Chessboard Corner Research Articles

Accurate dynamic vision system calibration using circular control points

Dynamic vision system (DVS) is a high time resolution camera. Due to the lack of standard frame pipeline, DVS cannot directly detect traditional control points (such as chessboard corners and circle centers). Two methods based on blinking pattern and calibration panel motion are proposed in this paper to solve the problem. The detection accuracy of blinking circular control points on the Light-Emitting Diodes (LED) is greatly improved by the proposed event frame convertor and the root mean square (RMS) is less than 0.04. Besides, this paper presents an incomplete grid detection method to increase the detection success ratio (0.97) and an effective event frames selection strategy to decrease the RMS (0.15). The results show that the standard deviation of both methods is substantially reduced compared to the conventional methods.

Calibration Method for Fisheye Camera Based on Multi-checkerboard Detection

This paper proposes a fisheye camera calibration method based on multiple chessboard detection. To address the complexity and multiple-frame requirement of traditional calibration methods, this method detects multiple chessboard corner points using libcbdetect algorithm and obtains pixel coordinates. Then, it uses a depth-first search algorithm to obtain the world coordinates of the chessboard corners and calculates the homography matrix based on the world-pixel coordinates pair through RANSAC algorithm. Finally, the undistorted image is transformed into a bird's eye view (BEV) using the obtained homography matrix. This method is simple and effective, and can improve the accuracy of lane keeping functions, which is of practical significance in autonomous driving applications.

Chessboard Corner Detection Based on EDLines Algorithm.

To improve the robustness and accuracy of the corner-detection algorithm, this paper proposes a camera-calibration method based on the EDLines algorithm for the automatic detection of chessboard corners. The EDLines algorithm is initially used to perform straight-line detection on the calibration image. The features of the broken straight lines at the corners are then used to filter the straight lines and remove the background straight lines outside the chessboard. The pixels in the rectangular area around the filtered straight line are sorted by the gray gradient. After using the sorted results to fit the straight line, the coordinates of the intersection of the straight lines are taken as the initial coordinates of the corners and perform subpixel optimization on them. Finally, the corner points are sorted by the conversion between pixel-coordinate systems. The camera exposure time changes and complex imaging-background experiments show that the algorithm has no missed detection and redundancy in corner detection. The average reprojection error is found to be less than 0.05 pixels, which can be used in actual calibration.

Extrinsic Calibration between Camera and LiDAR Sensors by Matching Multiple 3D Planes.

This paper proposes a simple extrinsic calibration method for a multi-sensor system which consists of six image cameras and a 16-channel 3D LiDAR sensor using a planar chessboard. The six cameras are mounted on a specially designed hexagonal plate to capture omnidirectional images and the LiDAR sensor is mounted on the top of the plates to capture 3D points in 360 degrees. Considering each camera–LiDAR combination as an independent multi-sensor unit, the rotation and translation between the two sensor coordinates are calibrated. The 2D chessboard corners in the camera image are reprojected into 3D space to fit to a 3D plane with respect to the camera coordinate system. The corresponding 3D point data that scan the chessboard are used to fit to another 3D plane with respect to the LiDAR coordinate system. The rotation matrix is calculated by aligning normal vectors of the corresponding planes. In addition, an arbitrary point on the 3D camera plane is projected to a 3D point on the LiDAR plane, and the distance between the two points are iteratively minimized to estimate the translation matrix. At least three or more planes are used to find accurate external parameters between the coordinate systems. Finally, the estimated transformation is refined using the distance between all chessboard 3D points and the LiDAR plane. In the experiments, quantitative error analysis is done using a simulation tool and real test sequences are also used for calibration consistency analysis.

360-deg shape measurement system based on three-dimensional orientation target

We present a system for 360-deg shape measurement based on three-dimensional (3-D) orientation target. The measurement system mainly includes a CCD camera, digital light projector, 3-D orientation target, and the measured object. The global coordinates for all chessboard corners of the target are calculated based on close-range photogrammetry, for which the relative orientation between camera stations is discussed in detail. According to the homography matrix between the checkerboard and its image plane, the position and orientation of the measuring camera relative to the target are determined. A dot-matrix structured light is designed and projected onto the measured object surface. In each image of the structured light deformed by the object shape, the pixel coordinates of the dot-centers are accurately calculated by the ellipse-fitting algorithm, and then they are all sorted in the same order by the beads-stringing matching method. In the experiment, the designated front, rear, left, and right surfaces of a reference object are measured, and their separate point clouds are automatically stitched together by the 3-D orientation target. Compared with coordinate measurement machine data, the accuracy of the system is demonstrated; the maximum deviations are +1.257 / − 0.977 mm, and the standard deviation is 0.124 mm.

Plane chessboard-based calibration method for a LWIR ultra-wide-angle camera.

A method is proposed to calibrate the long-wave infrared ultra-wide-angle camera in this paper. In addition, a novel calibration chessboard is designed and an advanced chessboard corner positioning method is adopted to improve the calibration precision. The designed calibration chessboard can achieve high thermal infrared contrast and exhibits outstanding stability, which is made of a thermoelectric semiconductor refrigeration device. The proposed subpixel corner positioning method can accurately locate the corners on the calibration chessboard according to the characteristics of the infrared image and the checkerboard pattern. Both the principle of the proposed infrared chessboard and the subpixel corner positioning procedure were presented, and the calibration experiment showed that the mean reprojection error and the root mean square error were reduced to 0.32 pixel and 0.39 pixel, respectively. Comparison studies were also performed to verify the calibration effect of the proposed method, and the possibilities of camera calibration error of the proposed method were analyzed.

Sub-Pixel Chessboard Corner Localization for Camera Calibration and Pose Estimation

This work describes a novel approach to localize sub-pixel chessboard corners for camera calibration and pose estimation. An ideally continuous chessboard corner model is established, as a function of corner coordinates, rotation and shear angles, gain and offset of grayscale, and blurring strength. The ideal model is evaluated by a low-cost and high-similarity approximation for sub-pixel localization, and by performing a nonlinear fit to input image. A self-checking technique is also proposed by investigating qualities of the model fits, for ensuring the reliability of addressing perspective-n-point problem. The proposed method is verified by experiments, and results show that it can share a high performance. It is also implemented and examined in a common vision system, which demonstrates that it is suitable for on-site use.

Automatic chessboard corner detection method

Chessboard corner detection is a necessary procedure of the popular chessboard pattern-based camera calibration technique, in which the inner corners on a two-dimensional chessboard are employed as calibration markers. In this study, an automatic chessboard corner detection algorithm is presented for camera calibration. In authors' method, an initial corner set is first obtained with an improved Hessian corner detector. Then, a novel strategy that utilises both intensity and geometry characteristics of the chessboard pattern is presented to eliminate fake corners from the initial corner set. After that, a simple yet effective approach is adopted to sort the detected corners into a meaningful order. Finally, the sub-pixel location of each corner is calculated. The proposed algorithm only requires a user input of the chessboard size, while all the other parameters can be adaptively calculated with a statistical approach. The experimental results demonstrate that the proposed method has advantages over the popular OpenCV chessboard corner detection method in terms of detection accuracy and computational efficiency. Furthermore, the effectiveness of the proposed method used for camera calibration is also verified in authors' experiments.

Comprehensive Calibration of Structured Light Measurement System Using Planar Chessboard

For producing dense 3D reconstructed coordinates in a 360º view, comprehensive calibration of structured light measurement system is proposed to calibrate the camera, laser and turntable using planar chessboard. The camera captures images containing the chessboard placed on the turntable with laser stripe projecting onto it. Firstly, camera model is determined by using multiple views of chessboard positioned differently; secondly, laser plane is estimated with the 3D reconstructed coordinates of laser stripes viewed at various angles; finally, rotation axis is decided under the constraint of each circular motion trajectory of chessboard corners from different perspectives. Experimental results demonstrate that the proposed method is effective for estimating the parameters of camera model, laser plane and rotation axis at a high accuracy.

Lens distortion correction based on one chessboard pattern image

This paper proposes a detection method of chessboard corner to correct camera distortions -including radial distortion, decentering distortion and prism distor- tion. This proposed method could achieve high corner detection rate. Then we used iterative procedure to optimize distortion parameter to minimize distortion residual. In this method, first, non-distortion points are evaluated by four points near image center; secondly, Levenberg-Marquardt nonlinear optimization algorithm was adopted to calculate distortion parameters, and then to correct image by these parameters; thirdly, we calculated corner points on the corrected image, and repeated previous two steps until distortion parameters converge. Results showed the proposed method by iterative proce- dure can make the impact of slight distortion around image center negligible and the average of distortion residual of one line is almost 0.3 pixels.

A two-step calibration method of a large FOV binocular stereovision sensor for onsite measurement

As there are some shortages with using both standard calibration method and self-calibration method to calibrate a large Field of View (FOV) binocular stereovision sensor, a two-step calibration method of a large FOV binocular stereovision sensor for onsite measurement is proposed. Furthermore, in order to improve the calibration efficiency, a novel method of corner detection based on region of interest (ROI) is also proposed to quickly detect the chessboard corner in the image of a small calibration target. Compared with the other two methods, the proposed method is more suitable for rapid onsite structure–parameter calibration of a large FOV binocular stereovision sensor. The experiments show that the onsite calibration process is efficient, and the average value of 10 measurements for a distance of 1071mm is 1071.03mm with a RMS less than 0.1mm. Moreover, the application results indicate that the measurement accuracy of the stereovision prototype system calibrated by this method is improved by 13.56% compared with the other from standard calibration method.

Distortion Correction for the Gun Barrel Bore Panoramic Image

Single Reflector Panoramic Imaging System (SRPIS) has been widely used because of its advantages such as simple structure, fast imaging, integration and miniaturization. It can observe objects around the reflector mirror, which fits for the quality inspection of gun barrel bore. However, its images often suffer from serious distortions in radial and tangential directions. Therefore, to ensure the accuracy of captured images, distortion must be eliminated. In this paper, a distortion correction method is proposed based on the imaging characteristics of SRPIS. Firstly the relationship between the height of a certain point on the gun barrel bore and the radius of image point is derived. Then the correction model is built based on the relationship. Aiming at the captured annular image, a new chessboard corner detection algorithm is proposed. The correction parameters are obtained by using the algorithm according to the labeled image. The real experiment results demonstrate that the correction effects of radial and tangential distortions are satisfactory. The error is controlled at sub-pixel level.

Chessboard corner detection under image physical coordinate

The existing chessboard corner detection algorithms cannot be used in complex scenes because distortion might occur as a result of overexposure or low resolution, among other factors. This distortion hinders the precise detection of the chessboard corners using the previous methods. To address this issue, we proposed a chessboard corner detector based on image physical coordinates and a round template. The physical coordinates allowed our detected chessboard corners to reach the subpixel-level after only one step. We first covered the distorted chessboard corners by utilising the morphological dilation. Then, we employed our round template to pass through the dilated image and ultimately determine the chessboard corner coordinate by analysing the grey distribution of the traversed round template and calculating the centroids of redundant points. The experimental results showed that our algorithm performs better than other algorithms in both simple backgrounds and complex scenes. By applying our detector to camera calibration, we obtained a smaller re-projection error, thereby proving the validity of our proposed detector.

采用环形模板的棋盘格角点检测

Over exposure and camera lens distortion often result in the separation and local area asymmetry of a chessboard corner and the existing chessboard corner detection algorithms can not extract the corner information in such conditions accurately.Therefore,this paper proposed a detecting algorithm based on the circular template under an image coordinate.The symmetry and interchangeability needed by gray distribution of the local area for the chessboard corner were analyzed,and the properties of a convoluted image for the circular template were obtained.On the basis of the properties,the chessboard corner was defined and extracted.Finally,the symmetry of local redundant corner distribution was used to remove the redundant corner to improve the corner detection accuracy and to allowed the extracted accuracy of the corner to sub-pixel level in merely one step by employing the image coordinate.Experiment results show that our algorithm can achieve better results in over exposure and lens distortion both at simple backgrounds and complex scenes,and it is characterized by higher operation speed and smaller errors.Applying proposed algorithm to a camera calibration,a re-projection error less than 0.3 pixels is obtained.

A New Camera and Projector Calibration Method Based on Discrete Computation

This paper presents a novel method for the calibration of camera and projector based on discrete computation, which uses a linear moving chessboard and project plane to get a calibration database for camera and projector. The camera database contains multi-layers data of the captured chessboard corners and their corresponding practical coordinates in the system. Then by using this database and interpolation algorithm, we can calculate the accurate position for each given pixel position in the captured image. After this we change the chessboard with a white scene and project a chessboard image to the plane. By capturing the projected image and computing the practical position for the corners in the image, we can get a relationship between practical coordinates and the projected image pixels. So another database for the project image and the practical coordinates is established. By using these two databases, we can define the light line for the pixels in a camera captured image and projected image, which can be used for 3D scanning system and other industrial systems, like mask-image projection stereo lithography rapid prototyping system.