README
======
The "Matlab Calibration Toolbox" from http://www.vision.caltech.edu/bouguetj/calib_doc/index.html was used.
Instructions for use in Octave
______________________________
The GUI doesn't seem to be functional as of Octave 2.6.4,
so we'll use the commandline interface.
Setup
-----
- In the file 'click_ima_calib.m', comment line 282:
% zoom on;
Usage
-----
>>> clear all
Replace <path to TOOLBOX_calib>:
>>> addpath("<path to TOOLBOX_calib>")
>>> cd "calib_example"
>>> data_calib
. chessboard05.jpg chessboard11.jpg chessboard17.jpg
.. chessboard06.jpg chessboard12.jpg chessboard18.jpg
chessboard01.jpg chessboard07.jpg chessboard13.jpg chessboard19.jpg
chessboard02.jpg chessboard08.jpg chessboard14.jpg chessboard20.jpg
chessboard03.jpg chessboard09.jpg chessboard15.jpg
chessboard04.jpg chessboard10.jpg chessboard16.jpg
Basename camera calibration images (without number nor suffix): chessboard
Image format: ([]='r'='ras', 'b'='bmp', 't'='tif', 'p'='pgm', 'j'='jpg', 'm'='ppm') j
Loading image 1...2...3...4...5...6...7...8...9...10...11...12...13...14...15...16...17...18...19...20...
done
>>> click_calib
Extraction of the grid corners on the images
Number(s) of image(s) to process ([] = all images) =
Window size for corner finder (wintx and winty):
wintx ([] = 5) =
winty ([] = 5) =
Window size = 11x11
Do you want to use the automatic square counting mechanism (0=[]=default)
or do you always want to enter the number of squares manually (1,other)?
Processing image 1...
Using (wintx,winty)=(5,5) - Window size = 11x11 (Note: To reset the window size, run script clearwin)
Click on the four extreme corners of the rectangular complete pattern (the first clicked corner is the origin)...
Size dX of each square along the X direction ([]=100mm) = 29
Size dY of each square along the Y direction ([]=100mm) = 29
If the guessed grid corners (red crosses on the image) are not close to the actual corners, it is necessary to enter an initial guess for the radial distortion factor kc (useful for subpixel detection)
Need of an initial guess for distortion? ([]=no, other=yes)
Corner extraction...
... (repeat for all 20 images)
done
>>> go_calib_optim
Aspect ratio optimized (est_aspect_ratio = 1) -> both components of fc are estimated (DEFAULT).
Principal point optimized (center_optim=1) - (DEFAULT). To reject principal point, set center_optim=0
Skew not optimized (est_alpha=0) - (DEFAULT)
Distortion not fully estimated (defined by the variable est_dist):
Sixth order distortion not estimated (est_dist(5)=0) - (DEFAULT) .
Initialization of the principal point at the center of the image.
Initialization of the intrinsic parameters using the vanishing points of planar patterns.
Initialization of the intrinsic parameters - Number of images: 20
Calibration parameters after initialization:
Focal Length: fc = [ 749.51098 749.51098 ]
Principal point: cc = [ 319.50000 239.50000 ]
Skew: alpha_c = [ 0.00000 ] => angle of pixel = 90.00000 degrees
Distortion: kc = [ 0.00000 0.00000 0.00000 0.00000 0.00000 ]
Main calibration optimization procedure - Number of images: 20
Gradient descent iterations: 1...2...3...4...5...6...7...8...9...10...11...12...13...14...15...16...17...18...19...20...done
Estimation of uncertainties...
done
Calibration results after optimization (with uncertainties):
Focal Length: fc = [ 714.63414 718.09482 ] ± [ 8.09474 8.08007 ]
Principal point: cc = [ 325.57555 211.17740 ] ± [ 2.33863 2.11411 ]
Skew: alpha_c = [ 0.00000 ] ± [ 0.00000 ] => angle of pixel axes = 90.00000 ± 0.00000 degrees
Distortion: kc = [ 0.02363 -0.26528 -0.00397 0.00053 0.00000 ] ± [ 0.01382 0.08046 0.00091 0.00103 0.00000 ]
Pixel error: err = [ 0.23998 0.20895 ]
Note: The numerical errors are approximately three times the standard deviations (for reference).
>>> recomp_corner_calib
Re-extraction of the grid corners on the images (after first calibration)
Window size for corner finder (wintx and winty):
wintx ([] = 5) =
winty ([] = 5) =
Window size = 11x11
Number(s) of image(s) to process ([] = all images) =
Use the projection of 3D grid or manual click ([]=auto, other=manual):
Processing image 1...2...3...4...5...6...7...8...9...10...11...12...13...14...15...16...17...18...19...20...
done
>>> go_calib_optim
Aspect ratio optimized (est_aspect_ratio = 1) -> both components of fc are estimated (DEFAULT).
Principal point optimized (center_optim=1) - (DEFAULT). To reject principal point, set center_optim=0
Skew not optimized (est_alpha=0) - (DEFAULT)
Distortion not fully estimated (defined by the variable est_dist):
Sixth order distortion not estimated (est_dist(5)=0) - (DEFAULT) .
Main calibration optimization procedure - Number of images: 20
Gradient descent iterations: 1...2...3...4...5...6...7...8...9...10...11...12...13...14...15...done
Estimation of uncertainties...done
Calibration results after optimization (with uncertainties):
Focal Length: fc = [ 714.61054 718.07119 ] ± [ 8.09003 8.07538 ]
Principal point: cc = [ 325.57678 211.17560 ] ± [ 2.33706 2.11280 ]
Skew: alpha_c = [ 0.00000 ] ± [ 0.00000 ] => angle of pixel axes = 90.00000 ± 0.00000 degrees
Distortion: kc = [ 0.02362 -0.26518 -0.00397 0.00053 0.00000 ] ± [ 0.01381 0.08040 0.00091 0.00103 0.00000 ]
Pixel error: err = [ 0.23986 0.20882 ]
Note: The numerical errors are approximately three times the standard deviations (for reference).
>>> recomp_corner_calib
Re-extraction of the grid corners on the images (after first calibration)
Window size for corner finder (wintx and winty):
wintx ([] = 5) =
winty ([] = 5) =
Window size = 11x11
Number(s) of image(s) to process ([] = all images) =
Use the projection of 3D grid or manual click ([]=auto, other=manual):
Processing image 1...2...3...4...5...6...7...8...9...10...11...12...13...14...15...16...17...18...19...20...
done
>>> est_dist(5) = 1
>>> go_calib_optim
Aspect ratio optimized (est_aspect_ratio = 1) -> both components of fc are estimated (DEFAULT).
Principal point optimized (center_optim=1) - (DEFAULT). To reject principal point, set center_optim=0
Skew not optimized (est_alpha=0) - (DEFAULT)
Main calibration optimization procedure - Number of images: 20
Gradient descent iterations: 1...2...3...4...5...6...7...8...9...10...11...12...13...14...15...16...17...18...done
Estimation of uncertainties...done
Calibration results after optimization (with uncertainties):
Focal Length: fc = [ 713.62323 717.08055 ] ± [ 8.03415 8.01983 ]
Principal point: cc = [ 325.67232 211.13183 ] ± [ 2.32230 2.08834 ]
Skew: alpha_c = [ 0.00000 ] ± [ 0.00000 ] => angle of pixel axes = 90.00000 ± 0.00000 degrees
Distortion: kc = [ 0.07761 -0.95816 -0.00395 0.00058 2.52464 ] ± [ 0.03303 0.39522 0.00090 0.00102 1.41062 ]
Pixel error: err = [ 0.23774 0.20759 ]
Note: The numerical errors are approximately three times the standard deviations (for reference).
>>> saving_calib
Saving calibration results under Calib_Results.mat
Generating the matlab script file Calib_Results.m containing the intrinsic and extrinsic parameters...
done
>>> visualize_distortions
Select "Complete distortion model" window.
>>> print ("complete-distortion-model.png", "-color", "-FHelvetica:10", "-dpng", "-S2400,1600")
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毕业设计多四旋翼飞行器SLAM-Multi-Quadrotor源码.zip (1242个子文件)
test2.mat.back_convCritVars_DepthAbsoluteRatio 67KB
test_1and2.mat.back_convCritVars_Dist 213KB
test_1and2.mat.back_trueNeg_vs_falsePos 213KB
living_room.blend 8.34MB
visualize.blend 949KB
scene_3D_points.blend 892KB
calibrate_pose_visualization.blend 679KB
visualize-BA.blend 531KB
bundle_adjust 1.77MB
triangulation.c 6KB
test.c 1KB
calib_data 65KB
Calib_Results 267KB
bundle_adjust.cpp 23KB
run_pipeline.cpp 12KB
mainpage.dox 135B
livingRoom0n.gt.freiburg 108KB
livingRoom3n.gt.freiburg 88KB
livingRoom1n.gt.freiburg 70KB
livingRoom2n.gt.freiburg 63KB
livingRoom0n.gt.freiburg_repaired 152KB
livingRoom3n.gt.freiburg_repaired 125KB
livingRoom1n.gt.freiburg_repaired 98KB
livingRoom2n.gt.freiburg_repaired 89KB
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IO.hpp 19KB
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