TheCamera Calibratorapp uses a checkerboard pattern. A checkerboard pattern is a convenient calibration target. If you want to use a different pattern to extract key points, you can use the camera calibration MATLAB functions directly. SeeSingle and Stereo Camera Calibrationfor the list of functions.

You can print (from MATLAB) and use thecheckerboard patternprovided. The checkerboard pattern you use must not be square. One side must contain an even number of squares and the other side must contain an odd number of squares. Therefore, the pattern contains two black corners along one side and two white corners on the opposite side. This criteria enables the app to determine the orientation of the pattern. The calibrator assigns the longer side to be thex-direction.

To prepare the checkerboard pattern:

To calibrate your camera, follow these rules:

For best results, use at least 10 to 20 images of the calibration pattern. The calibrator requires at least three images. Use uncompressed images or images in lossless compression formats such as PNG. For greater calibration accuracy:

You can add images from multiple folders by clickingAdd imagesin theFilesection of theCalibrationtab. Select the location for the images corresponding to camera 1 using theBrowsebutton, then do the same for camera 2. SpecifySize of checkerboard squareby entering the length of one side of a square from the checkerboard pattern.

The calibrator attempts to detect a checkerboard in each of the added images, displaying an Analyzing Images progress bar window, indicating detection progress. If any of the images are rejected, the Detection Results dialog box appears, which contains diagnostic information. The results indicate how many total images were processed, and of those processed, how many were accepted, rejected, or skipped. The calibrator skips duplicate images.

To view the rejected images, clickView images. The calibrator rejects duplicate images. It also rejects images where the entire checkerboard could not be detected. Possible reasons for no detection are a blurry image or an extreme angle of the pattern. Detection takes longer with larger images and with patterns that contain a large number of squares.

TheData Browserpane displays a list of image pairs with IDs. These image pairs contain a detected pattern. To view an image, select it from theData Browserpane.

TheImagepane displays the selected checkerboard image pair with green circles to indicate detected points. You can verify that the corners were detected correctly using the zoom controls. The yellow square indicates the (0,0) origin. The X and Y arrows indicate the checkerboard axes orientation.

You can choose for the app to compute camera intrinsics or you can load pre-computed fixed intrinsics. To load intrinsics into the app, selectUse Fixed Intrinsicsin the Intrinsics section of theCalibrationtab. TheRadial DistortionandComputeoptions in theOptionssection are disabled when you load intrinsics.

To load intrinsics as variables from your workspace, clickLoad Intrinsics. For example, if thewideBaselineStereostruct contains the intrinsics for both cameras.

ld = load('wideBaselineStereo'); int1 = ld.intrinsics1 int2 = ld.intrinsics2

When the camera has severe lens distortion, the app can fail to compute the initial values for the camera intrinsics. If you have the manufacturer’s specifications for your camera and know the pixel size, focal length, or lens characteristics, you can manually set initial guesses for camera intrinsics and radial distortion. To set initial guesses, clickOptions>Optimization Options.

Thereprojection errorsare the distances, in pixels, between the detected and the reprojected points. TheStereo Camera Calibratorapp calculates reprojection errors by projecting the checkerboard points from world coordinates, defined by the checkerboard, into image coordinates. The app then compares the reprojected points to the corresponding detected points. As a general rule, mean reprojection errors of less than one pixel are acceptable.

TheStereo Calibration App显示器,以像素为单位,作为一个reprojection错误bar graph. The graph helps you to identify which images that adversely contribute to the calibration. Select the bar graph entry and remove the image from the list of images in theData Browserpane.

Reprojection Errors Bar Graph
The bar graph displays the mean reprojection error per image, along with the overall mean error. The bar labels correspond to the image IDs. The highlighted bars correspond to the selected image pair.

Select an image pair in one of these ways:

The 3-D extrinsic parameters plot provides a camera-centric view of the patterns and a pattern-centric view of the camera. The camera-centric view is helpful if the camera was stationary when the images were captured. The pattern-centric view is helpful if the pattern was stationary. You can click the cursor and hold down the mouse button with the rotate icon to rotate the figure. Click a checkerboard (or camera) to select it. The highlighted data in the visualizations correspond to the selected image in the list. Examine the relative positions of the pattern and the camera to determine if they match what you expect. For example, a pattern that appears behind the camera indicates a calibration error.

To view the effects of stereo rectification, clickShow Rectifiedin theViewsection of theCalibrationtab. If the calibration was accurate, the images become undistorted and row-aligned.

Checking the rectified images is important even if the reprojection errors are low. For example, if the pattern covers only a small percentage of the image, the distortion estimation might be incorrect, even though the calibration resulted in few reprojection errors.The following image shows an example of this type of incorrect estimation for a single camera calibration.

Consider adding more images if:

Consider removing images if the images:

You can specify 2 or 3 radial distortion coefficients by selecting the corresponding radio button from theOptionssection.Radial distortionoccurs when light rays bend more near the edges of a lens than they do at its optical center. The smaller the lens, the greater the distortion.

The radial distortion coefficients model this type of distortion. The distorted points are denoted as (x_distorted,y_distorted):

Typically, two coefficients are sufficient for calibration. For severe distortion, such as in wide-angle lenses, you can select 3 coefficients to includek₃.

When you select theCompute Skewcheck box, the calibrator estimates the image axes skew. Some camera sensors contain imperfections that cause thex- andy-axes of the image to not be perpendicular. You can model this defect using a skew parameter. If you do not select the check box, the image axes are assumed to be perpendicular, which is the case for most modern cameras.

Tangential distortion occurs when the lens and the image plane are not parallel. The tangential distortion coefficients model this type of distortion.

The distorted points are denoted as (x_distorted,y_distorted):

When you select theCompute Tangential Distortioncheck box, the calibrator estimates the tangential distortion coefficients. Otherwise, the calibrator sets the tangential distortion coefficients to zero.

SelectExport Camera Parameters>Export Parameters to Workspaceto create astereoParametersobject in your workspace. The object contains the intrinsic and extrinsic parameters of the camera and the distortion coefficients. You can use this object for various computer vision tasks, such as image undistortion, measuring planar objects, and 3-D reconstruction. SeeMeasuring Planar Objects with a Calibrated Camera. You can optionally export thestereoCalibrationErrorsobject, which contains the standard errors of estimated stereo camera parameters, by selecting theExport estimation errorscheck box.

SelectExport Camera Parameters>Generate MATLAB scriptto save your camera parameters to a MATLAB script, enabling you to reproduce the steps from your calibration session.