Camera Calibration Technology Based on Circular Points for Binocular Stereovision System

Binocular stereo vision (BSV) system has been widely used in various fields, such as intelligent manufacture, smart robot, and so on. However, the location accuracy of the current BSV still cannot fully satisfy industry requirements due to lack of a parameters optimization BSV system. In this paper, a high accuracy BSV system is proposed. This is achieved through analyzing the seven parameters of the BSV system, which are classified into two groups: system structure parameters (SSPs) and camera calibration parameters (CCPs). For the SSPs, an improved analysis model is designed to expose the possible errors caused by three parameters. Furthermore, a new correlation model among them is also proposed to analyze the errors caused by their correlation. On the other hand, for the CCPs, the orthogonal experiment model is employed for selecting the optimal combination of the four calibration parameters. Meanwhile, the weight among the four parameters is also analyzed for reducing errors. Finally, the effectiveness of our proposed method is demonstrated by a large number of experiments. It gives a useful reference to the BSV system used in applied optics research and application fields.

One of the goals of planetary exploration is to cache rock samples for subsequent return to Earth in the future Mars Sample Return (MSR) mission. This paper presents a method of binocular camera calibration. Robotic vision has become a very popular field in recent years due to the numerous promising applications it may enhance. However, errors within the cameras and in their perception of their environment can cause applications in robotics to fail. To help correct these intrinsic and extrinsic imperfections, stereo camera calibrations are performed. There are currently many accurate methods of camera calibration available; however, most or all of them are barely theoretical and difficultly practical. Because the authors' robot need to accurately approach and placement scientific objects, in this paper the authors present an image rectification method that is an extension to two-step method. There is an additional step for correcting the distorted image coordinates. The image rectification is performed and accurately compensates for radial and tangential distortions. Finally, through Matlab tool developed the results of experiment are accurate and available.

Binocular stereovision system is regularly only calibrated once, that is each camera is calibrated using a picture of the calibration pattern laid in the cross field. This methods calibration accuracy is expected to be improved. A twice calibrating method is proposed here. Each cameras extrinsic and intrinsic parameters are calibrated in different fields. The extrinsic parameters are firstly calibrated in the cross field, then the intrinsic parameters are calibrated in the close-range field. By applying the method to one binocular stereovision system built in our lab, it is demonstrated that the proposed method can improve the calibration accuracy more than 3.4dB than the once calibrating method. Its accuracy makes this novel method more suitable for high accurate target tracking application.

The consumer digital stereo camera FinePix REAL 3D W1, which was designed to take a pair of stereo images for stereo viewing, was released in August 2009. The aim of our study is to evaluate camera calibration methods for a low-cost and easy-operation system to measure dimensions of an object on a sub-meter scale only by using a pair of stereo images acquired by the camera without any controls such as control points whose spatial locations are known or a ruler which provides the scale of the object. Three camera calibration methods were evaluated by estimation errors of lengths of line segments measured without any controls. The experiment results indicate that the best calibration method would be the method that ordinary camera calibration is first executed for each imaging unit in order to estimate its image distortion model, and then alignment between two imaging units is estimated by using two sets of the exterior orientation parameters of both imaging units obtained in the ordinary camera calibration executed previously. The experiment results indicate that the suitable calibration method would be able to provide sufficiently accurate measurement results less than 0.4 pixels on an image as well.

Camera calibration using one-dimensional information and its applications in both controlled and uncontrolled environments

This paper presents a new algorithm of traditional camera calibration which is based on Extended Kalman Filter. The Extended Kalman Filter is a set of mathematical equations that provides an efficient way to estimate the state of a process. The technique of camera calibration can be divided into three kinds in practice, among which the traditional camera calibration has the highest precision. The substance of camera calibration is to figure out the intrinsic and extrinsic parameters of a camera for its further application in computer vision. In this paper, the image coordinates of the characteristics and the corresponding world coordinates are taken as the filter's inputs, the estimate value of these parameters are taken as the filter's outputs. Simulation and experiments results show the well optimization of this method.

With the increasing need for higher accuracy measurement in computer vision, the precision of camera calibration is a more important factor. The objective of stereo camera calibration is to estimate the intrinsic and extrinsic parameters of each camera. We presented a high-accurate technique to calibrate binocular stereo vision system having been mounted the locations and attitudes, which was realized by combining nonlinear optimization method with accurate calibration points. The calibration points with accurate coordinates, were formed by an infrared LED moved with three-dimensional coordinate measurement machine, which can ensure indeterminacy of measurement is 1/30000. By using bilinear interpolation square-gray weighted centroid location algorithm, the imaging centers of the calibration points can be accurately determined. The accuracy of the calibration is measured in terms of the accuracy in the reconstructing calibration points through triangulation, the mean distance between reconstructing point and given calibration point is 0.039mm. The technique can satisfy the goals of measurement and camera accurate calibration.

We present, a method for camera calibration and metric reconstruction of the three-dimensional structure of scenes with several, possibly small and nearly planar objects from one or more images. We formulate the projection of object models explicitly according to the pin-hole camera model in order to be able to estimate the pose parameters for all objects as well as relative poses and the focal lengths of the cameras. This is accomplished by minimising a multivariate non-linear cost function. The only information needed is simple geometric object models, the correspondence between model and image features, and the correspondence of objects in the images if more than one view of the scene is used. Additionally, we present a new method for the projection of circles using projective invariants. Results using both simulated and real images are presented.

From spheres images we have developed a new method for camera calibration in order to calculate with accuracy its intrinsic parameters. We prove an interesting geometric propriety about ellipses extracted from sphere images. Taking into account the lens geometrical distortion introduced by the optical system and searching a precise points detection for spheres images, permit to obtain satisfactory results.KeywordsFocal LengthCamera CalibrationSynthetic ImageIntrinsic ParameterContour PointThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Binocular vision technology is widely used in daily life, and binocular camera calibration is of great significance. Aiming at the problem that the training accuracy of the current camera calibration method based on neural network is not high enough, this paper proposes a binocular camera calibration method based on the improved particle swarm optimization BP neural network (APSO-BPNN), and introduces the adaptive particle swarm optimization algorithm to optimize the BP neural network. A binocular camera calibration model is constructed, and real data are used to verify the effectiveness and feasibility of the method. The experimental results show that the dual-target calibration method based on APSO-BPNN has higher calibration accuracy than the traditional neural network calibration method.

This passage puts forward a kind of simple and convenient three-dimensional laser measurement technology for a small volume and complex shape space data acquisition of the object. This technology mainly uses the camera self calibration method for construction of three-dimensional model algorithm and creates the measurement environment of the measuring technique in machinery and hardware camp. The result of the experiment shows that the desktop 3D laser scanning measuring instrument is convenient, and can acquire spatial location ideal data with low cost.

An improved outdoor camera calibration method based on Extended Kalman Filter (EKF) is proposed in this paper. GPS coordinate system is introduced to establish world coordinate system in the calibration procedure. According to the characteristics of GPS coordinate converted to plane coordinate and the camera calibration model, we establish the mapping relations of reference points between image plane coordinate and the world coordinate. Further, we consider the world coordinate error, set the world coordinates as the measure quantities, use the method of EKF to get the optimal estimation of intrinsic and extrinsic camera parameters, in EKF the camera's intrinsic and extrinsic parameters are set as state variables, the transformation equations of image coordinate to the world coordinate are set as the measurement equations, and we let the observed feature points' image coordinates and corresponding world coordinates as the input of the filter. Experimental results confirmed that the proposed method with high precision. Index Terms - camera calibration, GPS coordinate, EKF, world coordinate error

Stereo Vision based 3-D reconstruction is the focus of computer vision research. The essence for 3-D reconstruction is to use disparity information, which recovers the depth information from 2-D images. This paper mainly discusses 3-D reconstruction based on binocular stereo vision. Single camera could acquire images from different perspectives. The intrinsic and extrinsic parameters are calculated accurately using calibration template. On the basis of those parameters, calibration, matching and 3-D reconstruction is realized in the sense of Euclidean geometry.

Monitoring the static and dynamic displacements of large engineering structures, such as buildings and bridges, can provide quantitative information for evaluating structural safety and maintenance purposes. Camera calibration is a key process in vision-based sensor systems for remote displacement measurement. Due to the large field of view of engineering structures, conventional camera calibration methods using precise calibration boards are difficult to apply. A modified calibration method for a binocular stereo vision system based on the epipolar constraint relationship is proposed to simplify the calibration process. Due to the absence of reference points in outdoor applications, an unmanned aerial vehicle that carries a reference marker is adopted. During its flight in the field, sequential images are captured simultaneously with the left and right imaging stations. An alternative to determine the scale factor is also proposed, which provides adequate precision for camera calibration. Two important issues are discussed, including the number of reference points and their selection with regards to the depth of view. The experimental results show that the proposed method is convenient to apply in outdoor situations and can achieve high accuracy in displacement measurement.

The line structure light three-dimensional reconstruction system is a kind of three-dimensional non-contact measurement system, which has the advantages of high precision, high speed, small damage to objects and strong adaptability. Camera calibration is a major factor that constrains the accuracy of 3D measurement systems. The camera calibration is based on the pinhole imaging model, and through a series of complex calculations, the camera’s internal parameters (focal length, distortion coefficient) and external parameters (rotation matrix and translation vector). The different calibration methods use different calibration targets, which can be divided into 3D calibration targets, 2D calibration targets, and one-dimensional calibration targets according to the characteristics of the calibration targets. This paper mainly discusses: calibration content and significance, calibration methods for different targets and evaluation methods for calibration of different targets. Firstly, the content and significance of calibration are expounded. Then, according to different calibration targets, the calibration algorithm is analyzed. Finally, the calibration algorithm is analyzed and summarized, and the development trends, advantages and disadvantages of different calibration methods are pointed out.