Scaramuzza, D., Martinelli, A. and Siegwart, R., (2006). After rectification, a perspective like view is generated. 0000054624 00000 n
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To recover the relative pose, two cameras must observe the same pattern at the same time, so the objectPoints of two cameras are the same. Useful functions to use after calibration in both MATLAB or C/C++. That means that the basename of the images is VMRImage. Use the Find center tool always BEFORE using the Calibration Refinement. Approach the checkerboard to the mirror or to the fisheye. This is due to some images are failed in the initialization step so they are not used in the final optimization. Click on Extract grid corners. Thus. or do you want to process the images individually ( [] = automatic, other = individual )? (see the next figure below). tz aj/q)5]D+3IW @{VGc^UIA^Z/tB$25uy-#12W15-U,%;j
8l7p`. By clicking on the button Calibration Refinement, the Toolbox will start the non-linear refinement of the calibration parameters, by using the Levenberg-Marquadt algorithm. You can see them. 0000058127 00000 n
The OCamCalib Toolbox GUI has to be selected (the window has to be in focus) for the interruption to work. The model is capable of modeling catadioptric cameras and fisheye cameras, which may both have very large field of view. You will see a message on the Matlab command shell: Basename camera calibration images (without number nor suffix): This message asks you to tape the basename of your image files, without the file format. The output parameters include camera parameters of two cameras and the relative pose of them. The OCamCalib Toolbox for Matlab has been successfully tested under Matlab 8.0, 8.1 2013 for Windows, MacOS and Linux. Let p be a pixel point of your image, and (u,v) its pixel coordinates with respect to the center of the omnidirectional image (see image below). The calibration results will also be visualized: Average reprojection error computed for each chessboard [pixels]: 15. Hb```f````c`Pef@ a da a If you opted for the automatic extraction then the toolbox will display this message: In this case for instance 100% of the corners were detected. Several experiments on different camera models showed that a polynomial order=4 gives the best results. If you finished the corner extraction, and you didnt get any error, then you can finally pass to the calibration phase! If during the, you didnt set the correct values for the center of the omnidirectional image, then you can use the automatic detection of the center. . For an example of camera + hyperbolic mirror see the image above. ATTENTION!!! By doing this, you allow calibration to compensate for possible misalignments between the camera and mirrors axes. , which respectively contain row and column of the center location. That is, every optical ray, which is reflected by the mirror surface, intersects into a unique point, which is called single effective viewpoint (see image below for a better understanding). 0000039381 00000 n
The mirror is rotationally symmetrical with respect to its axis. Starting refinement of INTRINSIC parameters As you will see, the last step, which concerns the refinement of the INTRINSIC parameters of the camera, is the phase requiring most of the time, but in general it should not take more than a few seconds. As outlined in [3], central omnidirectional cameras can be built by optimally combining a pinhole camera (perspective camera) with hyperbolic, parabolic, and elliptical mirrors. At last, 3D points can be generated from disparity map. So, once you estimate the image center, then you can run Calibration Refinement, which refines both all calibration parameters and the position of the center using a non-linear method.
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Make sure that there is a thick white border all around the pattern. So type: Number of squares along the X direction ([]=10) = 5, Number of squares along the Y direction ([]=10) = 6. Observe however that the checker size is ONLY used to recover the absolute positions of the checkerboards. . 0000022534 00000 n
cUW>3um:W]5#ufr:A]7C]t-IIR@ HAMtbX)R@EN=,]+2-T(p|jmOWQ2JGl8/CaA#pHbBNI;')By^>;mE]%n`4$Z(Dc'06ffj1PP+r2~mYit+kp8*U 0000016805 00000 n
Here are an example of them: It can be observed that they are well aligned. This work was conducted within the EU Integrated Project COGNIRON ("The Cognitive Robot Companion") and was funded by the European Commission Division FP6-IST Future and Emerging Technologies under Contract FP6-002020.
An alternative method to enlarge the camera field of view without using mirrors consists in adding a fisheye lens above the camera CCD. 0000036276 00000 n
Iteration 123456789 At the end, the Toolbox recomputes all calibration parameters for the new position of the center, and outputs the new coordinates of the center. Anyway, this just requires a little more patience. When you have zoomed in, press ENTER. By clicking on the button Reproject on images, the Toolbox will reproject the all grid corners according to the new calibration parameters just estimated. 0000008583 00000 n
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This function is very useful if during the extraction of grid corners you did some mistakes, or the automatic corner detector did. 0000005001 00000 n
Observe however that the checker size is ONLY used to recover the absolute positions of the checkerboards. For doing it, just click on the button. Cameras using fisheye lenses are not in general central systems, but they very well approximate the single view point property. But lets go on. Just press ENTER if you want, or press another number if you do not. But for the intrinsic parameters THIS IS NOT needed so you can even just leave this field empty. K, D, xi are camera parameters. Finally, I want to thank all the users who sent me feedback since the first release of this toolbox in 2005! Here rvec and tvec are the transform between the first and the second camera. 0000022874 00000 n
2. The variable disMap is the disparity map computed by cv::StereoSGBM from imageRec1 and imageRec2. Our model describes function f() by means of a polynomial, whose coefficients are the calibration parameters to be estimated. No.PR00704), In this paper we demonstrate that all single viewpoint catadioptric projections are equivalent to the composition of central projection to the sphere followed by a point projection from the sphere to, The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a. Coming back to our example, accept the suggested values by pressing ENTER. Starting refinement of EXTRINSIC parameters Optimizing chessboard pose 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. , Martinelli, A. and Siegwart, R., (2006). A nonlinear omnidirectional camera model is designed to project the probabilistic map elements with uncertainty manipulation and extract image features in the vicinity of corresponding projected curves. 0000046129 00000 n
Then, it refines the camera intrinsic parameters (i.e. The calibration supports some features, flags is a enumeration for some features, including: Your can specify flags to fix parameters during calibration. The second and most important reason for doing this is that it helps the automatic detection of the center of the omnidirectional image. idx is a CV_32S Mat that stores indices of images that are really used in calibration. Next step is to extract corners from calibration pattern. Rufli, M., Scaramuzza, D., and Siegwart, R. (2008), Automatic Detection of Checkerboards on Blurred and Distorted Images, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008), Nice, France, September 2008. For checkerboard, use OpenCV function cv::findChessboardCorners; for circle grid, use cv::findCirclesGrid, for random pattern, use the randomPatternCornerFinder class in opencv_contrib/modules/ccalib/src/randomPattern.hpp. ). The calibration performed by the OCamCalib Toolbox is based on the following hypotheses: The camera-mirror system possesses a single effective viewpoint (see section 18 for a definition), or also a quasi single viewpoint. As outlined in [3], central omnidirectional cameras can be built by optimally combining a pinhole camera (perspective camera) with, mirrors. Load the data by. You will receive the following message: If you read the paper in [1], which describes the calibration procedure, you know what this parameter is, if you did not this parameter permits you to choose the maximum order of the polynomial which approximates the function that back projects every pixel point into the 3D space. Colors refer to the different images of the checkerboard. The optimization is performed by attempting to minimize the sum of squared reprojection errors. Go to section 6 (Calibration). This routine requires you to use a checkerboard pattern with white large border. In this case, the Toolbox will in a first stage take as center the point (height/2, width/2). With the new version of the toolbox this operation is done completely automatically. Use 'plus' operator to set multiple features. A detailed introduction to this model is in section 19 of this Tutorial. Conversely, if the resolution of your pictures is very high (up to 5 Mega pixels!) You will have something like this: Using (wintx,winty)=(8,8) - Window size = 17x17. So, make sure to preserve the point correspondences. 0000032296 00000 n
Do not care about this because later you will use the button, to find the correct position of the center! That is: So the parameters to estimate are: a0, a1, a2, a3, a4, Actually, the OcamCalib Toolbox asks to specify the polynomial degree you want to use. [1 2 3], [] = all images) =. 0000043486 00000 n
If you are not satisfied with the results obtained, try to decrease or increase the polynomial order. If you are lucky the toolbox will automatically detect 100% of the corners in all the checkerboards. The Calibration Refinement tool requires the Matlab Optimization Toolbox, in particular the function lsqnonlin, which you should have by default. PhD Thesis advisor: Prof. Roland Siegwart. 0000007930 00000 n
The calibration parameters are the variable ocam_model.ss. In the following figure you can see a the sample images of the checkerboard I will use in this tutorial: Here are other sample images captured with a fisheye camera with 190-degree field of view. By doing so, the shape of the cursor changes into a square, meaning that you are in the click mode. 19.3 The Omnidirectional camera model: complete model explanation. Calibrating an omnidirectional camera implies finding the relation between a given 2D pixel point p and the 3D vector P emanating from the mirror effective viewpoint (see figure below). Omnidirectional Camera Calibration Toolbox for Matlab (for Windows, MacOS & Linux), Includes Automatic Corner Extraction and undistortion functions (Matlab & C/C++), This toolbox is currently used by NASA, PHILIPS, BOSCH, DAIMLER, Please report any bug, question, suggestion to the Google group: ocamcalib-toolbox (at) googlegroups (dot) com, This Toolbox was partially inspired by the "Caltech Calibration Toolbox" by Jean-Yves Bouguet. X coordinate (along height) of the omnidirectional image center = ([]=384) =, Y coordinate (along width) of the omnidirectional image center = ([]=512) =. Then, click on the button Read names. For images with very large distortion, the longitude-latitude rectification does not give a good result, but it is available to make epipolar constraint in a line so that stereo matching can be applied in omnidirectional images. You can compute they for yourself if you know the physical size of your pattern. 0000006713 00000 n
Because the OcamCalib Toolbox is able to automatically determine the location of the center, you can just leave this field empty by pressing ENTER. Now use your favorite image capture program to take the images with your camera. For the purpose of this tutorial, it's recommended to choose the individual image processing. from. To load and save your calibration results, just click on the corresponding buttons. Note: To have a better result, you should carefully choose Knew and it is related to your camera. You can see them here. , and OcamCalib Toolbox will start an iterative method for computing the image center, which minimizes the reprojection error of all grid points. The second and most important reason for doing this is that it helps the automatic detection of the center of the omnidirectional image. 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE). The calibration parameters are the variable. The file opencv_contrib/modules/ccalib/tutorial/data/omni_calib_data.xml stores an example of objectPoints, imagePoints and imageSize. The red crosses are the grid corners you clicked on, while the rounds are the grid corners reprojected onto the image, after calibration. Save 3D points in objectPoints, similar to imagePoints, it can be std::vector
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Camera lens distortion has not been included because omnidirectional cameras using mirrors usually need large focal length to focalize the image on the mirror. Copy all the images into OCamCalib folder. For instance, the first selected point of image 1 has to be the first selected point of image 2, 3, and so on. ]vsgO[pRbk_rwJKmu&u6;474:vu:g8L(4`3)bbjhcSU5czji3s}=/k^f+c/HXxi%1FfN&VBb">rL.be;''xBI[&FeNLlBb\Vf]9@>sd3x[|o)3ExXYB2;]XYz-9LM,
yb Note the size of objectPoints and imagePoints must be the same because they are corresponding to each other. This white border is needed by the Automatic Checkerboard Extraction tool to facilitate the corner extraction.
Once you have chosen the polynomial order, the calibration is performed very quickly because a least square linear minimization method is used. The toolbox does not require a priori knowledge about the mirror shape. 0000031264 00000 n
Omnidirectional Vision: from Calibration to Robot Motion Estimation, ETH Zurich, PhD Thesis no. images extracted from video data) or if the corner extraction is known to work well for the calibration data set. , the Toolbox will start the non-linear refinement of the calibration parameters, by using the Levenberg-Marquadt algorithm. The function we want the calibration to estimate is the function, which maps an image point p into its corresponding 3D vector P . Committee members: Prof. Patrick Rives (INRIA Sophia Antipolis), Prof. Luc Van Gool (ETH Zurich). Many thanks to you all!
RRfin). Please read this tutorial very carefully before contacting me for help. 0000017059 00000 n
However, for fisheye camera calibration modeling, we demonstrate that the spherical projection model is more appreciate for nonlinear image system. 0000052655 00000 n
Omnidirectional images have very large distortion, so it is not compatible with human's eye balls. 0000040216 00000 n
You will see a message on the Matlab command shell: For example, the images that you will find in the OcamCalib Toolbox are of the type: . Use of the automatic image selection is recommended for large calibration data sets (e.g. Press ENTER to continue. The grid corner extraction will continue until you process the whole set of images you selected at the beginning. For example, the images that you will find in the OcamCalib Toolbox are of the type: VMRImage0.gif, VMRImage1.gif VMRImage9.gif. , the Toolbox will attempts to recompute the positions of every corner point you clicked on, by using the reprojected grid as initial guess locations for the corners. It is a basic problem of computer vison. However, if you desire, you can optionally specify the location of the center and refine it later with. the internal focus of the hyperbola). In many cases even 100%. Because the camera and mirror axes are supposed to be perfectly aligned, observe that x and y are proportional to u and v respectively. , Actually, the OcamCalib Toolbox asks to specify the polynomial degree you want to use. %PDF-1.6
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numDisparities is the max disparity value and SADWindowSize is the window size of cv::StereoSGBM. 0000060405 00000 n
Corners, the Toolbox will attempts to recompute the positions of every corner point you clicked on, by using the reprojected grid as initial guess locations for the corners. This variable contains the polynomial coefficients of function F. I would like to recall that F has the following form: Where, for instance, I used a 4th order polynomial, and is the distance from the center of the omnidirectional image, measured in pixels. Actually, the more one augments polynomial order the more the accuracy of calibration increases. The xi is a single value variable of Mei's model. In this tutorial we set the value to 4. First, it refines the extrinsic camera parameters, that is, the rotation and translation matrices of each checkerboard with respect to the camera (i.e. 0000052287 00000 n
(see sample images below). Moreover, in processing the remaining images, be careful to preserve the same correspondences of clicked points. Before processing the next image, the toolbox will change the numbering of the points and display the orientation of the x-y axes and the axis origin. If you click on the Analyse error button you can see the distribution of the reprojection error of each point for all the checkerboards. Our omnidirectional camera model treats the imaging system as a unique compact system; that is, it does not care if you are using a mirror or a fisheye lens in combination with your camera. Size dX of each square along the X direction ([]=30mm) = 30, Size dY of each square along the Y direction ([]=30mm) = 30. ,:> 1. The window is the size (2*winx+1)*(2*winy+1). button to export the calibration results to the file calib_results.txt. Then, it will ask you to type the image format. The type can also be std::vector
As the extrinsic and intrinsic parameters are not independent, the refinement may need a few iterations to converge to a solution that minimizes both intrinsic and extrinsic parameters. RECTIFY_LONGLATI: rectify to longitude-latitude map like a world map of the earth. In fact, the Toolbox is able to provide an optimal solution even when the single view point property is not perfectly verified (for instance when the camera optical center is not exactly in the focus of the hyperbola or also for spherical mirrors). 0000045033 00000 n
to display the reprojection error or the calibration results. Download the pattern from here, print it out and attach on a flat rigid surface. For a catadioptric camera to be a central system, the following arrangements have to be satisfied: The camera optical center (namely the center of the lens) has to coincide with the focus of the hyperbola. The point cloud is stored in pointCloud, which is a 3-channel or 6-channel cv::Mat. First, it requires the user collect a few pictures of a checkerboard shown at different positions and orientations. To facilitate the corner extraction while clicking, the toolbox takes advantage of a corner detector to interpolate the best position of the grid corner around the point you clicked on.
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Now detect image corners for both cameras as discussed above to get imagePoints1 and imagePoints2. Furthermore, the quality of the result obtained justifies the wasted time! This module includes calibration, rectification and stereo reconstruction of omnidirectional camearas. RECTIFY_PERSPECTIVE: rectify to perspective images, which will lose some filed of view. At the end of calibration, the Toolbox displays the following graph, which shows the plot of function F, and the plot of angle THETA of the corresponding 3D vector with respect to the horizon. Note, if at any time you would like to modify the coordinates of the center, you can simply modify the value of the variables ocam_model.xc and ocam_model.yc, which respectively contain row and column of the center location.
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