations and experiments showed that the maximum internal parameter error obtained using the proposed method was 0.463 %, and the maximum position estimation error of the roadheader was 30 mm. In addition, the results show that this method is superior to other methods in terms of calibration accuracy and robustness, solving the difficulty of camera calibration in underground roadway construction sites and guaranteeing high-precision pose vision measurement.

Image super-resolution based on improved ESRGAN and its application in camera calibration

Camera calibration is a key technique in the field of computer vision. The Zhang’s method is currently the most commonly used camera calibration method, but its accuracy is mainly dependent on the image quality, thusly constrained by camera hardware conditions. Considering that high-performance cameras are often expensive, a cost-effective method to improve the quality of calibration images is of great practical value. Without changing the existing hardware conditions, image super-resolution can be considered to enhance the quality of the calibration image. Image super-resolution (SR) is the process of reconstructing an image from low resolution (LR) to high resolution (HR). It can enhance the clarity and detail of the calibration images, which will be beneficial in improving the accuracy of camera calibration. However, there is very little research on the application of image super-resolution to camera calibration. Therefore, this study innovatively proposed a method and process for applying image super-resolution in camera calibration. Firstly, the ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks) was optimized and improved, followed by the proposal of the MMRSRGAN (Multi-scale Multi-adaptive-weights Residual Super-Resolution Generative Adversarial Networks). Two methods for local image super-resolution were also proposed for efficient training and testing of datasets, and two evaluation metrics based on reprojection error were proposed evaluate the effectiveness of the proposed model. Training and testing experiments of image super-resolution for camera calibration were conducted on both virtual and real image datasets. The proposed MMRSRGAN was compared with several advanced image super-resolution networks developed in recent years across multiple dimensions and indicators. The positive results have demonstrated the feasibility of applying image super-resolution to camera calibration and showcased the good performance of the MMRSRGAN.

A binocular camera calibration method based on circle detection

Camera calibration is a crucial step in binocular measurement, and the accuracy of camera calibration largely determines the measurement accuracy of binocular vision. However, the calibration accuracy of existing calibration methods is difficult to satisfy the requirements of variable calibration environments and engineering applications. Therefore, based on the principle of Zhang's calibration method, a calibration method is proposed by combining bundle adjustment and diagonal constraints of the calibration target. Firstly, the improved Canny edge extraction algorithm is used to obtain the sub-pixel center of mass of ellipse (CME). Then, the Zhang's calibration method is used to obtain the initial values of the calibration parameters. The camera calibration parameters are optimized by using the bundle adjustment. Finally, diagonal constraints are used to further optimize the extrinsic camera position parameters. The experimental results show that compared to the other excellent methods, the proposed method can significantly improve calibration accuracy and has certain engineering application value.

A calibration method for telecentric line-scan cameras with an enhanced dynamic imaging model and initial estimates derived from direct linear transformation

This study presents a calibration method for telecentric line-scan cameras, incorporating a dynamic imaging model and initial estimates derived from direct linear transformation. The enhanced dynamic imaging model includes an inclination parameter instead of velocities in three orthogonal spatial directions. It is applicable to any direction of motion, considers lens distortion, and accommodates the line sensor at an arbitrary position relative to the optical axis. Employing the enhanced camera model, the initial values of telecentric line-scan camera parameters are determined derived from direct linear transformation. Sensitivity of our method with respect to the noise level of image points and the number of pattern planes are assessed through numerical simulations, while effectiveness and robustness are validated through experiments using real-world data, achieving a reprojection error of 0.6386 pixels and a standard deviation of 0.0160 pixels. The versatility of the proposed method can be extended to measurement applications utilizing flatbed scanners.

Comparison of two 3D calibration methods for thermal imaging cameras to track bat flight paths near wind turbines

Zusammenfassung 3D Kameras mit geringer Auflösung, die in einem großen Außenvolumen eingesetzt werden sollen, benötigen große interaxiale Abstände von einigen Metern zwischen den Kameras. Das ist zum Beispiel der Fall, wenn 3D Fledermaus- Flugbahnen mit Wärmebildkameras in der Nähe von Windenergieanlagen erforscht werden, um deren Auswirkungen auf die Tiere zu untersuchen. Derartige Kamera- Systeme müssen nach erfolgter Montage der Kameras im Feld 3D kalibriert werden. Dazu haben wir zwei Methoden identifiziert, die große interaxiale Abstände ermöglichen: Eine Methode (easyWand) basiert auf Landmarken die in beiden Kameras sichtbar sind und eine weitere von uns entwickelte Methode (Batflight3D) basiert auf dem 3D Flugpfad einer Drohne. In dieser Publikation versuchen wir einen möglichst repräsentativen quantitativen Vergleich dieser Methoden, indem wir die 2D Korrespondenzen des jeweiligen Drohnenflugpfades für beide Methoden nutzen, um sie so direkt miteinander vergleichen zu können. Die Ergebnisse zeigen, dass Batflight3D für den oben genannten Anwendungsfall wesentlich genauere und auch verlässlichere Ergebnisse liefert, aber auf die Möglichkeit eines Drohnenfluges und GPS-Empfangs angewiesen ist.

Catadioptric omnidirectional thermal odometry in dynamic environment

This paper presents a catadioptric omnidirectional thermal odometry (COTO) system that estimates the six degrees of freedom (DoF) pose of a camera using only omnidirectional thermal images in visually degraded, fast-motion, and dynamic environments. First, we design and fabricate a central hyperbolic catadioptric omnidirectional thermal camera that captures surrounding thermal images with 360° horizontal field of view (FoV), and improve the omnidirectional camera model and calibration method to obtain high-precision camera intrinsic parameter. Second, we propose the epipolar curve constraint combining with omnidirectional thermal object detection to significantly reduce the interference of moving objects on pose estimation. Third, the implemented COTO pipeline consists of photometric calibration, dynamic region removal, tracking and mapping to overcome the drawbacks of photometric inconsistency and large distortion in omnidirectional thermal images. Experiments have been conducted on a total of 17 sequences of Lab, Outdoor and Driving, amounting to more than 60,000 omnidirectional thermal images of real environments. The experimental results indicate that the proposed COTO system has excellent localization accuracy and unparalleled robustness over the current state-of-the-art methods. The average localization accuracy measured by the absolute trajectory error (ATE) is less than 15 cm from the ground truth in both Lab and Outdoor sequences. In addition, COTO was the only system with complete and successful tracking in all sequences. The system can be used as an innovative localization solution, particularly in challenging environments with changes in ambient light, rapid vehicle motion, and moving object interference, which can be a difficult problem for visual odometry to solve.

A plane-based method for extrinsic calibration of RGB and depth cameras

Este artículo presenta un método, implementado en ROS2, para la calibración extrínseca de un conjunto heterogeneo de cámaras que incluyen tanto RGB como de profundidad. El método propuesto estima las poses relativas entre dichas cámaras a partir de la observacion de planos. Para ello, en primer lugar, se extraen y emparejan los vectores normales de las superficies planas de las imágenes (RGB y de profundidad). En segundo lugar, se plantea un problema de optimización que estima las rotaciones y traslaciones que minimizan los errores entre los pares de vectores normales en correspondencia. La aplicación utiliza algoritmos disponibles en librarias estándar para la extracción de planos (OpenCV, PCL) y optimización (Eigen). La eficacia y precisicón del método se ilustran en una configuración con dos cámaras RGB y una cámara de profundidad.

Implicit calibration method for underwater stereo cameras.

Underwater stereo cameras can effectively capture intricate environments with restricted accessibility, offering an appealing solution for precise perception. Stereo imaging is however susceptible to distortions caused by the refraction of incoming rays. These distortions are nonlinear and challenge the standard single viewpoint projection assumption. In this paper, we propose a data-driven implicit calibration method for underwater stereo cameras. To address the imaging characteristics and aberration distributions across different coordinates of underwater stereo cameras, we have developed the corresponding coordinates regression network and fusion strategy, thereby converting the calibration process into network-based learning. Secondly, we designed an underwater self-luminous calibration target system and the underwater corner point extraction strategy for sample dataset acquisition. We evaluated the proposed method comprehensively in terms of measurement, camera posture estimation, and 3D reconstruction, and compared it with other explicit calibration methods. The experimental results show that the proposed implicit calibration method is superior to other explicit calibration. We demonstrate with real experiments that our method enables efficient camera calibration for underwater vision applications.

Motion-error-free calibration of event camera systems using a flashing target.

Event cameras, inspired by biological vision, offer high dynamic range, excellent temporal resolution, and minimal data redundancy. Precise calibration of event camera systems is essential for applications such as 3D vision. The cessation of extra gray frame production in popular models like the dynamic vision sensor (DVS) poses significant challenges to achieving high-accuracy calibration. Traditional calibration methods, which rely on motion to trigger events, are prone to movement-related errors. This paper introduces a motion-error-free calibration method for event cameras using a flashing target produced by a standard electronic display that elicits high-fidelity events. We propose an improved events-accumulator to reconstruct gray images with distinct calibration features and develop an optimization method that adjusts camera parameters and control point positions simultaneously, enhancing the calibration accuracy of event camera systems. Experimental results demonstrated higher accuracy compared to the traditional motion-based calibration method (reprojection error: 0.03 vs. 0.96 pixels). The 3D reconstruction error remained around 0.15 mm, significantly improving over the motion-based method's 8.00 mm. Additionally, the method's adaptability for hybrid calibration in event-based stereovision systems was verified (e.g., with frame cameras or projectors).

Hybrid calibration method for a single camera stereo digital image correlation system

In single camera stereo digital image correlation systems, the existing calibration methods have problems with poor accuracy and violation of theoretical constraints, so we have proposed a hybrid calibration method. In this study, we first explain the principles of the existing single camera stereo measurement calibration methods. Then, on the basis of the classical camera calibration method combined with the characteristics of planar mirror reflection imaging, a hybrid calibration method is proposed that not only satisfies the constraints of the same intrinsic parameters but also has higher accuracy. Next, we complete the mathematical model of the hybrid calibration method and explain the specific calibration process. Finally, the experimental results of point distance reconstruction and static displacement measurement show that, compared with the existing calibration methods, the hybrid calibration method has lower error and higher reconstruction accuracy.

Evaluating Linear Coral Growth Estimation Using Photogrammetry and Alternative Point Cloud Comparison Methods

Abstract. Corals are critical reef-building organisms, providing essential habitat and ecosystem services. Tracking coral growth over time indicates coral reef health, which can be measured using various established techniques. Several coral growth-related studies have successfully applied photogrammetry to a particular coral of various types. While the focus of previous work was on standardised data processing and, to a certain degree, on the assessment of different point cloud comparison methods (Lange et al. 2022), little attention has been given to the impact of camera calibration. This study measured the annual linear extension of five Acropora spp. colonies using photogrammetry and evaluated all stages of imagery processing. A high focus was given to the analysis of the camera calibration method and the validation of camera parameters derived using an in-situ calibration of coral images with scale bars placed in the camera's field of view. We demonstrate that this method is as reliable as the calibration using a calibration frame. This study also examined the impact of the different point cloud comparison methods for Acropora spp. More specifically, the derived point clouds are compared by applying the point-to-point and point-to-model methods and manually selecting 12 coral branch tips. Histograms derived from the comparison methods were analysed and deemed a suitable and efficient alternative approach for measuring the maximum growth rate of mature colonies over shorter time periods (1 year or less).

Vortex-induced vibration dynamic characteristics monitoring with AI-based target object detection for long-span bridges

This research proposes an innovative framework for monitoring bridge Vortex-induced Vibration (VIV) dynamic characteristics utilizing AI-based machine-learning target object detection and tracking techniques with keypoint detection. Two camera calibration methods are implemented for cases with and without intrinsic and extrinsic camera setup information based on homography matrix conversion and distance-based conversion. The framework is verified on a video recording of a real-bridge VIV event and a simulation animation of bridge VIV with a peak/trough-based statistical method for calculating VIV frequency and amplitude. Accurate detection is achieved in both cases with short video durations. The framework demonstrates great potential for real-time bridge VIV monitoring, requiring minimal camera calibration and a straightforward device setup. It offers reliable and accurate results while remaining cost-effective.

An edge feature-based external calibration of monocular camera and LiDAR using in-orbit data

Multimodal perception represents a vital approach for observing non-cooperative targets in space. The accurate determination of the extrinsic parameters for monocular camera and LiDAR systems serves as the cornerstone for achieving effective space multimodal perception. However, due to factors like launch and operation, these extrinsic parameters can experience drift, thereby compromising the overall performance of the multimodal perception system. To tackle this challenge, this study introduces a novel approach: an edge feature-based external calibration method for monocular camera and LiDAR using in-orbit data. The proposed methodology involves several steps. Firstly, edges are extracted from the image, followed by distance transformation based on the edge image. The subsequent integration of the edge image and distance image yields the search scene image. Then, edge feature is extracted from the point cloud data. Finally, after constructing the objective function, genetic algorithms are used to determine precise external parameters. Simulation results underscore the effectiveness of the proposed algorithm, affirming its ability to achieve high-precision calibration results for external parameters even within intricate spatial environments.