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Stereo calibration baseline mm

Hi, I calibrated my stereo webcam with a chessboard and:

stereoCalibrate(object_points, imagePoints1, imagePoints2, CM1, D1, CM2, D2, img1.size(), R, T, E, F, cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5), CV_CALIB_SAME_FOCAL_LENGTH | CV_CALIB_ZERO_TANGENT_DIST);

I need to know the baseline of the stereo camera in meters, so I look inside the T matrix. The first element of the T matrix is -1.6952669833501108e+00, the problem is that when I measure physically the distance between sensors with a ruler is approximately 4 cm, that is 0.04 m, what is the metrics of the elements in the T matrix? Therefore, I don't know the physical size of the pixels, because I don't know the sensor used.

Stereo calibration baseline mm

Hi, I calibrated my stereo webcam with a chessboard and:

stereoCalibrate(object_points, imagePoints1, imagePoints2, CM1, D1, CM2, D2, img1.size(), R, T, E, F, cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5), CV_CALIB_SAME_FOCAL_LENGTH | CV_CALIB_ZERO_TANGENT_DIST);

I need to know the baseline of the stereo camera in meters, so I look inside the T matrix. The first element of the T matrix is -1.6952669833501108e+00, the problem is that when I measure physically the distance between sensors with a ruler is approximately 4 cm, that is 0.04 m, what is the metrics of the elements in the T matrix? Therefore, I don't know the physical size of the pixels, because I don't know the sensor used.

%YAML:1.0 CM1: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 6.7035296112450442e+02, 0., 3.0366775716427702e+02, 0., 6.7334702239218382e+02, 2.3446212435423504e+02, 0., 0., 1. ] CM2: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 6.7035296112450442e+02, 0., 3.0235229497623436e+02, 0., 6.7334702239218382e+02, 2.3428252297251004e+02, 0., 0., 1. ] D1: !!opencv-matrix rows: 1 cols: 5 dt: d data: [ -2.7243204402554655e-01, 6.7461566940160000e-01, 0., 0., -1.1338340048806972e+00 ] D2: !!opencv-matrix rows: 1 cols: 5 dt: d data: [ -2.9489669516506523e-01, 8.7704813783022018e-01, 0., 0., -1.7809117056153765e+00 ] R: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 9.9994998218891662e-01, 1.8032584564876427e-03, -9.8377527578461001e-03, -1.7978260373390045e-03, 9.9999822653927839e-01, 5.6101678905229378e-04, 9.8387469692470843e-03, -5.4330216016358439e-04, 9.9995145076190473e-01 ] T: !!opencv-matrix rows: 3 cols: 1 dt: d data: [ -1.6952669833501108e+00, -8.3183862355057793e-04, 1.6553031717676806e-02 ] E: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 2.1575221682576832e-05, -1.6552550421804146e-02, -8.4108476712251815e-04, 3.3231506665764049e-02, -8.9119281968270958e-04, 1.6950218347962700e+00, 3.8795921397114311e-03, -1.6952624768406708e+00, -9.5925666229836515e-04 ] F: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 6.5486525518936608e-08, -5.0017984753281888e-05, 9.9960823121244876e-03, 1.0041793870914565e-04, -2.6810044647830857e-06, 3.4036589992213000e+00, -1.5652165011118380e-02, -3.4182603874751876e+00, 9.9999999999999989e-01 ] R1: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 9.9980525157248468e-01, 2.2991356175347437e-03, -1.9600329167972070e-02, -2.2937864080306968e-03, 9.9999732564029009e-01, 2.9539157402834107e-04, 1.9600955894930411e-02, -2.5037507834536546e-04, 9.9980785145963180e-01 ] R2: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 9.9995221263036305e-01, 4.9065951283155365e-04, -9.7637958235422401e-03, -4.9332403320461707e-04, 9.9999984173287226e-01, -2.7049145825487527e-04, 9.7636615590471869e-03, 2.7529524731464261e-04, 9.9995229642492811e-01 ] P1: !!opencv-matrix rows: 3 cols: 4 dt: d data: [ 6.0327398477959753e+02, 0., 3.0294886016845703e+02, 0., 0., 6.0327398477959753e+02, 2.3173668479919434e+02, 0., 0., 0., 1., 0. ] P2: !!opencv-matrix rows: 3 cols: 4 dt: d data: [ 6.0327398477959753e+02, 0., 3.0294886016845703e+02, -1.0227593432896966e+03, 0., 6.0327398477959753e+02, 2.3173668479919434e+02, 0., 0., 0., 1., 0. ] Q: !!opencv-matrix rows: 4 cols: 4 dt: d data: [ 1., 0., 0., -3.0294886016845703e+02, 0., 1., 0., -2.3173668479919434e+02, 0., 0., 0., 6.0327398477959753e+02, 0., 0., 5.8984939980032047e-01, 0. ]

Stereo calibration baseline mm

Hi, I calibrated my stereo webcam with a chessboard and:

stereoCalibrate(object_points, imagePoints1, imagePoints2, CM1, D1, CM2, D2, img1.size(), R, T, E, F, cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5), CV_CALIB_SAME_FOCAL_LENGTH | CV_CALIB_ZERO_TANGENT_DIST);

I need to know the baseline of the stereo camera in meters, so I look inside the T matrix. The first element of the T matrix is -1.6952669833501108e+00, the problem is that when I measure physically the distance between sensors with a ruler is approximately 4 cm, that is 0.04 m, what is the metrics of the elements in the T matrix? Therefore, I don't know the physical size of the pixels, because I don't know the sensor used.

%YAML:1.0 CM1: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 6.7035296112450442e+02, 0., 3.0366775716427702e+02, 0., 6.7334702239218382e+02, 2.3446212435423504e+02, 0., 0., 1. ] ]

CM2: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 6.7035296112450442e+02, 0., 3.0235229497623436e+02, 0., 6.7334702239218382e+02, 2.3428252297251004e+02, 0., 0., 1. ] ]

D1: !!opencv-matrix rows: 1 cols: 5 dt: d data: [ -2.7243204402554655e-01, 6.7461566940160000e-01, 0., 0., -1.1338340048806972e+00 ] ]

D2: !!opencv-matrix rows: 1 cols: 5 dt: d data: [ -2.9489669516506523e-01, 8.7704813783022018e-01, 0., 0., -1.7809117056153765e+00 ] ]

R: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 9.9994998218891662e-01, 1.8032584564876427e-03, -9.8377527578461001e-03, -1.7978260373390045e-03, 9.9999822653927839e-01, 5.6101678905229378e-04, 9.8387469692470843e-03, -5.4330216016358439e-04, 9.9995145076190473e-01 ] ]

T: !!opencv-matrix rows: 3 cols: 1 dt: d data: [ -1.6952669833501108e+00, -8.3183862355057793e-04, 1.6553031717676806e-02 ] ]

E: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 2.1575221682576832e-05, -1.6552550421804146e-02, -8.4108476712251815e-04, 3.3231506665764049e-02, -8.9119281968270958e-04, 1.6950218347962700e+00, 3.8795921397114311e-03, -1.6952624768406708e+00, -9.5925666229836515e-04 ] ]

F: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 6.5486525518936608e-08, -5.0017984753281888e-05, 9.9960823121244876e-03, 1.0041793870914565e-04, -2.6810044647830857e-06, 3.4036589992213000e+00, -1.5652165011118380e-02, -3.4182603874751876e+00, 9.9999999999999989e-01 ] ]

R1: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 9.9980525157248468e-01, 2.2991356175347437e-03, -1.9600329167972070e-02, -2.2937864080306968e-03, 9.9999732564029009e-01, 2.9539157402834107e-04, 1.9600955894930411e-02, -2.5037507834536546e-04, 9.9980785145963180e-01 ] ]

R2: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 9.9995221263036305e-01, 4.9065951283155365e-04, -9.7637958235422401e-03, -4.9332403320461707e-04, 9.9999984173287226e-01, -2.7049145825487527e-04, 9.7636615590471869e-03, 2.7529524731464261e-04, 9.9995229642492811e-01 ] ]

P1: !!opencv-matrix rows: 3 cols: 4 dt: d data: [ 6.0327398477959753e+02, 0., 3.0294886016845703e+02, 0., 0., 6.0327398477959753e+02, 2.3173668479919434e+02, 0., 0., 0., 1., 0. ] ]

P2: !!opencv-matrix rows: 3 cols: 4 dt: d data: [ 6.0327398477959753e+02, 0., 3.0294886016845703e+02, -1.0227593432896966e+03, 0., 6.0327398477959753e+02, 2.3173668479919434e+02, 0., 0., 0., 1., 0. ] ]

Q: !!opencv-matrix rows: 4 cols: 4 dt: d data: [ 1., 0., 0., -3.0294886016845703e+02, 0., 1., 0., -2.3173668479919434e+02, 0., 0., 0., 6.0327398477959753e+02, 0., 0., 5.8984939980032047e-01, 0. ]

click to hide/show revision 4
No.4 Revision

updated 2013-12-05 07:07:46 -0500

berak gravatar image

Stereo calibration baseline mm

Hi, I calibrated my stereo webcam with a chessboard and:

stereoCalibrate(object_points, imagePoints1, imagePoints2, CM1, D1, CM2, D2, img1.size(), R, T, E, F, cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5), CV_CALIB_SAME_FOCAL_LENGTH | CV_CALIB_ZERO_TANGENT_DIST);

I need to know the baseline of the stereo camera in meters, so I look inside the T matrix. The first element of the T matrix is -1.6952669833501108e+00, the problem is that when I measure physically the distance between sensors with a ruler is approximately 4 cm, that is 0.04 m, what is the metrics of the elements in the T matrix? Therefore, I don't know the physical size of the pixels, because I don't know the sensor used.

%YAML:1.0
CM1: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 6.7035296112450442e+02, 0., 3.0366775716427702e+02, 0.,
       6.7334702239218382e+02, 2.3446212435423504e+02, 0., 0., 1. ]

] CM2: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 6.7035296112450442e+02, 0., 3.0235229497623436e+02, 0., 6.7334702239218382e+02, 2.3428252297251004e+02, 0., 0., 1. ]

] D1: !!opencv-matrix rows: 1 cols: 5 dt: d data: [ -2.7243204402554655e-01, 6.7461566940160000e-01, 0., 0., -1.1338340048806972e+00 ]

] D2: !!opencv-matrix rows: 1 cols: 5 dt: d data: [ -2.9489669516506523e-01, 8.7704813783022018e-01, 0., 0., -1.7809117056153765e+00 ]

] R: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 9.9994998218891662e-01, 1.8032584564876427e-03, -9.8377527578461001e-03, -1.7978260373390045e-03, 9.9999822653927839e-01, 5.6101678905229378e-04, 9.8387469692470843e-03, -5.4330216016358439e-04, 9.9995145076190473e-01 ]

] T: !!opencv-matrix rows: 3 cols: 1 dt: d data: [ -1.6952669833501108e+00, -8.3183862355057793e-04, 1.6553031717676806e-02 ]

] E: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 2.1575221682576832e-05, -1.6552550421804146e-02, -8.4108476712251815e-04, 3.3231506665764049e-02, -8.9119281968270958e-04, 1.6950218347962700e+00, 3.8795921397114311e-03, -1.6952624768406708e+00, -9.5925666229836515e-04 ]

] F: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 6.5486525518936608e-08, -5.0017984753281888e-05, 9.9960823121244876e-03, 1.0041793870914565e-04, -2.6810044647830857e-06, 3.4036589992213000e+00, -1.5652165011118380e-02, -3.4182603874751876e+00, 9.9999999999999989e-01 ]

] R1: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 9.9980525157248468e-01, 2.2991356175347437e-03, -1.9600329167972070e-02, -2.2937864080306968e-03, 9.9999732564029009e-01, 2.9539157402834107e-04, 1.9600955894930411e-02, -2.5037507834536546e-04, 9.9980785145963180e-01 ]

] R2: !!opencv-matrix rows: 3 cols: 3 dt: d data: [ 9.9995221263036305e-01, 4.9065951283155365e-04, -9.7637958235422401e-03, -4.9332403320461707e-04, 9.9999984173287226e-01, -2.7049145825487527e-04, 9.7636615590471869e-03, 2.7529524731464261e-04, 9.9995229642492811e-01 ]

] P1: !!opencv-matrix rows: 3 cols: 4 dt: d data: [ 6.0327398477959753e+02, 0., 3.0294886016845703e+02, 0., 0., 6.0327398477959753e+02, 2.3173668479919434e+02, 0., 0., 0., 1., 0. ]

] P2: !!opencv-matrix rows: 3 cols: 4 dt: d data: [ 6.0327398477959753e+02, 0., 3.0294886016845703e+02, -1.0227593432896966e+03, 0., 6.0327398477959753e+02, 2.3173668479919434e+02, 0., 0., 0., 1., 0. ]

] Q: !!opencv-matrix rows: 4 cols: 4 dt: d data: [ 1., 0., 0., -3.0294886016845703e+02, 0., 1., 0., -2.3173668479919434e+02, 0., 0., 0., 6.0327398477959753e+02, 0., 0., 5.8984939980032047e-01, 0. ]

]

Stereo calibration baseline mmin meters

Hi, I calibrated my stereo webcam with a chessboard and:

stereoCalibrate(object_points, imagePoints1, imagePoints2, CM1, D1, CM2, D2, img1.size(), R, T, E, F, cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5), CV_CALIB_SAME_FOCAL_LENGTH | CV_CALIB_ZERO_TANGENT_DIST);

I need to know the baseline of the stereo camera in meters, so I look inside the T matrix. The first element of the T matrix is -1.6952669833501108e+00, the problem is that when I measure physically the distance between sensors with a ruler is approximately 4 cm, that is 0.04 m, what is the metrics of the elements in the T matrix? Therefore, I don't know the physical size of the pixels, because I don't know the sensor used.

%YAML:1.0
CM1: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 6.7035296112450442e+02, 0., 3.0366775716427702e+02, 0.,
       6.7334702239218382e+02, 2.3446212435423504e+02, 0., 0., 1. ]
CM2: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 6.7035296112450442e+02, 0., 3.0235229497623436e+02, 0.,
       6.7334702239218382e+02, 2.3428252297251004e+02, 0., 0., 1. ]
D1: !!opencv-matrix
   rows: 1
   cols: 5
   dt: d
   data: [ -2.7243204402554655e-01, 6.7461566940160000e-01, 0., 0.,
       -1.1338340048806972e+00 ]
D2: !!opencv-matrix
   rows: 1
   cols: 5
   dt: d
   data: [ -2.9489669516506523e-01, 8.7704813783022018e-01, 0., 0.,
       -1.7809117056153765e+00 ]
R: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 9.9994998218891662e-01, 1.8032584564876427e-03,
       -9.8377527578461001e-03, -1.7978260373390045e-03,
       9.9999822653927839e-01, 5.6101678905229378e-04,
       9.8387469692470843e-03, -5.4330216016358439e-04,
       9.9995145076190473e-01 ]
T: !!opencv-matrix
   rows: 3
   cols: 1
   dt: d
   data: [ -1.6952669833501108e+00, -8.3183862355057793e-04,
       1.6553031717676806e-02 ]
E: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 2.1575221682576832e-05, -1.6552550421804146e-02,
       -8.4108476712251815e-04, 3.3231506665764049e-02,
       -8.9119281968270958e-04, 1.6950218347962700e+00,
       3.8795921397114311e-03, -1.6952624768406708e+00,
       -9.5925666229836515e-04 ]
F: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 6.5486525518936608e-08, -5.0017984753281888e-05,
       9.9960823121244876e-03, 1.0041793870914565e-04,
       -2.6810044647830857e-06, 3.4036589992213000e+00,
       -1.5652165011118380e-02, -3.4182603874751876e+00,
       9.9999999999999989e-01 ]
R1: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 9.9980525157248468e-01, 2.2991356175347437e-03,
       -1.9600329167972070e-02, -2.2937864080306968e-03,
       9.9999732564029009e-01, 2.9539157402834107e-04,
       1.9600955894930411e-02, -2.5037507834536546e-04,
       9.9980785145963180e-01 ]
R2: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 9.9995221263036305e-01, 4.9065951283155365e-04,
       -9.7637958235422401e-03, -4.9332403320461707e-04,
       9.9999984173287226e-01, -2.7049145825487527e-04,
       9.7636615590471869e-03, 2.7529524731464261e-04,
       9.9995229642492811e-01 ]
P1: !!opencv-matrix
   rows: 3
   cols: 4
   dt: d
   data: [ 6.0327398477959753e+02, 0., 3.0294886016845703e+02, 0., 0.,
       6.0327398477959753e+02, 2.3173668479919434e+02, 0., 0., 0., 1.,
       0. ]
P2: !!opencv-matrix
   rows: 3
   cols: 4
   dt: d
   data: [ 6.0327398477959753e+02, 0., 3.0294886016845703e+02,
       -1.0227593432896966e+03, 0., 6.0327398477959753e+02,
       2.3173668479919434e+02, 0., 0., 0., 1., 0. ]
Q: !!opencv-matrix
   rows: 4
   cols: 4
   dt: d
   data: [ 1., 0., 0., -3.0294886016845703e+02, 0., 1., 0.,
       -2.3173668479919434e+02, 0., 0., 0., 6.0327398477959753e+02, 0.,
       0., 5.8984939980032047e-01, 0. ]

Stereo calibration baseline in meters

Hi, I calibrated my stereo webcam with a chessboard and:

stereoCalibrate(object_points, imagePoints1, imagePoints2, CM1, D1, CM2, D2, img1.size(), R, T, E, F, cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5), CV_CALIB_SAME_FOCAL_LENGTH | CV_CALIB_ZERO_TANGENT_DIST);

I need to know the baseline of the stereo camera in meters, so I look inside the T matrix. The first element of the T matrix is -1.6952669833501108e+00, the problem is that when I measure physically the distance between sensors with a ruler is approximately 4 cm, that is 0.04 m, what is the metrics of the elements in the T matrix? Therefore, I don't know the physical size of the pixels, because I don't know the sensor used.

%YAML:1.0
CM1: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 6.7035296112450442e+02, 0., 3.0366775716427702e+02, 0.,
       6.7334702239218382e+02, 2.3446212435423504e+02, 0., 0., 1. ]
CM2: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 6.7035296112450442e+02, 0., 3.0235229497623436e+02, 0.,
       6.7334702239218382e+02, 2.3428252297251004e+02, 0., 0., 1. ]
D1: !!opencv-matrix
   rows: 1
   cols: 5
   dt: d
   data: [ -2.7243204402554655e-01, 6.7461566940160000e-01, 0., 0.,
       -1.1338340048806972e+00 ]
D2: !!opencv-matrix
   rows: 1
   cols: 5
   dt: d
   data: [ -2.9489669516506523e-01, 8.7704813783022018e-01, 0., 0.,
       -1.7809117056153765e+00 ]
R: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 9.9994998218891662e-01, 1.8032584564876427e-03,
       -9.8377527578461001e-03, -1.7978260373390045e-03,
       9.9999822653927839e-01, 5.6101678905229378e-04,
       9.8387469692470843e-03, -5.4330216016358439e-04,
       9.9995145076190473e-01 ]
T: !!opencv-matrix
   rows: 3
   cols: 1
   dt: d
   data: [ -1.6952669833501108e+00, -8.3183862355057793e-04,
       1.6553031717676806e-02 ]
E: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 2.1575221682576832e-05, -1.6552550421804146e-02,
       -8.4108476712251815e-04, 3.3231506665764049e-02,
       -8.9119281968270958e-04, 1.6950218347962700e+00,
       3.8795921397114311e-03, -1.6952624768406708e+00,
       -9.5925666229836515e-04 ]
F: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 6.5486525518936608e-08, -5.0017984753281888e-05,
       9.9960823121244876e-03, 1.0041793870914565e-04,
       -2.6810044647830857e-06, 3.4036589992213000e+00,
       -1.5652165011118380e-02, -3.4182603874751876e+00,
       9.9999999999999989e-01 ]
R1: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 9.9980525157248468e-01, 2.2991356175347437e-03,
       -1.9600329167972070e-02, -2.2937864080306968e-03,
       9.9999732564029009e-01, 2.9539157402834107e-04,
       1.9600955894930411e-02, -2.5037507834536546e-04,
       9.9980785145963180e-01 ]
R2: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 9.9995221263036305e-01, 4.9065951283155365e-04,
       -9.7637958235422401e-03, -4.9332403320461707e-04,
       9.9999984173287226e-01, -2.7049145825487527e-04,
       9.7636615590471869e-03, 2.7529524731464261e-04,
       9.9995229642492811e-01 ]
P1: !!opencv-matrix
   rows: 3
   cols: 4
   dt: d
   data: [ 6.0327398477959753e+02, 0., 3.0294886016845703e+02, 0., 0.,
       6.0327398477959753e+02, 2.3173668479919434e+02, 0., 0., 0., 1.,
       0. ]
P2: !!opencv-matrix
   rows: 3
   cols: 4
   dt: d
   data: [ 6.0327398477959753e+02, 0., 3.0294886016845703e+02,
       -1.0227593432896966e+03, 0., 6.0327398477959753e+02,
       2.3173668479919434e+02, 0., 0., 0., 1., 0. ]
Q: !!opencv-matrix
   rows: 4
   cols: 4
   dt: d
   data: [ 1., 0., 0., -3.0294886016845703e+02, 0., 1., 0.,
       -2.3173668479919434e+02, 0., 0., 0., 6.0327398477959753e+02, 0.,
       0., 5.8984939980032047e-01, 0. ]

I tried to calibrate another stereo rig with baseline of about 8.5 cm:

image description

in this case the baseline computed is -3.5152250398995970e+00:

T: !!opencv-matrix rows: 3 cols: 1 dt: d data: [ -3.5152250398995970e+00, -3.3355285984050492e-02, -1.1137642414481164e-01 ]

Stereo calibration baseline in meters

Hi, I calibrated my stereo webcam with a chessboard and:

stereoCalibrate(object_points, imagePoints1, imagePoints2, CM1, D1, CM2, D2, img1.size(), R, T, E, F, cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5), CV_CALIB_SAME_FOCAL_LENGTH | CV_CALIB_ZERO_TANGENT_DIST);

I need to know the baseline of the stereo camera in meters, so I look inside the T matrix. The first element of the T matrix is -1.6952669833501108e+00, the problem is that when I measure physically the distance between sensors with a ruler is approximately 4 cm, that is 0.04 m, what is the metrics of the elements in the T matrix? Therefore, I don't know the physical size of the pixels, because I don't know the sensor used.

%YAML:1.0
CM1: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 6.7035296112450442e+02, 0., 3.0366775716427702e+02, 0.,
       6.7334702239218382e+02, 2.3446212435423504e+02, 0., 0., 1. ]
CM2: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 6.7035296112450442e+02, 0., 3.0235229497623436e+02, 0.,
       6.7334702239218382e+02, 2.3428252297251004e+02, 0., 0., 1. ]
D1: !!opencv-matrix
   rows: 1
   cols: 5
   dt: d
   data: [ -2.7243204402554655e-01, 6.7461566940160000e-01, 0., 0.,
       -1.1338340048806972e+00 ]
D2: !!opencv-matrix
   rows: 1
   cols: 5
   dt: d
   data: [ -2.9489669516506523e-01, 8.7704813783022018e-01, 0., 0.,
       -1.7809117056153765e+00 ]
R: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 9.9994998218891662e-01, 1.8032584564876427e-03,
       -9.8377527578461001e-03, -1.7978260373390045e-03,
       9.9999822653927839e-01, 5.6101678905229378e-04,
       9.8387469692470843e-03, -5.4330216016358439e-04,
       9.9995145076190473e-01 ]
T: !!opencv-matrix
   rows: 3
   cols: 1
   dt: d
   data: [ -1.6952669833501108e+00, -8.3183862355057793e-04,
       1.6553031717676806e-02 ]
E: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 2.1575221682576832e-05, -1.6552550421804146e-02,
       -8.4108476712251815e-04, 3.3231506665764049e-02,
       -8.9119281968270958e-04, 1.6950218347962700e+00,
       3.8795921397114311e-03, -1.6952624768406708e+00,
       -9.5925666229836515e-04 ]
F: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 6.5486525518936608e-08, -5.0017984753281888e-05,
       9.9960823121244876e-03, 1.0041793870914565e-04,
       -2.6810044647830857e-06, 3.4036589992213000e+00,
       -1.5652165011118380e-02, -3.4182603874751876e+00,
       9.9999999999999989e-01 ]
R1: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 9.9980525157248468e-01, 2.2991356175347437e-03,
       -1.9600329167972070e-02, -2.2937864080306968e-03,
       9.9999732564029009e-01, 2.9539157402834107e-04,
       1.9600955894930411e-02, -2.5037507834536546e-04,
       9.9980785145963180e-01 ]
R2: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [ 9.9995221263036305e-01, 4.9065951283155365e-04,
       -9.7637958235422401e-03, -4.9332403320461707e-04,
       9.9999984173287226e-01, -2.7049145825487527e-04,
       9.7636615590471869e-03, 2.7529524731464261e-04,
       9.9995229642492811e-01 ]
P1: !!opencv-matrix
   rows: 3
   cols: 4
   dt: d
   data: [ 6.0327398477959753e+02, 0., 3.0294886016845703e+02, 0., 0.,
       6.0327398477959753e+02, 2.3173668479919434e+02, 0., 0., 0., 1.,
       0. ]
P2: !!opencv-matrix
   rows: 3
   cols: 4
   dt: d
   data: [ 6.0327398477959753e+02, 0., 3.0294886016845703e+02,
       -1.0227593432896966e+03, 0., 6.0327398477959753e+02,
       2.3173668479919434e+02, 0., 0., 0., 1., 0. ]
Q: !!opencv-matrix
   rows: 4
   cols: 4
   dt: d
   data: [ 1., 0., 0., -3.0294886016845703e+02, 0., 1., 0.,
       -2.3173668479919434e+02, 0., 0., 0., 6.0327398477959753e+02, 0.,
       0., 5.8984939980032047e-01, 0. ]

I tried to calibrate another stereo rig with baseline of about 8.5 cm:

image description

in this case the baseline computed is -3.5152250398995970e+00:

T: !!opencv-matrix rows: 3 cols: 1 dt: d data: [ -3.5152250398995970e+00, -3.3355285984050492e-02, -1.1137642414481164e-01 ]] how is it possible?